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Digital Health Research and Partnerships

November 2, 2023: CDRH is seeking to gather more information about how digital health technologies, included those those enabled by artificial intelligence/machine learning (AI/ML), may help with early detection of risk factors for type 2 diabetes, prediabetes, and type 2 undiagnosed diabetes. Learn more: CDRH Seeks Public Comment .

Both regulatory science research and partnerships play an integral role in the Center for Devices and Radiological Health (CDRH) Digital Health Center of Excellence’s (DHCoE) mission to protect and promote public health by fostering responsible digital health innovation. 

Through digital health-focused collaborations, CDRH is fostering regulatory science research that enables FDA’s experts to understand and assess benefits and risks of new digital health technologies; and this research helps ensure the safety or reduce the harm of products used by patients and consumers by providing scientific, non-biased, and objective expertise.

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Partnerships and Collaborations

Research through our collaborations, areas of interest .

Read the Spotlight (PDF - 7MB)

On October 27, 2022, CDRH’s DHCoE published “Spotlight: Digital Health Regulatory Science Opportunities.” The Spotlight highlights some important research areas identified by stakeholders internal and external to the FDA. In the Spotlight, CDRH’s DHCoE describes these current regulatory science areas of interest in digital health for stakeholders to consider. Advances in digital health occur rapidly and new areas of interest are likely to arise prompting new regulatory science research focus areas throughout the ecosystem. 

Patients, researchers, health care providers, medical product manufacturers, technology companies, standards organizations, and many other stakeholders play integral roles in advancing digital health. Strategic alignment among these stakeholders can help focus efforts to optimize research and possible applications of digital health technologies. CDRH’s DHCoE fosters digital-health focused collaborations that advance public health including the following:

FDA’s Centers of Excellence in Regulatory Science and Innovation (CERSIs) are collaborations between the FDA and academic institutions to advance regulatory science through innovative research, training, and scientific exchanges.

Collaborative Communities are continuing forums in which private- and public-sector members work together on medical device challenges. These groups can invite CDRH to participate. CDRH’s DHCoE participates in the following digital health-related collaborative communities:

• AI Global Healthcare Initiative Collaborative Community (AFDO/RAPS)
• Collaborative Community on Ophthalmic Imaging (CCOI)
• National Evaluation System for health Technology Coordinating Center (NESTcc) Collaborative Community
• Pathology Innovation Collaborative Community (PICC)
• MedTech Color Collaborative Community
• Digital Health Measurement Collaborative Community (DATAcc)

CDRH’s Network of Digital Health Experts  is a pool of vetted experts available to share knowledge and experience regarding digital health issues with FDA staff on an as-needed basis. 

Other collaborations include CDRH’s DHCoE partnership with the Medical Device Innovation Consortium (MDIC)'s Digital Health Initiative  and federal observer to Coalition for Health AI (CHAI) .

Through our partnerships and collaborations, CDRH’s DHCoE is fostering regulatory science research projects in several areas important to digital health. This research includes the following topic areas:

Artificial Intelligence/Machine Learning (AI/ML)

• Assessing the Robustness of Clinical Machine Learning Models to Changes in Context of Use
• Data Science Methods for Post-marketing Surveillance of AI Diagnostic Tools and Algorithm-Based Therapeutics
• Benefit-Risk Preferences for the Use of Artificial Intelligence and Machine Learning in Imaging Diagnostics
• AI in Healthcare: Understanding Patient Information Needs and Designing Comprehensible Transparency
• Patient and Provider Informed Labeling of AI/ML-Based Software to Enable Transparency and Trust for Cardiac Monitoring and Diagnostics
• A Quality Assessment Tool for Artificial Intelligence-Centered Diagnostic Test Accuracy Studies: QUADAS-AI

Digital Biomarkers

• Digital Biomarkers: Convergence of digital health technologies and biomarkers

Digital Health Technologies

• Quantifying Physical Function in Cancer Patients Undergoing Chemotherapy Using Clinician-Reported, Patient-Reported, and Wearable Device Data Sources
• Post-Market Evaluation of Smartwatch Cardiovascular Notifications
• Integrating Sensor Generated Data into Data Platforms to Power Clinical Research and Patient Care
• Development of Digital Measures
• Deployment of Digital Measures

Real World Data and Performance

• The Digital Variome: Understanding the Implications of Digital Tools on Health
• Aggregating Multiple Real-World Data Sources Using a Patient-Centered Health-Data-Sharing Platform

Augmented Reality / Virtual Reality

• Usability Testing of Virtual Reality for Opioid-Sparing Pain Management Among Diverse Patients

Interested in Collaborating with CDRH’s Digital Health Center of Excellence?

There are several ways to collaborate with the DHCoE, including those partnerships listed in the Partnerships and Collaborations section above. 

Please visit the DHCoE webpage Ask a Question About Digital Health Regulatory Policies for information on how to collaborate with the DHCoE or to submit questions related to current digital health policies.

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• Review Article
• Topical Collection - Digital Hypertension
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• Published: 31 May 2023

Digital health, digital medicine, and digital therapeutics in cardiology: current evidence and future perspective in Japan

• Akihiro Nomura 1 , 2 , 3 , 4  

Hypertension Research volume  46 ,  pages 2126–2134 ( 2023 ) Cite this article

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Ten years passed since Japan set out the Action Plan of Growth Strategy that declared the initiatives of digitalization for medicine, nursing care, and healthcare to achieve the world’s most advanced medical care. The initiatives formed the foundation of the Japanese national strategy and have been continuously refined, resulting in the current environment of digital health and digital medicine. Digital health–related terminologies are organized, such as “digital health,” “digital medicine,” and “digital therapeutics” (DTx), as well as several common digital technologies, including artificial intelligence, machine learning, and mobile health (mHealth). DTx is included in mHealth and is a novel disease treatment option. Also, this article thoroughly describes DTx in Japan and compares it with those in the US and Germany, the leading countries in digital health–related policies, regulations, and their development status. In Japan, two of three DTx applications that have been approved and reimbursed by the Ministry of Health, Labor, and Welfare are explained in detail in relation to cardiovascular medicine. When added to a standard smoking cessation program, the DTx system for nicotine dependence significantly improved the continuous abstinence rate. Moreover, the DTx for hypertension together with the guideline-based hypertension management was effective in patients aged 65 years or younger who were diagnosed with essential hypertension without antihypertensive agents, and it was also found to be cost-effective. DTx in cardiovascular medicine, with consideration on safety, efficacy, and cost-effectiveness, could be widely used not only through basic experiments and clinical studies but also through social implementation.

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Introduction

Almost 10 years ago, Japan set out the Action Plan of Growth Strategy that declared the initiatives of digitalization for medicine, nursing care, and healthcare to achieve the world’s most advanced medical care. This action promoted a major push for digital health and digital medicine in Japan. Specific plans included the following: (1) construction of a digital infrastructure for medicine, nursing care, and healthcare; (2) utilization of the digital infrastructure; (3) advanced digitalization of on-site operations; and (4) system establishment for utilizing medical and personal information. These initiatives formed the foundation of the Japanese national strategy and have been continuously refined, resulting in the current environment of digital health and digital medicine [ 1 ].

In this article, we define digital health-related terminologies. First, “digital health” is a comprehensive concept of the utilization of information and communication technology (ICT) for all medical, nursing care, or healthcare support. ICT includes digital technologies such as medical big data of genomic and electronic health information, artificial intelligence (AI), or extended reality (XR) [ 2 ]. This term often implies the use of the latest and state-of-the-art digital technology to solve various problems in healthcare fields as the objective. Digital technology seems well suited in the following fields: patient treatment; health promotion including primary prevention; the conduct and support of clinical research including decentralized clinical trials; medical education; observation and evaluation of the patients’ clinical course; and public health monitoring for the general population or specific disease cohorts. Moreover, “digital medicine” refers to digital health related to medical care and broadly supporting medicine practice.” [ 3 ] In digital medicine, the use of digital technology for disease treatment is referred to as “digital therapeutics (DTx)” (Fig.  1 ) [ 4 ].

Correlation diagram among digital health-related terminologies

For years, various studies and clinical applications other than digital technologies have been conducted to solve healthcare issues. However, digital technology has become one of the powerful tools to solve healthcare-related problems in any medical field and has strongly assisted the evolution of digital health, along with the recent leap in ICT development, miniaturization and technical advantages of mobile devices, easy access to vast and organized data and ample computational resources, and establishment of 5th (5 G) or higher-generation mobile communication systems that allow for high-capacity, low-latency, and multiple connections. Typical digital technologies include online medical services, AI and machine learning (AI/ML), Web 3.0 (web3) and blockchain technology, XR including metaverse, electronic health records (EHR) and personal health records (PHR), and mobile health (mHealth).

Telehealth and telemedicine

As for online medical service, the Ministry of Health, Labour, and Welfare (MHLW) in Japan has provided “the guidance for the appropriate implementation of telemedicine.” [ 5 ] In this guidance, telehealth refers to health promotion and medically related activities using ICT equipment [ 5 ]. It includes not only telemedicine but also online medical advice, remote healthcare communication, and real-time online consultation between physicians, similar in concept to digital health. In particular, telemedicine is strictly defined as real-time examination, diagnosis, explanation of laboratory results, and treatment between the physician and the patient, using remote communication tools installed on mobile devices or computers [ 5 ]. Of note, regardless of whether it is an insurance-covered medical treatment or not, telemedicine based on this guidance is required.

Owing to the recent coronavirus disease 2019 (COVID-19) pandemic, telemedicine has rapidly and mandatorily gained recognition in Japan. After the state of emergency was declared in April 2020, the MHLW has permitted the use of telemedicine from the first online consultation [ 6 , 7 ]. Since then, the proportion of hospitals or clinics that could provide telemedicine increased to 15.2% in April 2021 [ 8 ]. The revision for medical service fee [ 9 ] in 2022 that raised several telemedicine fees (but still less than the face-to-face outpatient fees) may help accelerate the widespread use of telemedicine [ 10 ]. However, the number of conducting telemedicine in Japan remains considerably lower than that in European and North American countries [ 11 ]. Thus, the advantages and challenges of telemedicine should be reconsidered to further promote its use.

AI has no single definition. The Japanese Society of Artificial Intelligence defined AI as something aimed to perform advanced inference accurately on a large amount of knowledge data [ 12 ]. However, the concept of AI is highly diversified and still under discussion. Thus, when using this term, we need to pay attention to what kind of specific AI technology is referred to [ 13 ] Currently, rule-based and ML are frequently used AI technology in medical sciences. In addition, the development of computer resources and easy access to medical big data enable us to utilize ML and its subfield, that is, deep learning, for clinical applications.

One of the primary approaches for medical AI implementation today would be to leverage ML, including deep learning or reinforcement learning, as a tool to obtain the target output [ 13 ]. In other words, medical AI aims to maximize the performance of the output as “prediction,” “classification,” or “generation” of diseases or data that are currently required in medicine, and numerous efforts are being made to implement it in society. Recently, a large language model of Generative Pre-trained Transformer 3 (GPT-3) with supervised fine-tuning and reinforcement learning from human feedback as InstructGPT [ 14 ] and its dialogue-optimized conversation web-console (ChatGPT) [ 15 ] attracts huge attention worldwide. Surprisingly, the ChatGPT has already been scored at or near the passing threshold on the United States Medical Licensing Exam [ 16 ]. The products applying these natural language processing models have the potential to rapidly penetrate in every aspect of medical fields soon.

Web3.0/Metaverse/Blockchain

Web3.0 is a next-generation Internet environment utilizing blockchain technology, and the metaverse supports part of this environment. Blockchain technology is a type of database that processes and records transactions using cryptography, directly connecting terminals on information communication networks [ 17 ]. It has excellent tamper resistance for efficient monitoring and data management in clinical trials [ 18 ]. The term “metaverse” refers to a virtual space where anyone can communicate similar to the real world and engage in economic activities involving money as both fiat currency and cryptocurrency [ 19 ]. Metaverse uses cross reality (XR), including virtual reality (VR) as its utilization technology, and XR is becoming noteworthy in the medical field.

VR is a technology that creates a virtual environment through a computer, stimulating the human senses and making such an environment perceived as “reality.” [ 20 ] Currently, VR controls the visual and auditory senses, and it was often defined as an environment where the external “real” world is completely shut off by a full immersive head-mounted display (HMD). Similar concepts include Augmented Reality (AR) and Mixed Reality (MR), which mainly refer to real-time overlaying (for AR) or merging (for MR) of the environment and objects onto the actual reality we perceived using a see-through HMD or smartphones [ 20 ]. Clearly distinguishing them is difficult; thus, a comprehensive concept of XR (cross reality or extended reality) emerged. In the medical field, XR has already been used for medical equipment-level surgical support system [ 21 ], medical education [ 22 ], and XR-based rehabilitation system [ 23 ].

EHR is a collection of electronic medical records stored in an electronic chart originally intended to use only in each hospital or clinic but made shareable and accessible in a specific region or nationwide [ 24 ]. EHR contains sensitive personal information; thus, it has been managed mainly by medical institutions. Conversely, the PHR refers to securely usable online medical, health, care and well-being information collected and managed by the person who is being described in the record [ 25 ] In PHR, health-related information can be shared and aggregated at the individual level. Thus, even if people visited multiple clinics, PHR can manage not only their medical records but also their lifelong data obtained by wearable devices during daily life.

The term “mHealth” generally refers to digital health using mobile devices. Currently, the most used mobile devices are smartphones and wearable devices. With the advancement of “smart” devices, mobile devices can now measure and estimate not only steps or pulse rates but also electrocardiograms, skin temperature, blood oxygen levels, stress levels, blood pressure, or plasma glucose levels [ 26 ]. The wearable devices can also be linked with smartphones to allow viewing, verifying, and processing of biometric data in detail and sharing of data with healthcare providers as needed.

In mHealth, DTx is attracting attention as a novel third option for disease treatment; it is one of the three core treatment pillars, namely, medical, surgical, and digital therapies. I especially focus on DTx in the following chapters.

What is DTx?

DTx is a novel therapeutic option that provides treatment for illnesses through software applications (apps) delivered via digital devices [ 4 ], and is expanding its scope to disease prevention and management. Currently, smartphones and VR HMDs are prevalently used for this purpose. This concept of DTx was introduced in Japan with the revision of the Act on Securing Quality, Efficacy, and Safety of Products Including Pharmaceuticals and Medical Devices, which demonstrated that software programs (i.e., Software as a Medical Device [SaMD]), including standalone apps themselves, could be certified as medical devices (Fig.  1 ) [ 27 ]. Currently, the software app that provides DTx is called “therapeutic apps.” The term “prescription digital therapeutics” is also used, considering that physicians or healthcare professionals “prescribe” the therapeutic app to the patients, let them install it on their digital device, and provide the intended treatment [ 4 ]. DTx not only aims to treat illnesses but also provides a direct digital intervention to patients that have a scientifically proven treatment effect, and it has been approved by regulatory agencies. In addition, compared with medical care provided in hospitals or clinics, DTx can provide seamless treatment interventions through mobile digital devices even in patients’ daily lives.

DTx in the US and Germany

Presently, the US and Germany are particularly leading the digital health-related policies, regulations, and their development status. In the US, regulation of mobile medical applications (MMAs), including DTx, was first issued by the Food and Drug Administration (FDA) in 2013, and was updated in 2015, 2019, and 2022 [ 28 ]. Similar to traditional medical devices, MMAs that have a significant impact on patients or medical decision-making requires appropriate regulation processes, including clinical trials. However, the conventional medical device approval process does not automatically adopt the rapidly evolving digital technology used in software or MMA development. Therefore, in July 2017, the FDA launched the Digital Health Innovation Action Plan, which includes the Software Pre-Cert Pilot Program, a system used for faster and safer review and approval processes of digital health products, including MMAs [ 29 ]. This innovative system assesses the development capabilities and safety of the development manufacturers rather than each individual medical device software, allowing the companies to bring their FDA-cleared software to market faster and more efficiently. Although the pilot program was completed in September 2022 [ 30 ], the FDA continues to develop policies with the Digital Health Center of Excellence, a digital health resource center, to improve regulatory processes related to medical device software, enabling digital health stakeholders to advance all aspects of healthcare through high-quality digital health innovation [ 31 ].

Germany is releasing more DTx medical device software to the market than the US. Germany installs the same public health insurance system as Japan, and the implemented policies provide important hints on how to generalize DTx and medical device software. In November 2019, Germany launched the Digital Healthcare Act ( Digitale-Versorgung-Gesetz or DVG) [ 32 ], which explains the review and approval process of low-risk medical devices essentially based on digital technologies such as Digital Health Applications ( Digitale Gesundheitsanwendungen or DiGA) [ 33 ]. Same as FDA, the German Federal Institute for Drug and Medical Devices ( Bundesinstitut für Arzneimittel und Medizinprodukte or BFArM) assesses DiGA according to the following requirements: safety, functionality, quality, data protection, data security, and positive effects on care under the DVG. However, the DVG’s striking point is that even the DiGA and its manufacturer satisfy all requirements excluding the “positive effects on care,” the DiGA can still provisionally be registered in the BFArM directory [ 34 ]. Therefore, even if the manufacturer has not submitted the DiGA’s clinical efficacy validation data through regulation processes, such as clinical trials, the DiGA can still be registered and tentatively reimbursed by health insurance as long as the app’s safety, functionality, quality, data protection, and data security are satisfactory. The provisional reimbursement period is limited to 12 months (or can be extended to 24 months in a specific situation) until the clinical efficacy evaluation is confirmed. However, during this period, the manufacturer can conduct the DiGA’s pivotal trials, or real-world data can be collected while distributing the app in the market with health insurance coverage [ 34 ]. As of February 2023, 48 DiGAs have been registered in the BFArM directory. Of these apps, 16 (33%) have reached permanent reimbursement, 5 (approximately 10%) were removed from the list, and 27 (56%) are in the provisional reimbursement period and are closely monitored if they can demonstrate sufficient clinical efficacy to obtain permanent reimbursement [ 35 ].

DTx in Japan

DTx in Japan has been led mainly by several start-ups since 2014 when the Act on Securing Quality, Efficacy, and Safety of Products Including Pharmaceuticals and Medical Devices were revised. Same in the US or Germany, Japan’s medical device software showing a therapeutic effect for diseases needs to be regulated by the MHLW. In 2020, to further promote early implementation of novel SaMD products, including DTx apps in Japan, the MHLW launched the Digital Transformation Action Strategies in Healthcare (DASH) for SaMD [ 36 ]. This strategy included the following: (1) seeking promising technologies; (2) arranging and disclosing the concept of a review process specialized in SaMD; (3) centralizing the SaMD consultation service; (4) establishing a SaMD-compatible rapid, efficient, and flexible review system; and (5) reinforcing the review system for early SaMD implementation. Such regulatory efforts to implement SaMD have improved the related guidance and guidelines [ 37 ], leading to a better environment to develop medical device software in Japan.

As of February 2023, two types of DTx (for nicotine addiction [CureApp SC TM ] and for hypertension [CureApp HT TM ]) have been approved and reimbursed by the MHLW in Japan. Additionally, DTx for insomnia (SUSMED Med CBT-i) has newly been cleared by MHLW [ 38 ]. The following section focuses on the two former DTx apps relating to cardiovascular medicine.

DTx system for nicotine dependence

The CureApp SC TM DTx for nicotine dependence is a therapeutics system that aims to provide intervention and support for psychological dependence to quit smoking in addition to the 12-week standard smoking cessation program in Japan [ 39 ]. This DTx system consists of a smartphone therapeutic app, a Bluetooth-paired mobile checker device for exhaled carbon monoxide (CO), and a web-based personal computer software for physicians [ 40 ]. It provides individually tailored behavioral therapy and quit-smoking guidance content through a therapeutic app, thereby intensifying the treatment for psychological dependence on smoking. Moreover, an equipped mobile CO breath analyzer allows patients to measure their expiratory CO levels daily and view their cessation progress through a smartphone app or web-based software for physicians. A multicenter randomized controlled trial assessed the usefulness of the DTx for nicotine dependence [ 41 ]. A total of 584 patients diagnosed with nicotine dependence were allocated to either of the following groups: intervention group (using the DTx system for nicotine dependence in addition to a standard smoking cessation program) and control group (using a sham app in addition to a standard smoking cessation program). The primary outcome of the continuous abstinence rate from weeks 9 to 24 was significantly higher in the DTx intervention group than in the control group (63.9% vs. 50.5%; odds ratio [OR], 1.73; 95% confidence interval [CI], 1.24–2.42, P  = 0.001), and this DTx add-on effect continued at least up to 52 weeks. Hence, the DTx system for nicotine dependence significantly improved the continuous abstinence rate when added to a standard smoking cessation program. Based on these results, the CureApp SC TM DTx system was approved and reimbursed by the MHLW in Japan in 2020 as the first DTx in Asia.

SaMD DTx app for hypertension

The CureApp HT TM DTx for hypertension is a SaMD therapeutic app that aims to provide continuous treatment for high blood pressure, not only during intermittent clinic visits but also in their daily life. This app was developed to efficiently support and maximize the blood pressure-lowering effect of lifestyle modification [ 42 ], which is recommended for all patients with high blood pressure by the hypertension management guidelines [ 43 , 44 ]. Although many physicians think that hypertension treatment links directly to pharmacological therapy, nonpharmacological therapy has also demonstrated robust blood pressure-lowering effects. Nonpharmacological therapy includes a low-salt diet, weight reduction, regular exercise, moderate alcohol consumption, good sleep, stress management [ 45 , 46 ]. With this background in the algorithm, the DTx app for hypertension aims to educate, practice, and habituate each nonpharmacological therapy for patients with hypertension through the app during daily life outside hospitals or clinics. The app first provides knowledge and techniques to the users for the six non-pharmacological therapy for hypertension (Step 1: input and education). Next, with the app’s support, the users implement specific lifestyle modifications related to the nonpharmacological therapy based on the knowledge and techniques obtained in Step 1 (Step 2: app-supported experiences). Finally, the users independently set, implement, and evaluate their own goals and achievements of lifestyle modification and truly habituate the target nonpharmacological therapy in their daily life (Step 3: self-planning and evaluation) [ 47 ].

The efficacy of DTx for hypertension was tested in the HERB-DH1 pivotal clinical trial [ 48 ]. The trial enrolled 390 patients aged 65 years or younger who had essential hypertension (grade I or II) but were not taking antihypertensive agents; they were then allocated to either of the DTx intervention group (received the DTx app for hypertension and lifestyle modification guidance according to the guidelines) or the control group (only received lifestyle modification education according to the guidelines) [ 47 ]. The primary endpoint of the change in 24-hour systolic blood pressure by ambulatory blood pressure monitoring from baseline (week 0) to week 12 was −4.9 and −2.3 mmHg mmHg in the DTx intervention and control groups, respectively. Hence, the DTx app intervention group had a significantly greater reduction in blood pressure than the control group (mean difference, −2.4 mmHg; 95% CI, −4.5 to −0.3; P  = 0.024). Additionally, the reduction of morning home systolic blood pressure from baseline to week 12 was greater in the DTx intervention group than in the control group (−10.6 mmHg vs. −6.2 mmHg; mean difference, −4.3 mmHg; 95% CI, −6.7 to −1.9; P  < 0.001). Furthermore, these blood pressure reduction effects persisted at week 24 at least. In conclusion, the DTx for hypertension in addition to the guideline-based hypertension management was effective in patients aged 65 years or younger who had essential hypertension without antihypertensive agents.

On top of that, we conducted a cost-effectiveness analysis of the DTx for hypertension by using the background characteristics and effect data of both intervention and control groups in the HERB-DH1 trial [ 49 ]. In this analysis, we examined the medical economic effects of using the therapeutic app of DTx for hypertension with a time horizon. The differences in medical costs and quality-adjusted life years (QALY) between the DTx intervention group and the control group were 110 717 yen (higher in the DTx intervention group) and 0.092 (longer in the app intervention group). Therefore, the incremental cost-effectiveness ratio (ICER) was calculated to be 1 199 880 yen/QALY [ 49 ]. This ICER value was lower than the “willingness-to-pay” threshold of 5 million yen/QALY, which is one of the acceptable medical costs for each increase in 1 QALY. Thus, prescribing the DTx app might be cost-effective through life. Considering these series of evidence, the CureApp HT TM DTx for hypertension was cleared and was reimbursed by the MLHW in Japan as the world’s first hypertension therapeutic app in 2022.

Conclusions and future perspective

This review introduces the latest and various digital health technologies with specific terminologies along with the DTx in cardiovascular medicine. Although only three DTx apps have been approved by MHLW in Japan, several manufacturers, including DTx start-ups and pharmaceutical companies, continuously develop DTx and conduct clinical research to obtain regulatory approval. The number of DTx development pipelines in Japan surpasses more than 30, which continues to increase every year (Table  1 ). The movement to promote DTx in cardiovascular medicine, which applies various digital technologies to patients with cardiovascular diseases and considers the technologies’ safety, efficacy, and cost-effectiveness, will accelerate not only through basic experiments and clinical studies but also through social implementation.

Ministry of Internal Affairs and Communications in Japan. The promotion of ICT utilization (Japanese). http://www.soumu.go.jp/menu_seisaku/ictseisaku/ictriyou/iryou_kaigo_kenkou.html . Accessed Feb 3.

US Food and Drug Administration. What is Digital Health? https://www.fda.gov/medical-devices/digital-health-center-excellence/what-digital-health . Accessed Feb 18.

Digital Medicine Society (DiME). Defining Digital Medicine. https://dimesociety.org/about-us/defining-digital-medicine/ . Accessed Feb 21.

Digital Therapeutics Alliance. Industry overview. https://dtxalliance.org/ . Accessed Feb 5.

Ministry of Health Labour and Welfare in Japan. Guidance for appropriate implementation of telemedicine (Japanese). https://www.mhlw.go.jp/content/000889114.pdf . Accessed Feb 5.

Prime Minister’s Office of Japan. [COVID-19] Declaration of a State of Emergency in response to the Novel Coronavirus Disease (April 7). https://japan.kantei.go.jp/ongoingtopics/_00018.html . Accessed Feb 20.

Noto K, APCO worldwide. The Challenges and Growth of Telehealth in Japan: Is it here to stay? https://apcoworldwide.com/blog/the-challenges-and-growth-of-telehealth-in-japan/ . Accessed Feb 20.

Ministry of Internal Affairs and Communications in Japan. Information and Communications in Japan (WHITE PAPER 2021). Chapter 2. Digitalization Accelerated by the COVID-19 Pandemic. https://www.soumu.go.jp/johotsusintokei/whitepaper/eng/WP2021/chapter-2.pdf#page=4 . Accessed Feb 20.

Japan Health Policy Now (Health and Global Policy Institute). 7.2 The Medical Service Fee System. https://japanhpn.org/en/section-7-2/ . Accessed Feb 20.

Tokyo Medical Association. Summary of a questionnair survey regarding telemedicine (Japanese). https://www.tokyo.med.or.jp/29014 . Accessed Feb 20.

Organisation for Economic Co-operation and Development (OECD). The future of telemedicine after COVID-19. https://www.oecd.org/coronavirus/policy-responses/the-future-of-telemedicine-after-covid-19-d46e9a02/ . Accessed Feb 20.

The Japanese Society of Artificial Intelligence. Articles of incorporation. https://www.ai-gakkai.or.jp/about/about-us/jsai_teikan/ . Accessed Feb 9.

Nomura A, Noguchi M, Kometani M, Furukawa K, Yoneda T. Artificial Intelligence in Current Diabetes Management and Prediction. Curr Diab Rep. 2021;21:61.

Article   PubMed   PubMed Central   Google Scholar  

Ouyang L, Wu J, Jiang X, Almeida D, Wainwright CL, Mishkin P, et al. Training language models to follow instructions with human feedback. 2022. https://doi.org/10.48550/arXiv.2203.02155 .

Open AI. ChatGPT: Optimizing Langeuage Models for Dialogue. https://openai.com/blog/chatgpt/ . Accessed Feb 20.

Kung TH, Cheatham M, Medenilla A, Sillos C, De Leon L, Elepaño C, et al. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digit Health. 2023;2:e0000198.

Ministry of Internal Affairs and Communications in Japan. Information and Communications in Japan (WHITE PAPER 2020) (Japanese). https://www.soumu.go.jp/johotsusintokei/whitepaper/ja/r02/html/nd134620.html .

Motohashi T, Hirano T, Okumura K, Kashiyama M, Ichikawa D, Ueno T. Secure and Scalable mHealth Data Management Using Blockchain Combined With Client Hashchain: System Design and Validation. J Med Internet Res. 2019;21:e13385.

Kubota S, Ishimura N. Metaverse Future Strategy (Japanese). Nikkei BP, 2022.

Sutherland J, Belec J, Sheikh A, Chepelev L, Althobaity W, Chow BJW, et al. Applying Modern Virtual and Augmented Reality Technologies to Medical Images and Models. J Digit Imaging. 2019;32:38–53.

Article   PubMed   Google Scholar  

Ryu S, Kitagawa T, Goto K, Okamoto A, Marukuchi R, Hara K, et al. Intraoperative Holographic Guidance Using Virtual Reality and Mixed Reality Technology During Laparoscopic Colorectal Cancer Surgery. Anticancer Res. 2022;42:4849–56.

Omori K, Shigemoto N, Kitagawa H, Nomura T, Kaiki Y, Miyaji K, et al. Virtual reality as a learning tool for improving infection control procedures. Am J Infect Control. 2023;51:129–34.

Hara M, Kitamura T, Murakawa Y, Shimba K, Yamaguchi S, Tamaki M. Safety and Feasibility of Dual-task Rehabilitation Program for Body Trunk Balance Using Virtual Reality and Three-dimensional Tracking Technologies. Prog Rehabil Med. 2018;3:20180016.

Gunter TD, Terry NP. The emergence of national electronic health record architectures in the United States and Australia: models, costs, and questions. J Med Internet Res. 2005;7:e3.

National Health Service Digital. Personal Health Records definition. https://digital.nhs.uk/services/personal-health-records-adoption-service/personal-health-records-adoption-toolkit/initiating-a-personal-health-record/personal-health-records-definition . Accessed Feb 20.

Huhn S, Axt M, Gunga HC, Maggioni MA, Munga S, Obor D, et al. The Impact of Wearable Technologies in Health Research: Scoping Review. JMIR Mhealth Uhealth. 2022;10:e34384.

Ministry of Health Labour and Welfare in Japan. Outlines of amended Act on Pharmaceutical Affairs (Japanese). https://www.mhlw.go.jp/file/06-Seisakujouhou-11120000-Iyakushokuhinkyoku/0000066816.pdf . Accessed Feb 13.

US Food and Drug Administration. Device Software Functions Including Mobile Medical Applications. https://www.fda.gov/medical-devices/digital-health-center-excellence/device-software-functions-including-mobile-medical-applications . Accessed Feb 15.

US Food and Drug Administration. Digital Health Innovation Action Plan. https://www.fda.gov/media/106331/download . Accessed Feb 18.

US Food and Drug Administration. The Software Precertification (Pre-Cert) Pilot Program: Tailored Total Product Lifecycle Approaches and Key Findings. https://www.fda.gov/media/161815/download . Accessed Feb 18.

US Food and Drug Administration. Digital Health Center of Excellence. https://www.fda.gov/medical-devices/digital-health-center-excellence . Accessed Feb 18.

Federal Ministry of Health in Germany. Driving the digital transformation of Germany’s healthcare system for the good of patients. https://www.bundesgesundheitsministerium.de/en/digital-healthcare-act.html . Accessed Feb 18.

Federal Institute for Drugs and Medical Devices in Germany. DiGA Digital Health Applications. https://www.bfarm.de/EN/Medical-devices/Tasks/DiGA-and-DiPA/Digital-Health-Applications/_node.html . Accessed Feb 19.

Gerke S, Stern AD, Minssen T. Germany’s digital health reforms in the COVID-19 era: lessons and opportunities for other countries. NPJ Digit Med. 2020;3:94.

Federal Institute for Drugs and Medical Devices in Germany. DiGA directory. https://diga.bfarm.de/de/verzeichnis . Accessed Feb 19.

Ministry of Health Labour and Welfare in Japan. DASH for SaMD (Japanese). https://www.mhlw.go.jp/content/11124500/000737470.pdf . Accessed Feb 21.

Ministry of Health Labour and Welfare in Japan. Guideline for medical device software encouraging behavior change in health and care (Draft) (Japanese). https://md-guidelines.pj.aist.go.jp/wp-content/uploads/2022/05/65_guideline_draft.pdf . Accessed Feb 21.

Watanabe Y, Kuroki T, Ichikawa D, Ozone M, Uchimura N, Ueno T Effect of Smartphone-based Cognitive Behavioral Therapy App on Insomnia: a Randomized, Double-blind Study. Sleep. 2022. https://doi.org/10.1093/sleep/zsac270 ).

The Japanese Circulation Society, The Japan Lung Cancer Society, The Japanese Cancer Society, The Japanese Respiratory Society. The Standard Procedure Manual for Smoking Cessation, 8th edition. http://j-circ.or.jp/kinen/anti_smoke_std/pdf/anti_smoke_std_rev8_.pdf . Accessed Feb 13.

Nomura A, Tateno H, Masaki K, Muto T, Suzuki S, Satake K, et al. A Novel Smoking Cessation Smartphone App Integrated With a Mobile Carbon Monoxide Checker for Smoking Cessation Treatment: Protocol for a Randomized Controlled Trial. JMIR Res Protoc. 2019;8:e12252.

Masaki K, Tateno H, Nomura A, Muto T, Suzuki S, Satake K, et al. A randomized controlled trial of a smoking cessation smartphone application with a carbon monoxide checker. NPJ Digit Med. 2020;3:35.

Kario K, Nomura A, Kato A, Harada N, Tanigawa T, So R, et al. Digital therapeutics for essential hypertension using a smartphone application: A randomized, open-label, multicenter pilot study. J Clin Hypertens. 2021;23:923–34.

Article   Google Scholar  

Umemura S, Arima H, Arima S, Asayama K, Dohi Y, Hirooka Y, et al. The Japanese Society of Hypertension Guidelines for the Management of Hypertension (JSH 2019). Hypertens Res. 2019;42:1235–481.

Unger T, Borghi C, Charchar F, Khan NA, Poulter NR, Prabhakaran D, et al. 2020 International Society of Hypertension Global Hypertension Practice Guidelines. Hypertension. 2020;75:1334–57.

Article   CAS   PubMed   Google Scholar  

Mahmood S, Shah KU, Khan TM, Nawaz S, Rashid H, Baqar SWA, et al. Non-pharmacological management of hypertension: in the light of current research. Ir J Med Sci. 2019;188:437–52.

Fu J, Liu Y, Zhang L, Zhou L, Li D, Quan H, et al. Nonpharmacologic Interventions for Reducing Blood Pressure in Adults With Prehypertension to Established Hypertension. J Am Heart Assoc. 2020;9:e016804.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Kario K, Nomura A, Harada N, Tanigawa T, So R, Nakagawa K, et al. A multicenter clinical trial to assess the efficacy of the digital therapeutics for essential hypertension: Rationale and design of the HERB-DH1 trial. J Clin Hypertens. 2020;22:1713–22.

Kario K, Nomura A, Harada N, Okura A, Nakagawa K, Tanigawa T, et al. Efficacy of a digital therapeutics system in the management of essential hypertension: the HERB-DH1 pivotal trial. Eur Heart J. 2021;42:4111–22.

Nomura A, Tanigawa T, Kario K, Igarashi A. Cost-effectiveness of digital therapeutics for essential hypertension. Hypertens Res. 2022;45:1538–48.

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Akihiro Nomura

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Frontier Institute of Tourism Sciences, Kanazawa University, Kanazawa, Japan

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Nomura, A. Digital health, digital medicine, and digital therapeutics in cardiology: current evidence and future perspective in Japan. Hypertens Res 46 , 2126–2134 (2023). https://doi.org/10.1038/s41440-023-01317-8

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Digital health interventions refer to the use of digital technology and connected devices to improve health outcomes and healthcare delivery. This includes telemedicine, electronic health records, wearable devices, mobile health applications, and other forms of digital health technology. To this end, several research and developmental activities in various fields are gaining momentum. For instance, in the medical devices sector, several smart biomedical materials and medical devices that are digitally enabled are rapidly being developed and introduced into clinical settings. In the pharma and allied sectors, digital health-focused technologies are widely being used through various stages of drug development, viz. computer-aided drug design, computational modeling for predictive toxicology, and big data analytics for clinical trial management. In the biotechnology and bioengineering fields, investigations are rapidly growing focus on digital health, such as omics biology, synthetic biology, systems biology, big data and personalized medicine. Though digital health-focused innovations are expanding the horizons of health in diverse ways, here the development in the fields of medical devices, pharmaceutical technologies and biotech sectors, with emphasis on trends, opportunities and challenges are reviewed. A perspective on the use of digital health in the Indian context is also included.

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1 Introduction

Digital health is a rapidly growing field that offers exciting opportunities for innovation and improvement in healthcare delivery. The goal of digital health is to make healthcare more efficient, accessible, and effective, by leveraging the power of digital technology to collect, analyze, store and share health data. Electronic Health Records (EHRs), telemedicine, mobile health apps, wearable devices, the internet of medical things and cutting-edge digital technology constitute digital health. The digital health market has been growing rapidly in recent years and is expected to continue its growth trajectory in the near future. The global digital health market size was valued at approximately US$211 billion in 2022 and is expected to grow at a compound annual growth rate (CAGR) of 18.6% from 2023 to 2030 [ 1 ]. In the Indian context, the digital health market is reported to be about US$12.2 billion in 2023 and is projected to reach US$25.64 billion by 2027 with a CAGR of about 20.4% [ 2 ]. The digital health market is highly fragmented and is characterized by a large number of small and medium-sized enterprises operating in various segments, such as wearable devices, telemedicine, EHRs, and mobile health apps. Major players in the digital health market include Apple, Google, Philips, Medtronic, and Roche, among others [ 3 ].

The growth of the digital health market can be attributed to several factors, including the increasing adoption of smartphones and other digital devices, the growing demand for remote monitoring and telemedicine services, and the increasing focus on the development of digital health solutions to address the challenges posed by the COVID-19 pandemic. Several groups are working on various aspects of digital health, and the number of scientific publications in this area has been growing rapidly in recent years (Fig.  1 ).

An overview of the publication trends in digital health as available from the web of science ( a ), and details of sectors where much of the research work is being focused ( b )

The medical device sector has seen significant innovations in digital health in recent years. These range from wearable devices, remote monitoring systems, telemedicine devices, and electronic drug dispensing units to smart inhalers. These innovations in the medical device sector have the potential to greatly improve healthcare delivery and patient outcomes by providing more efficient and effective ways to monitor and manage health. Similarly, in the pharma sector, Digital Health Technologies (DHTs) are being used in many ways. Several DHTs are being used to speed up the drug development process through (i) drug design by virtual screening tools, (ii) reducing animal usage by predictive toxicology, and (iii) streamlining clinical trials by digital data management. In the biotechnology and bioengineering front, developments in the field of omics, synthetic and systems biology, big data and precision medicine are leaning towards digital health. This review gives an overview of the trends, opportunities and challenges for digital health innovations in the medical device, pharma and bio-allied fields. Although there are pervious review articles on digital health in general, the current review was the first of its kind covering digital health technologies across the key segments in the health sector i.e. medical devices, pharma and biotechnology.

2 Developments in medical devices and allied technologies toward digital health

2.1 medical devices.

Digital health-focused medical devices are devices that utilize digital technologies to improve health and healthcare. These devices are playing an increasingly important role in improving healthcare delivery by enabling remote patient monitoring, increasing access to medical services, and reducing healthcare costs. They also offer the potential for improved patient outcomes by enabling early detection and intervention in medical conditions. However, there are challenges associated with the use of these devices, such as the need for appropriate regulatory oversight, privacy concerns, and attention to cybersecurity risks. Examples of digital health-focused medical devices are described below.

2.1.1 Wearable devices

Wearable technology has been an active area of research in recent years, with numerous advances. These devices such as smartwatches and fitness trackers monitor various aspects of a person's health, such as heart rate, sleep patterns, and physical activity [ 4 ]. Some wearable devices also have features such as ECG monitoring and fall detection. The latest wearable medical devices global market report underlines that the market would grow from US$22.44 billion (2022) to US$27.37 billion by the end of 2023, with a predicted annual growth rate of 21.9%. It further suggests that the trend will continue with the same CAGR to reach $60.48 billion in 2027. Following are some of the latest trends and developments in this area:

(a) Augmented reality (AR) integrated wearable devices It is an area that has seen significant growth in recent years [ 5 , 6 ]. Researchers are exploring ways to integrate AR technology into wearable devices to create an enhanced user experience. This could include things like displaying information directly on a user's field of vision or providing additional context to the real world.

(b) Artificial Intelligence (AI) integrated wearable devices AI is also being integrated into wearable devices to provide users with more advanced features and capabilities [ 7 ]. For example, wearable devices could use AI to analyze data from various sensors and make predictions about a user's health or provide personalized recommendations.

(c) Energy harvesting wearable devices This is another area of research in wearable technology [ 8 ]. Researchers are developing devices that can generate their power through movement, body heat, or other sources, which would make them more self-sufficient and reduce the need for charging.

A schematic representation of wearable, minimally invasive/implantable devices integrated with DHTs is presented in Fig.  2 .

A schematic illustration of an array of wearable, minimally invasive/implantable devices integrated with DHTs. Reproduced with permission from [ 9 ]. © 2016 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim

2.1.2 Diagnostic devices

These are devices used for diagnostic purposes, such as glucose meters for diabetic patients, spirometry devices for pulmonary function testing, and portable ultrasound machines. Following are some of the latest trends and developments in this area:

(a) Point-of-care (POC) diagnostics POC testing devices are becoming increasingly popular as they allow for rapid diagnostic testing at the point of care. These devices are designed to be small, portable, and easy to use, making it possible to diagnose a wide range of conditions in a variety of settings [ 10 ].

(b) Non-invasive diagnostics Non-invasive diagnostic devices are being developed to provide a more comfortable and convenient testing experience for patients [ 11 ]. For example, devices that use breath analysis or skin sensors to diagnose conditions are being developed, eliminating the need for invasive procedures such as blood tests.

(c) Nanotechnology-based diagnostics Nanotechnology is being used to develop new diagnostic devices that are more sensitive and efficient [ 12 ]. For example, nanoparticle-based devices are being developed that can detect specific biomarkers in blood and other body fluids, allowing for the early detection of diseases such as cancers.

2.1.3 Therapeutic devices

These are devices used for treatment, such as insulin pumps, implantable cardiac pacemakers, and deep brain stimulation devices. Following are some of the latest trends and developments in this area:

(a) Wearable therapeutics These devices are becoming increasingly popular as they allow for continuous and non-invasive treatment. For example, wearable devices are being developed to deliver electrical stimulation to the brain to treat conditions such as depression or to deliver drugs directly to the site of an injury [ 13 , 14 ].

(b) Non-invasive stimulators Non-invasive stimulation devices, such as transcranial magnetic or electrical stimulators, are being developed to treat a variety of conditions, including depression, anxiety, and chronic pain. These devices use magnetic or electrical fields to stimulate specific regions of the brain, providing a safe and non-invasive alternative to traditional treatments [ 15 ].

(c) Regenerative therapeutics Regenerative medicine is a growing area of research, and therapeutic devices are being developed to support the growth and regeneration of damaged tissue [ 16 ]. For example, devices are being developed to deliver growth factors to the site of an injury, promoting tissue repair and regeneration.

2.1.4 Medical imaging devices

These are devices used for imaging the body, such as X-ray machines, CT scanners, and MRI machines. Following are some of the latest trends and developments in this area:

(a) Artificial Intelligence-enabled classification and detection which is being used to improve the accuracy and efficiency of medical imaging devices [ 17 ]. For example, AI algorithms can be trained to identify patterns in medical images, such as X-rays or CT scans, helping healthcare providers make more informed diagnoses and prognoses.

(b) Medical imaging-assisted customized 3D printing products medical imaging-assisted 3D printing technology is being used to create customized medical devices, such as surgical models, prosthetics and implantable systems [ 18 , 19 ]. This can be particularly useful for patients with complex medical conditions, as 3D printing allows for the creation of devices that are tailored to the individual's specific needs.

(c) Non-Invasive Imaging Invasive imaging modalities are followed for diagnosing complicated medical conditions, such as coronary angiography for diagnosing coronary artery stenosis. To this end, non-invasive imaging devices that use ultrasound or optical imaging to visualize internal organs or tissues are being developed for detecting various conditions, such as coronary artery stenosis, myocardial infarction, liver metastasis, and beyond [ 20 ].

A schematic representation of AI-based medical image detection and analysis in the context of COVID-19 is presented in Fig.  3 .

A schematic showing deep learning-based medical image detection and analysis in the context of COVID-19. Reproduced from [ 21 ]. © The Authors 2021

2.1.5 Telemedicine devices

These are devices that enable remote patient monitoring and teleconsultations, such as remote patient monitoring systems, webcams, and handheld devices with cameras and communication capabilities. Following are some of the latest trends and developments in this area.

(a) Wearable telemedicine devices These devices are becoming increasingly popular, as they allow for continuous monitoring of a patient's health status [ 22 ]. These devices can track vital signs, such as heart rate and blood pressure, and transmit the data to healthcare providers for analysis.

(b) Remote diagnostic and intervention tools These tools are being developed to allow healthcare providers to diagnose and attend to conditions remotely [ 11 , 23 ]. For example, some telemedicine devices are equipped with cameras and other tools that allow healthcare providers to examine and intervene as necessary to attend to a patient remotely.

(c) Integration with Electronic Health Records Telemedicine devices are being integrated with EHRs to provide a more comprehensive view of a patient's health status [ 24 , 25 ]. This integration can help healthcare providers make more informed decisions about a patient's treatment plan, as they have access to a patient's complete medical history.

A schematic representation showing various DHT-enabled telemedicine modalities is presented in Fig.  4 .

A schematic showing various avenues of digital health technology-enabled telemedicine modalities. Reproduced from [ 26 ]. © The Authors 2021

2.2 Medical materials

As in many other avenues, the advent of digital health paved the way for tremendous development in the field of material science and related research [ 27 ]. Modern material science can contribute smart materials and analytical tools suitable for developing wearable medical devices and sensors required for this purpose. The wearable devices could help in the monitoring of chronic health conditions, therapy, diagnosis, rehabilitation, and tracking of physical activities [ 28 ]. Timely interventions supported by the real-time monitoring of health parameters using wearable devices would save the lives of many. The cloud-based operation of wearable medical devices enables medical professionals to monitor real-time vital parameters and to plan the requirement of physical visits, changes in therapy, and modalities for disease management. A schematic representation showing the work-flow behind cloud-based device performance is presented in Fig.  5 .

Schematic representation of work flow behind a cloud-based wearable device

The materials used for the fabrication of smart wearable medical devices should be biocompatible, flexible/wearable, lightweight, cost-effective, and smart enough to generate transmittable signals in response to changes in physiological parameters such as arterial pulse, body temperature, humidity, motion, and biomarkers in body fluids. Recognition of signals and their transformation are the two fundamental processes associated with any sensors used in the healthcare sector [ 29 ]. In wearable medical devices, the sensors respond to various parameters such as pressure, strain, temperature, the concentration of biomarkers, etc., and generate transmittable electronic/optical signals. Pressure/strain sensors, humidity/gas sensors, electrochemical sensors, colorimetric sensors, etc., are the major types of wearable sensors employed in the healthcare sector [ 30 ]. Even though many inorganic and metallic materials are available with excellent conductivity and sensing capability, inflexibility hinders their application in wearable devices.

Recently a wide variety of advanced smart materials have been utilized for developing wearable devices for healthcare applications. The most prominent ones are described below.

2.2.1 Ionic liquids

Ionic liquids were frequently used for the development of wearable sensors owing to their flexibility, conductivity, broad electrochemical window, better miscibility, negligible toxicity, and low vapor pressure [ 31 ]. Ionic liquid-based smart materials have been reported for a wide range of healthcare applications.

Wearable strain/pressure sensors: Ionic liquid smart devices could convert mechanical strain into processable and transmissible electrical signals in both resistive and capacitive modes. wearable motion sensors could be suitable for monitoring the elderly or rehabilitating population to assess their progress and to provide intervention as and when required [ 32 ].

Thermal sensors: As ionic liquids and ionic liquid crystals are capable of thermal transitions of their phases; they could be employed for monitoring the body temperatures of patients [ 33 ].

Breathing monitors: Ionic liquid-based wearable strain sensors were also reported to monitor the breathing events of patients with COPD or sleep apnoea. Stomach attachment of IL-based wearable breath rate sensors would provide alarms during dangerous breath variations or apnoea [ 34 ].

Sensors for cardiovascular parameters: Ionic liquid-based wearable devices were reported for ECG and EMG recordings [ 35 ].

Others: Ionic liquid-based devices were reported for monitoring skin humidity and evolved gases [ 36 ], glucose or lactate levels and pH from sweat [ 37 ], and for applications in therapeutics and drug delivery [ 38 ].

2.2.2 Carbon materials

A wide array of carbon nanomaterials like carbon nanotubes (CNTs), graphene-based materials, and carbon black (CB) were exploited for healthcare applications. Low cost, mass production capability, biocompatibility, and good mechanical and conduction behaviors made them suitable for generating smart medical devices. Carbon-based smart devices could be fabricated by a variety of methods such as chemical vapor deposition, drop casting, spin coating, screen or inkjet printing, and vacuum filtration. Their major applications are:

Wearable sensors for strain, pressure, temperature and humidity [ 39 ].

Biosensors for biomarker detection [ 40 ].

Others: bone and cartilage regeneration, Bioimaging, and Breath analysis [ 41 ].

An overview of various applications of DHT-enabled carbon nanomaterials in medical and other allied fields is presented in Fig.  6 .

An overview of various applications of DHT-enabled carbon nanomaterials in medical and other allied fields. Reproduced from [ 42 ]. © The Authors 2021

2.2.3 Gold nanomaterials

Gold nanomaterials are known to have better electrical conductivity, mechanical flexibility, biocompatibility, and a wide electrochemical sensing window. Their surfaces could be modified by suitable chemical reactions to improve their electrical and optical behaviors to fine-tune sensing capabilities.

Wearable strain/pressure sensor: Mechanical perturbation on the nano-dimensional gold is converted into a readable electrical signal. They mainly follow a resistance-type, capacitance-type, piezoelectric-type, or triboelectric-type transduction mechanism. Strain/pressure sensors could be applied in soft robotics, human–machine interactions, human motion detection systems, and in health monitoring [ 43 ].

Humidity sensors for human breath analysis: Humidity sensors function on the variation of the impedance values of the membranes with respect to humidity variations [ 44 ].

Others: Gold nanomaterial-based wearable biosensors were reported for various biomarkers, Wearable pH sensors, and bioimaging therapeutics and drug delivery [ 45 ].

In addition to these materials other nano materials, conducting polymers and smart materials were reported to be contributing to the area of digital health [ 46 ]. Thus, material science research flourished extensively due to the arrival of digital health platforms. In addition to material science research, the analytical modalities were also influenced by the rapid development of digital health. NIR and Raman-based non-invasive disease monitoring strategies were reported for the detection of disease conditions and measuring vital parameters [ 47 , 48 ]

3 Developments in pharma and allied areas toward digital health

The tremendous expansion of DHTs at both customer and professional levels has opened a better arena for the effective utilization of digital resources for the benefit of human welfare. In this context, DHTs are playing an increasingly important role in the delivery of pharmaceutical care. Despite the widespread acceptance of personalized technologies in pharma health care, the DHT system is not comprehensively reviewed in terms of drug discovery and development. Drug discovery and development is a complex and multi-step process that involves multiple stages generally taking a time frame of 10–12 years [ 49 ]. The following is a general flowchart that outlines the process of drug discovery:

Target identification and validation: In this stage, researchers identify and validate a biological target (e.g., a protein, gene, or pathway) that is involved in the disease process. They use various high-throughput screening techniques to identify the small molecule or biological entities (hits) that modulate the activity of the target and have potential therapeutic effects [ 50 ].

Lead optimization: In the optimization stage, the researchers optimize the potency, selectivity, pharmacokinetics, and pharmacodynamics of the lead compounds to produce lead candidates [ 51 ].

Pre-clinical evaluation: This is the stage where the researcher conducts a series of in vitro and in vivo studies to evaluate the safety, efficacy, pharmacokinetics, and pharmacodynamics of the lead candidates.

Clinical trials: Lead candidates that have passed preclinical testing are then tested in human clinical trials to evaluate their safety and efficacy in a larger population.

Regulatory approval: If the clinical trials are successful, the drug is then submitted to regulatory agencies for approval.

Marketing and sales: only after the drug gets approved, it can be manufactured and marketed for therapeutic use.

Post-market surveillance: This will be an indefinite process making the regulators monitor the efficacy and safety of the drug throughout its lifetime.

In this context, digital technologies are playing an increasingly important role in the development of new drugs. Some of the key ways that digital technologies are being used in drug development are presented in Fig.  7 and are detailed in the following sections.

An overview of applications of digital health technologies in drug discovery and development

3.1 Drug design

Computer-aided drug design (CADD) is the process of creating new drugs based on a thorough understanding of the biological target and its interaction with potential drugs [ 52 ]. It is a multi-disciplinary field that combines knowledge from chemistry, biology, pharmacology, and computational modeling to develop new drugs. There are several in silico approaches in practice for computer-aided drug design. This includes,

Pharmacophore modeling It is a computational approach used to predict the molecular features that are responsible for a molecule's biological activity. This is a useful tool for drug discovery and design, as it allows researchers to identify key structural features that are important for a molecule's interaction with its target protein, and to design new molecules that are likely to have similar activities. There are two types of pharmacophore modeling. (a) Ligand-based drug design This approach is based on the structural information of the active ligands that bind to the target. In a study by Kist et al. [ 53 ] by employing a ligand-based drug design approach, novel potential inhibitors of the mTor pathway were identified as having comparable or better properties to that of the classic drug rapamycin. (b) Structure-based drug design This strategy uses the three-dimensional structure of the biological target to design drugs that fit into specific pockets or active sites on the target, effectively blocking its activity. An example of a structure-based drug design strategy is reported in the development of 5 LOX inhibitors, a therapeutic target for asthma as well as other inflammatory diseases [ 54 ]. Catalyst software package, LigandScout, MOE (Molecular Operating Environment, Schrodingers Maestro, PyRx are some of the software packages used to generate pharmacophore models.

Drug target fishing This is a computational approach that is used to identify potential drug targets for a particular disease or biological process. And the goal of drug target fishing is to find proteins or other molecular targets that are likely to be involved in the disease or process of interest and to design drugs that can interact with these targets in a specific way to produce a therapeutic effect [ 55 ]. Different approaches could be employed for drug target identification that includes (i) homology modeling: comparing the structure of a protein of interest to the structures of other related proteins that are already known targets and identifying conserved regions in the protein structure that could serve as a potential target [ 56 ], (ii) bioinformatics: analyzing biological data, such as genomic sequences, transcriptomic data, and protein–protein interaction data, to identify potential drug targets [ 57 ]. (iii) systems biology: studying the complex interactions between different biological components in a particular disease or process [ 58 ] and (iv) High-Throughput screening [ 59 ]. The experimental setup in this context is the molecular docking and molecular dynamics (MD) simulation. There are several widely used software programs available for molecular docking, including AutoDock, Glide, Leadit, and eHiTS. There are several widely used software programs available for molecular dynamics simulation, including GROMACS, AMBER, NAMD, CHARMM, and LAMMPS that have wide applications in drug discovery.

3.2 Pre-clinical research

Pre-clinical research in drug development is a phase where a variety of experiments are conducted to assess the safety and efficacy of a new drug candidate. These may include toxicity studies to determine the potential for harmful effects, pharmacology studies to evaluate the drug’s interactions with the body, and efficacy studies to determine the potential therapeutic benefits of the drug. They involve animal models and the administration of the drugs to animals to assess any adverse effects and determine the optimal dosing regimen. Animal testing has been used for decades to evaluate the safety and efficacy of new drug candidates, but there is growing concern about the ethical and scientific limitations of this approach [ 60 ]. As a result, there is a growing interest in developing alternative methods to animal testing. In silico modeling of biological systems is one such approach that could be employed as an alternative to animal testing. The computer simulations of biological systems predict the behavior of a particular biological system, and hence, are used to evaluate the safety and efficacy of potential new drugs. In silico models can also be used to analyze large amounts of data, such as gene expression data or proteomics data generated following the interaction of the drug with the biological system. By using AI and Machine Learning (ML) algorithms, these models can identify patterns in the data that would not be easily noticeable by a human researcher, providing new insights into the biological mechanisms underlying disease and thereby helping in the appropriate intervention strategies. The following are the commonly used tools in predicting safety and toxicity in pre-clinical research.

QSAR (Quantitative Structure–Activity Relationship) models These are computational tools that are used to predict the biological activity of a chemical compound based on its molecular structure [ 61 ]. ML algorithms, such as artificial neural networks, decision trees, and support vector machines, are then used to identify relationships between molecular descriptors and biological activities [ 62 ]. Several software programs can be used for QSAR modeling, including KNIME, Pipeline Pilot, MOE (Molecular Operating Environment), OChem and R.

Virtual Toxicity Predictors (VTPs) Unlike QSAR models, virtual toxicity predictor software tools use molecular modeling and simulation to predict the toxicity of a potential new treatment based on its molecular structure. QSAR models typically provide a quantitative prediction of the toxicity of a chemical compound, while VTPs can provide more detailed information about the potential toxicity mechanisms [ 63 ]. Some of the popular software programs used for virtual toxicity prediction include ToxCast, Toxtree, VEGA (Virtual Expert System for Toxicity Assessment), OSIRIS, Leadscope and eTOXlab.

ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) analysis It helps in understanding the pharmacokinetics and pharmacodynamics of a drug and is a crucial step in the drug discovery process [ 64 ]. There are several software programs available for ADMET analysis, including both commercial and open-source options. Some popular choices include Schrodinger, Simulations Plus, OpenEye, Pipeline Pilot, Molsoft ICM-Pro, SwissADME, DEREK. However, the choice of software will depend on the specific needs of the user and the type of ADMET analysis being performed.

PBPK (Physiologically-Based Pharmacokinetic) modeling This type of modeling takes into account the anatomy and physiology of the body to simulate the distribution and elimination of drugs. The main goal of PBPK modeling is to predict how a drug will behave in the body based on the known physiological properties of the drug and the individual being treated.[ 65 ]. Examples of software available for PBPK modeling include SimCyp GastroPlus, PK-Sim, ADAPT II, PKQuest, MCSim.

3.3 Clinical trials

The primary goal of a clinical trial is to determine if a new drug is effective in treating a specific medical condition and if it is safe for human use. Clinical trials are usually conducted in three phases, each of which provides increasing amounts of information about the drug's safety and effectiveness. Phase 1 trials typically involve a small number of healthy volunteers and are designed to test the drug's safety and identify any side effects. Phase 2 trials involve a larger number of patients with the specific medical condition the drug is intended to treat. These trials are designed to test the drug's effectiveness and gather additional information about its safety. Phase 3 trials involve an even larger number of patients and are the final stage of testing before a drug is submitted for approval by regulatory agencies [ 66 ]. These trials are designed to provide a more complete picture of the drug's benefits and risks and to confirm its effectiveness.

The DHTs are rapidly changing the way clinical trials are conducted. They are being used to streamline the clinical trial process, allowing for faster and more efficient trials[ 67 ] Here are some examples of DHTs used in clinical trials:

Electronic Patient-Reported Outcomes (ePRO) ePRO is a digital tool that allows patients to report symptoms, side effects, and other outcomes directly to the study team. This technology can help improve patient compliance and reduce the need for in-person visits [ 68 ].

Mobile Health (mHealth) Applications mHealth applications can be used to collect data from patients, provide education and support, and monitor health status. This technology can help increase patient engagement and improve data quality [ 69 ].

Electronic Clinical Outcome Assessments (eCOA) eCOA is a digital tool that allows patients to self-report outcomes, such as quality of life, using a smartphone or tablet. This technology can help reduce the burden on patients and improve data quality [ 70 ].

Telemedicine Telemedicine technology, such as video conferencing, can be used to conduct virtual visits with patients. This technology can help reduce the need for in-person visits, increase patient convenience, and improve patient engagement [ 71 ].

Wearable Devices Wearable devices, such as smartwatches and fitness trackers, can be used to collect data on physical activity, sleep patterns, and other health metrics. This technology can help improve data quality and increase patient engagement [ 72 ].

3.4 Post-market surveillance

DHTs have revolutionized the way drugs are monitored after they have been approved and entered the market. In the past, post-market surveillance of drugs relied heavily on passive systems, where healthcare professionals and patients reported adverse events or side effects. However, with the advent of DHTs, this process has become more proactive and efficient. Some examples of DHTs used in post-market surveillance of drugs include:

Electronic health records EHRs provide a centralized platform for healthcare professionals to report and track adverse events associated with drugs. This information can then be analyzed to identify potential safety issues with a drug [ 73 ].

Mobile health (mHealth) applications mHealth apps allow patients to easily report adverse events or side effects from their smartphones. This provides a more direct and convenient way for patients to report issues, which can lead to quicker identification of safety concerns [ 74 ].

Clinical trials platforms Clinical trial platforms have become increasingly digital, allowing for real-time monitoring of drug safety during the clinical trial phase. This information can then be used in post-market surveillance to identify potential safety issues with a drug [ 75 ].

Real-world data (RWD) platforms RWD platforms gather and analyze data from a variety of sources, including EHRs, claims data, and patient-generated data, to provide a more complete picture of a drug's safety profile. This information can be used to identify potential safety issues and monitor the effectiveness of drugs in real-world settings [ 76 ].

Overall, digital technologies are playing a critical role in drug discovery and development, helping to improve the speed, efficiency, and accuracy of the drug development process. This is leading to the discovery of new treatments for a wide range of diseases and conditions, improving patient outcomes and transforming the healthcare industry.

4 Developments in bio and allied technologies toward digital health

Digital health in biotechnology and bioengineering refers to the use of digital technologies to develop and improve biotechnology and bioengineering products and applications. Digital health is playing a critical role in these fields by enabling the development of more sophisticated models of biological systems and by facilitating the optimization of bio-based healthcare product design and manufacture. Following are some of the key areas where the innovation-driven science and technological advances can transform digital health worldwide.

4.1 Omics biology

Omics is a field of study in medicine that encompasses various sub-disciplines such as genomics, transcriptomics, proteomics, metabolomics, and epigenomics. The goal of omics is to understand the underlying mechanisms of biological processes and diseases by looking at the collective behavior of all the molecules involved, such as genes, proteins, and metabolites. By combining data from these various omics disciplines, researchers can gain a more comprehensive understanding of the molecular basis of health and disease, which may lead to the development of new diagnostic tools, therapies, and personalized medicine approaches.

Omics technologies are also playing an increasingly important role in digital health, where they can be leveraged to improve the accuracy and precision of health assessments, diagnoses, and treatments. Following are a few examples of how Omics technologies can aid in digital health.

(a) Personalized medicine Omics data can be used to predict an individual's risk for developing certain diseases, monitor their health status over time, and tailor treatments to their unique genetic profile [ 77 ].

(b) Predictive analytics Predictive models based on omics data can be used to identify individuals who are at high risk for a disease, such as cancer, and to monitor their health status over time, allowing for early intervention and improved outcomes [ 78 ].

(c) Clinical decision support Clinical decision support systems that incorporate omics data can provide healthcare providers with real-time information and recommendations to help them make more informed treatment decisions for their patients [ 79 ].

(d) Omics data collecting medical devices medical devices that collect omics data, such as continuous glucose monitoring systems, can be used to monitor a patient's health status and to provide early warning signs of potential health issues.

(e) Omics data integrated telemedicine platforms that incorporate omics data can provide remote healthcare services, such as virtual consultations, to individuals in remote or underserved communities, improving access to care and outcomes [ 80 ].

A schematic representation showing omics-based DHTs towards personalized medicine is presented in Fig.  8 .

A schematic showing potential applications of omics-based digital health technologies toward personalized medicine. Reproduced from [ 81 ]. © The Authors 2018

4.2 Big data analytics

Big data refers to extremely large and complex data sets that are generated from various sources, including EHRs, medical imaging, genetic sequencing, and other sources. The market size for big data in digital health has been growing rapidly in recent years, driven by the increasing adoption of DHTs and the growing demand for data-driven decision-making in healthcare. According to market research, the global big data in digital health market was valued at approximately US$ 39.7 billion in 2022 and is expected to grow at a CAGR of 19.2% from 2022 to 2032 [ 82 ]. However, the use of big data in medicine also raises concerns about privacy, security, and the ethics of data collection and analysis. It is important to address these concerns and ensure that the benefits of big data are maximized while minimizing its risks.

Big data plays a critical role in digital health by providing the vast amounts of information that are needed to drive innovation and improve patient outcomes.

(a) Big electronic health records data analytics One of the main ways that big data is used in digital health is through the analysis of EHRs and other sources of health-related data [ 83 ]. EHRs contain a vast amount of patient information, including demographic data, medical history, lab results, and other information. By analyzing this data, healthcare providers can gain insights into patient populations and identify trends and patterns that can inform decision-making and improve patient care.

(b) Big wearable devices data analytics big data is used in digital health through the analysis of data generated by wearable devices and other DHTs [ 84 ]. These devices generate vast amounts of data, including information about physical activity, sleep patterns, and other health-related metrics. This data can be used to track health status, monitor disease progression, and inform treatment plans.

(c) Big omics data analytics Another way that big data is used in medicine is through the analysis of genetic data [ 85 ]. Advances in genetic sequencing technologies have enabled the rapid and cost-effective generation of large amounts of genetic data, which can be used to identify the genetic basis of diseases and inform the development of personalized medicine.

(d) Big imaging data analytics Big data is also being used in medical imaging to improve diagnosis and treatment [ 86 ]. For example, advanced algorithms can be used to analyze medical images to identify patterns and anomalies that may indicate disease. This information can then be used to inform diagnosis and treatment planning.

(e) Big data and predictive analytics Big data is also being used in digital health to develop predictive models and algorithms that can improve health outcomes. For example, ML algorithms can be trained on large data sets to identify patterns and relationships that can inform decision-making and improve disease management [ 87 ].

4.3 Personalized/precision medicine

Personalized medicine, also known as precision medicine, is a medical approach that takes into account individual differences in genes, environment, and lifestyle to develop a customized approach to healthcare [ 88 ]. The goal of personalized medicine is to provide the right treatment, at the right time, for the right patient. In traditional medicine, treatments are often based on a "one-size-fits-all" approach, which does not take into account the unique differences between individuals. However, with the advancement of genomic technologies and the increasing availability of patient data, it is now possible to tailor treatments to the specific needs of each patient. It is therefore important to note that personalized medicine is still in its early stages of development, and more research is needed to fully realize its potential.

Several DHTs are playing a critical role in personalized medicine viz. EHRs, telemedicine tools, wearable devices, big data analytics, additive manufacturing, AI/ML-based algorithms, and several personalized mobile apps. These personalized DHTs are playing an increasingly important role in precision medicine, providing healthcare providers with the tools they need to deliver more effective and efficient care to patients. Examples of personalized medicine include:

(a) Precision or personalized care Precision oncology is a type of personalized medicine that uses genetic information to tailor cancer treatments to the specific needs of each patient [ 89 ]. Precision psychiatry is a type of personalized medicine that uses genetic information to tailor psychiatric treatments to the specific needs of each patient [ 90 ].

(b) Precision or personalized drug dosage Personalized drug therapy is a type of personalized medicine that uses genetic information to determine the most effective drug for a particular patient [ 91 ].

(c) Precision surgical models The use of digital technologies such as computer-aided designing and manufacturing helps in scanning the defect site and manufacturing a surgical model utilizing 3D printing for enabling the surgeons to plan the surgery effectively and efficiently [ 92 ].

(d) Personalized regenerative therapies Advanced tissue engineering and regenerative medicine technologies such as 3D bioprinting help in bio-fabricating a tissue that is not only biocompatible but also fits precisely to the defect size of the patient [ 93 ].

(e) Precision public health Proactive use of technology brings in new avenues to address many age-old public health issues. Optimal use of geographic information systems (GIS) and other spatiotemporal analysis will help in more precise and timely field-level interventions, which are crucial in the early detection and control of infectious diseases. This is well documented in the control strategies of the recent Zika outbreak in the USA. In Florida, only two small counties were to be put under lockdown, that too for a short while, to arrest the spread of Zika in 2016 [ 94 ]. The predict and prevent framework is a futuristic method to address the burgeoning problem of non-communicable diseases through precision public health. The use of Electronic medical Support for Public health (ESP) and its visualization platform Riscape helps in long-term follow-up and real-time intervention in the surveillance of NCDs [ 95 ].

A schematic representation showing DHT-enabled personalized medicine is presented in Fig.  9 .

A schematic showing integration of several technologies toward personalized/precision digital health. Reproduced with permission from [ 88 ]. © 2019 Elsevier Ltd

4.4 Synthetic biology

Synthetic biology is a multidisciplinary field that combines biology and engineering to design and construct novel biological systems for various applications [ 96 ]. It involves the manipulation of genetic material and metabolic pathways in living organisms to create new functions or modify existing ones. In synthetic biology, researchers use a combination of molecular biology techniques, computational modeling, and engineering principles to design, build, and test biological systems. These systems can be used in a wide range of applications, including the creation of new medicines, the development of biosensors, and beyond. One of the key features of synthetic biology is the use of standard biological parts, such as genes and regulatory elements, that can be combined and reused to create complex biological systems. This modular approach allows for rapid design and testing, as well as the potential for large-scale deployment of these systems. Synthetic biology has the potential to revolutionize many areas of biotechnology, medicine, and beyond, but it also requires careful consideration and oversight to ensure its safe and responsible development.

Synthetic biology has the potential to play a significant role in digital health [ 97 ]. One example of how synthetic biology can be used in digital health is the development of biosensors [ 98 ]. Biosensors are devices that use biological components, such as enzymes or antibodies, to detect specific substances. Synthetic biology can be used to design and construct biosensors that are specific for certain biomarkers, such as glucose or cholesterol, which can be used for continuous monitoring of health status. The results can be transmitted wirelessly to a digital platform for analysis and interpretation, enabling remote monitoring and disease management. Another example is the use of synthetic biology in the development of personalized medicine [ 99 ]. By using synthetic biology to design and engineer cells, researchers can create new therapeutic interventions that are tailored to a specific individual's needs [ 100 ]. For example, synthetic biology can be used to create cells that produce a specific protein or to correct genetic mutations that cause disease. These cells can then be monitored and controlled using digital technologies, enabling real-time monitoring of treatment efficacy and enabling adjustments to the therapy as needed. Overall, synthetic biology has the potential to revolutionize digital health by enabling the development of new technologies that can be integrated into digital platforms to improve health and healthcare.

4.5 Systems biology

Systems biology is an interdisciplinary field of study that aims to understand the complex relationships between the components of biological systems, such as cells, tissues, and organs [ 101 ]. It seeks to understand how these systems interact and function as a whole, rather than simply focusing on individual components in isolation. Systems biology approaches biological systems from a holistic perspective, using computational and mathematical models to simulate the interactions between different components and to predict the behavior of the system as a whole. It also incorporates high-throughput data from various sources, such as genomics, proteomics, and metabolomics, to create a comprehensive view of the system. One of the key goals of systems biology is to understand the underlying mechanisms of disease and to develop new therapeutic strategies that target the root causes of diseases, rather than just their symptoms. It also seeks to improve our understanding of the interactions between different components of the body and how they contribute to health and disease. In addition, systems biology is playing an important role in the development of personalized medicine, as it provides a framework for integrating patient-specific data and generating personalized models of disease.

Systems biology can play a critical role in digital health in several ways as follows. (a) Predictive Modelling: Systems biology can be used to build predictive models of disease, which can help healthcare providers to identify individuals at risk of developing certain conditions and to develop targeted prevention strategies [ 102 ]. (b) Clinical Decision Support: Systems biology can be used to develop clinical decision support systems, which can help healthcare providers to make more informed treatment decisions based on the latest scientific evidence and patient-specific data [ 103 ]. (c) Clinical Trial Design: Systems biology can be used to inform the design of clinical trials, by helping to identify the most promising therapeutic targets and to predict the outcomes of different treatment strategies [ 104 ]. (d) Data Integration and Management: Systems biology can be used to integrate and manage large amounts of patient data, including genomic, proteomic, and clinical data, to create a comprehensive view of the patient and to inform the development of personalized treatment plans [ 105 ]. (e) Monitoring and Evaluation: Systems biology can be used to monitor the effectiveness of treatments and to evaluate the impact of treatments on patient outcomes [ 106 ]. Overall, systems biology offers a framework for integrating and analyzing large amounts of patient data and for developing personalized models of disease, which can inform the development of more effective and efficient treatment strategies.

A schematic representation showing DHTs integrated systems biology approaches toward effective clinical decisions are presented in Fig.  10 .

A schematic showing a systems biology approach toward developing super models for effective clinical decisions. Reproduced from [ 107 ]. © The Authors 2015

5 Challenges associated with digital health technologies

With opportunities comes risks, and the same is true for the DHTs that present several challenges along with their opportunities [ 108 ].

(a) Privacy, security and ethical concerns With the increasing growth of mobile-based health apps and connected health systems, much the data including the personal information of patients is being collected. For instance, privacy and security are major concerns in digital health, with the sensitive nature of health data, making it a prime target for hackers. Also, DHTs had several ethical issues including the question of who owns the data.

(b) Interoperability This is another major challenge in digital health, with different digital health systems not being able to communicate with each other effectively. This makes it difficult for healthcare providers to access and share patient data, which can negatively impact patient outcomes.

(c) Regulatory framework Regulation is yet another bottleneck in digital health, with different countries having different regulations and guidelines for DHTs.

(d) Public awareness As many healthcare providers and patients are resistant to change and not fully understanding the benefits of DHTs, creating awareness about its benefits and risks is necessary for ensuring that DHTs are accepted by the general population.

(e) Legislative issues The laws of the land should appreciate the newer trends in science and technology for their optimal use. For example, vagueness in the legal validity of digital prescriptions was a major hurdle in the update of telemedicine in India before the hurried enactment of the Telemedicine Practice Guidelines in 2020, in the wake of the Covid-19 pandemic [ 109 , 110 ].

6 Digital health—an Indian perspective

6.1 digital health—initiatives from the government of india.

Digital health is a rapidly growing field that involves the use of digital technologies to improve health and healthcare delivery in India [ 111 ]. In recent years, there has been a significant investment in digital health infrastructure and initiatives, and a tremendous increase in the use of DHTs by both healthcare providers and patients in India. Examples of digital health initiatives in India include:

(a) Telemedicine Telemedicine services are widely available in India and are being used to provide remote consultations and support to patients in rural and underserved areas [ 112 ].

(b) Electronic health records Several hospitals have initiated programs to establish EHRs for all their clients, and even a comprehensive nationwide EHR platform is under consideration for the storage and sharing of patient health information between healthcare providers [ 113 ].

(c) mHealth The use of mobile health technologies, such as mobile apps and SMS-based services, is widespread in India and is being used to deliver health information and services to patients [ 114 ].

(d) Digital health marketplaces Digital health marketplaces, such as online pharmacies and telemedicine platforms, are becoming increasingly popular in India and are providing patients with access to a wide range of health products and services [ 115 ].

(e) Artificial intelligence in healthcare AI is being used in various applications in the Indian healthcare system, such as in radiology, oncology, and cardiology, to improve diagnosis and treatment [ 116 ].

Several institutions in India are working on digital health, including (a) Indian Council of Medical Research (ICMR): The ICMR is the main body responsible for promoting and coordinating biomedical research in India and has been involved in several digital health initiatives. (b) Apollo Hospitals: Apollo Hospitals is one of the largest healthcare groups in India and is a pioneer in the use of DHTs, including telemedicine and EHRs. (c) Tata Consultancy Services (TCS): TCS is a leading technology and consulting company in India and is involved in several digital health initiatives, including the development of EHRs and telemedicine solutions. (d) MedTech Zone: MedTech Zone is a digital health accelerator program in India that supports the development of early-stage digital health start-ups. (e) AI in Healthcare India: AI in Healthcare India is a non-profit organization that promotes the use of AI in healthcare in India and provides a platform for the exchange of ideas and knowledge on AI in healthcare. These are just a few of the many institutions in India that are involved in digital health initiatives.

Several companies in India are working on digital health, including (a) Practo and Doctor on Call: Practo and Doctor on Call are prominent digital health companies in India offering online doctor appointments including remote consultations and support to patients. (b) NetMeds, Tata 1 mg, Medlife and PharmEasy: these are prominent online platforms for ordering medicines and booking diagnostic tests. (c) HealthKart and HealthifyMe: these are some prominent digital health platforms in India that provide health supplements, and zpersonalized health and wellness coaching. (d) GoQii: GoQii is a digital health platform in India that provides personalized health and wellness coaching and wearable fitness trackers. (e) Besides, several small to medium scale digital health services offer online diagnostic services. These are just a few of the many companies in India that are involved in digital health initiatives. The Indian digital health market is rapidly growing, and many more companies and start-ups are entering the market.

The National Health Authority of the Government of India supports digital health through several schemes. Some of the prominent schemes include (a) Digital India: Digital India is a government initiative in India that aims to transform India into a digitally empowered society and knowledge economy. The initiative includes several components related to digital health, including CoWin and Arogya Sethu ( https://digitalindia.gov.in/ ). (b) e-Health & Telemedicine: various Information & Communication Technologies (ICT)-enabled initiatives are undertaken for improving the efficiency and effectiveness of the public healthcare system ( https://main.mohfw.gov.in/Organisation/departments-health-and-family-welfare/e-Health-Telemedicine ). (c) Ayushman Bharat Digital Mission (ABDM ): through this mission, the Govt. of India aims to develop the backbone necessary to support the integrated digital health infrastructure of the country ( https://abdm.gov.in/ ). (d) National Digital Health Mission (NDHM): The NDHM is a government-led health mission in India that aims to provide universal health coverage to all the citizens in the country through digital technologies ( https://www.makeinindia.com/national-digital-health-mission ). (e) National Health Stack (NHS): The program aims to facilitate the collection of comprehensive healthcare data to aid in policymaking, allocation of resources and identification of needy populations for health schemes. These are just a few of the many government schemes and it is seen that the Indian government is committed to promoting and supporting digital health initiatives in the country. A flyer released by Govt. of India on NDHM is presented in Fig.  11 .

A schematic representation showing various components of the National Digital Health Mission initiative by the Government of India. Adopted with permission from [ 117 ]. © 2023 Sanskriti IAS

Despite the significant opportunities and progress in digital health in India, several challenges need to be addressed, including the need for robust privacy and security measures, the need for greater investment in digital health infrastructure, and the need for greater training and capacity building for healthcare providers.

6.2 Digital health—initiatives from the state of Kerala (India)

Kerala is at the forefront of implementing EHRs for its population. Well before the era of the Individual Health ID of the Ayushman Digital Health Mission, Govt. of Kerala launched its ambitious eHealth Kerala project to create EHRs for all of its citizens in 2016. Though not perfect, it has added to the impetus of digitalization of the health sector in Kerala, thanks to the earlier implementation of the District Health System software (DHIS2) in all the 1000-plus public health institutions in the state.

The health workers in Kerala are familiar with digital health tools, and many public health centers use electronic medical records. However, the use of data for decision-making is not yet a norm in the health system, nor the public health area. Reasons for this low use of information are many, the lack of a clear data policy on who can have access to the data at various levels is a major one.

The state could take advantage of the e-Sanjeevani telemedicine platform during the Covid-19 pandemic to cater to the healthcare needs of its population. In the current e-Health Kerala project, there is a facility to do telemedicine consultations within the regular consultation hours. A few institutions in Kerala, like the Regional Cancer Centre (RCC) and SCTIMST, are going ahead with the doctor-to-doctor e-Sanjeevani consultations.

6.3 Digital health—initiatives from SCTIMST, Trivandrum (India)

Sree Chitra Tirunal Institute for Medical Sciences and Technology (SCTIMST) is an Institution of National Importance under the Department of Science and Technology, Govt. of India. The institute is known for its high-quality advanced treatment of cardiac and neurological disorders, indigenous development of technologies for biomedical devices and public health training and research. The Institute has three wings—the Hospital, Biomedical Technology Wing and the Achutha Menon Centre for Health Science Studies (AMCHSS). The institute is proactive in catching up with the latest technologies in the field including DHTs.

In the clinical scenario, the medical wing of the institute has a custom-built electronic medical record system for its clinical services, entirely created by the in-house computer division. In recent years, the institute has incorporated newer standards, including the SNOMED-CT coding for the diagnostic fields. The anonymized data extraction from this system has supported many research initiatives of the institute. Similarly, SCTIMST has created a fully geo-referenced mapping of its field practice area covering around 35,000 households (a population of 1.32 lakhs) with community participation [ 118 ]. The experiences from such and similar initiatives have given confidence to the state government to undertake more challenging digital health interventions like the e-Health Kerala project. Besides, the various divisions of the medical wing are actively involved in the development of various DHTs in clinical settings.

The Biomedical Technology Wing of the Institute was instrumental in nurturing the Indian medical device industry through know-how development and transfer, providing internationally accredited testing services and offering technology incubation facilities for young entrepreneurs. The BMT wing is actively involved in the development of DHT-enabled medical devices such as para-corporeal left ventricular assist device, centrifugal blood pump with blood flowmeter, deep brain stimulator system for movement disorders, intracranial electrodes, optical peripheral nerve stimulator, 3D printed liver and skin tissue constructs for regenerative applications, PT/INR sensing devices, loop-mediated isothermal amplification-based diagnostic kits, implantable cardioverter defibrillator, programmable hydrocephalus shunt, implantable micro infusion pump with wireless recharging system, and POC kits for sepsis and chlamydia trachomatis. Besides, there are many innovative projects based on smart biomaterials and combinational medical devices under development.

The AMCHSS, the public health division of SCTIMST, was a partner in the customization of the DHIS-2 software for the Indian context, which was piloted in its field practice area in Athiyannur block in Thiruvananthapuram. This led to many field-based research initiatives on digital health, spanning from its use in infectious diseases [ 119 , 120 , 121 , 122 ] to non-communicable diseases [ 123 , 124 , 125 ]. In recent times AMCHSS is moving ahead with infectious disease modeling and the use of data science approaches to large-scale data [ 126 , 127 , 128 , 129 , 130 ]. Lately, the ICMR has entrusted AMCHSS with the analysis of the COVID-19 test data for the entire country. Besides, the AMCHSS is actively involved in the development of various public-health-focused DHTs.

7 Conclusions

Digital health technologies (DHTs) aim to improve the healthcare system across the globe. By providing more accurate diagnoses, enabling more effective treatments and improving patient engagement and compliance, DHTs have the potential to significantly improve patient outcomes. By enabling more efficient and effective delivery of healthcare services and by reducing the need for in-person visits, DHTs can reduce healthcare costs. DHTs can increase access to healthcare through means of remote consultations and support to patients in remote and underserved areas. By compiling patient data from several healthcare providers a more comprehensive digital health record of each patient can be created, providing more accurate and comprehensive patient data to healthcare providers. Thus DHTs can significantly improve healthcare delivery by enabling informed clinical decisions. Further big data can be analyzed by AI-clinical decision support systems to aid the healthcare provider.

Digital health is a rapidly developing field and DHTs are providing new opportunities for innovation and growth, and thus are transforming the medical, pharma, biotech and allied fields. In the medical devices sector, innovations in the field of smart materials, wearable devices, and AI/ML-based systems are rapidly being introduced for clinical use. In the pharma sector, the use of digital technologies has widespread use through various stages of drug development viz. drug design, preclinical validation, and clinical trial. In the biotech and bioengineering sector, digital technologies are aiding in the development of precision and personalized medicinal products. This means there is a growing opportunity for start-ups and established companies to develop new and innovative DHTs. However, a word of caution is necessary to beware of the risks associated with DHTs including ethical and technical concerns.

Digital Health Market Size, Share & Trends Report, 2030. https://www.grandviewresearch.com/industry-analysis/digital-health-market

Digital Health—India. https://www.statista.com/outlook/dmo/digital-health/india

Smartphone-based patient monitoring global market report 2022. https://www.businesswire.com/news/home/20230104005482/en/Smartphone-Based-Patient-Monitoring-Global-Market-Report-2022-Featuring-Leading-Players---Apple-Boston-Scientific-Cerner-Medtronic-and-Phillips-Healthcare---ResearchAndMarkets.com

Dunn J, Runge R, Snyder M (2018) Wearables and the medical revolution. Pers Med 15:429–448. https://doi.org/10.2217/pme-2018-0044

Article   Google Scholar  

Venkatesan M, Mohan H, Ryan JR et al (2021) Virtual and augmented reality for biomedical applications. Cell Rep Med 2:100348. https://doi.org/10.1016/j.xcrm.2021.100348

Vinolo Gil MJ, Gonzalez-Medina G, Lucena-Anton D et al (2021) Augmented reality in physical therapy: systematic review and meta-analysis. JMIR Serious Games 9:e30985. https://doi.org/10.2196/30985

Chen M, Decary M (2020) Artificial intelligence in healthcare: an essential guide for health leaders. Healthc Manag Forum 33:10–18. https://doi.org/10.1177/0840470419873123

Li L, Lou Z, Chen D et al (2018) Recent advances in flexible/stretchable supercapacitors for wearable electronics. Small 14:1702829. https://doi.org/10.1002/smll.201702829

Choi S, Lee H, Ghaffari R et al (2016) Recent advances in flexible and stretchable bio-electronic devices integrated with nanomaterials. Adv Mater 28:4203–4218. https://doi.org/10.1002/adma.201504150

Gous N, Boeras DI, Cheng B et al (2018) The impact of digital technologies on point-of-care diagnostics in resource-limited settings. Expert Rev Mol Diagn 18:385–397. https://doi.org/10.1080/14737159.2018.1460205

Salem M, Elkaseer A, El-Maddah IAM et al (2022) Non-invasive data acquisition and iot solution for human vital signs monitoring: applications. Limit Future Prospects Sens 22:6625. https://doi.org/10.3390/s22176625

Belushkin A, Yesilkoy F, Altug H (2018) Nanoparticle-enhanced plasmonic biosensor for digital biomarker detection in a microarray. ACS Nano 12:4453–4461. https://doi.org/10.1021/acsnano.8b00519

Kar A, Ahamad N, Dewani M et al (2022) Wearable and implantable devices for drug delivery: applications and challenges. Biomaterials 283:121435. https://doi.org/10.1016/j.biomaterials.2022.121435

Long Y, Li J, Yang F et al (2021) Wearable and implantable electroceuticals for therapeutic electrostimulations. Adv Sci 8:2004023. https://doi.org/10.1002/advs.202004023

Pilotto A, Rizzetti MC, Lombardi A et al (2021) Cerebellar rTMS in PSP: a double-blind sham-controlled study using mobile health technology. Cerebellum 20:662–666. https://doi.org/10.1007/s12311-021-01239-6

Farahani M, Shafiee A (2021) Wound healing: from passive to smart dressings. Adv Healthc Mater 10:2100477. https://doi.org/10.1002/adhm.202100477

Gore JC (2020) Artificial intelligence in medical imaging. Magn Reson Imaging 68:A1–A4. https://doi.org/10.1016/j.mri.2019.12.006

Pugliese L, Marconi S, Negrello E et al (2018) The clinical use of 3D printing in surgery. Update Surg 70:381–388. https://doi.org/10.1007/s13304-018-0586-5

Sun L, Wong Y (2019) Personalized three-dimensional printed models in congenital heart disease. J Clin Med 8:522. https://doi.org/10.3390/jcm8040522

Sirajuddin A, Mirmomen SM, Kligerman SJ et al (2021) Ischemic heart disease: noninvasive imaging techniques and findings. Radiographics 41:E990–E1021. https://doi.org/10.1148/rg.2021200125

Yang D, Martinez C, Visuña L et al (2021) Detection and analysis of COVID-19 in medical images using deep learning techniques. Sci Rep 11:19638. https://doi.org/10.1038/s41598-021-99015-3

Shan R, Sarkar S, Martin SS (2019) Digital health technology and mobile devices for the management of diabetes mellitus: state of the art. Diabetologia 62:877–887. https://doi.org/10.1007/s00125-019-4864-7

Ong DSY, Poljak M (2020) Smartphones as mobile microbiological laboratories. Clin Microbiol Infect 26:421–424. https://doi.org/10.1016/j.cmi.2019.09.026

Coons SJ, Eremenco S, Lundy JJ et al (2015) Capturing patient-reported outcome (PRO) data electronically: the past, present, and promise of epro measurement in clinical trials. Patient Cent Outcomes Res 8:301–309. https://doi.org/10.1007/s40271-014-0090-z

Dinh-Le C, Chuang R, Chokshi S, Mann D (2019) Wearable health technology and electronic health record integration: scoping review and future directions. JMIR MHealth UHealth 7:e12861. https://doi.org/10.2196/12861

Shen Y-T, Chen L, Yue W-W, Xu H-X (2021) Digital technology-based telemedicine for the COVID-19 pandemic. Front Med 8:646506. https://doi.org/10.3389/fmed.2021.646506

Guo C, Ashrafian H, Ghafur S et al (2020) Challenges for the evaluation of digital health solutions—a call for innovative evidence generation approaches. NPJ Digit Med 3:110. https://doi.org/10.1038/s41746-020-00314-2

Kim J, Campbell AS, de Ávila BE-F, Wang J (2019) Wearable biosensors for healthcare monitoring. Nat Biotechnol 37:389–406. https://doi.org/10.1038/s41587-019-0045-y

Koydemir HC, Ozcan A (2018) Wearable and implantable sensors for biomedical applications. Annu Rev Anal Chem 11:127–146. https://doi.org/10.1146/annurev-anchem-061417-125956

Wang C, Xia K, Wang H et al (2019) Advanced carbon for flexible and wearable electronics. Adv Mater 31:1801072. https://doi.org/10.1002/adma.201801072

Correia DM, Fernandes LC, Fernandes MM et al (2021) Ionic liquid-based materials for biomedical applications. Nanomaterials 11:2401. https://doi.org/10.3390/nano11092401

Choi DY, Kim MH, Oh YS et al (2017) Highly stretchable, hysteresis-free ionic liquid-based strain sensor for precise human motion monitoring. ACS Appl Mater Interfaces 9:1770–1780. https://doi.org/10.1021/acsami.6b12415

Yamada S, Toshiyoshi H (2020) Temperature sensor with a water-dissolvable ionic gel for ionic skin. ACS Appl Mater Interfaces 12:36449–36457. https://doi.org/10.1021/acsami.0c10229

Zhang H, Lowe A, Kalra A, Yu Y (2021) A flexible strain sensor based on embedded ionic liquid. Sensors 21:5760. https://doi.org/10.3390/s21175760

Yu Z, Wu P (2021) Water-resistant ionogel electrode with tailorable mechanical properties for aquatic ambulatory physiological signal monitoring. Adv Funct Mater 31:2107226. https://doi.org/10.1002/adfm.202107226

Article   MathSciNet   Google Scholar  

Esteves C, Palma SICJ, Costa HMA et al (2022) Tackling humidity with designer ionic liquid-based gas sensing soft materials. Adv Mater 34:2107205. https://doi.org/10.1002/adma.202107205

Curto VF, Fay C, Coyle S et al (2012) Real-time sweat pH monitoring based on a wearable chemical barcode micro-fluidic platform incorporating ionic liquids. Sens Actuators B Chem 171–172:1327–1334. https://doi.org/10.1016/j.snb.2012.06.048

Zandu SK, Chopra H, Singh I (2020) Ionic liquids for therapeutic and drug delivery applications. Curr Drug Res Rev 12:26–41. https://doi.org/10.2174/2589977511666191125103338

Jian M, Wang C, Wang Q et al (2017) Advanced carbon materials for flexible and wearable sensors. Sci China Mater 60:1026–1062. https://doi.org/10.1007/s40843-017-9077-x

Castro KPR, Colombo RNP, Iost RM et al (2023) Low-dimensionality carbon-based biosensors: the new era of emerging technologies in bioanalytical chemistry. Anal Bioanal Chem. https://doi.org/10.1007/s00216-023-04578-x

Das S, Pal M (2020) Review—non-invasive monitoring of human health by exhaled breath analysis: a comprehensive review. J Electrochem Soc 167:037562. https://doi.org/10.1149/1945-7111/ab67a6

Pang J, Bachmatiuk A, Yang F et al (2021) Applications of carbon nanotubes in the internet of things era. Nano-Micro Lett 13:191. https://doi.org/10.1007/s40820-021-00721-4

Yi J, Xianyu Y (2022) Gold nanomaterials-implemented wearable sensors for healthcare applications. Adv Funct Mater 32:2113012. https://doi.org/10.1002/adfm.202113012

Ali I, Chen L, Huang Y et al (2018) Humidity-responsive gold aerogel for real-time monitoring of human breath. Langmuir 34:4908–4913. https://doi.org/10.1021/acs.langmuir.8b00472

Haine AT, Niidome T (2017) Gold nanorods as nanodevices for bioimaging, photothermal therapeutics, and drug delivery. Chem Pharm Bull (Tokyo) 65:625–628. https://doi.org/10.1248/cpb.c17-00102

Jin H, Jin Q, Jian J (2018) Smart materials for wearable healthcare devices. In: Ortiz JH (ed) Wearable technologies. InTech

Google Scholar  

Choo-Smith L-P, Edwards HGM, Endtz HP et al (2002) Medical applications of Raman spectroscopy: from proof of principle to clinical implementation. Biopolymers 67:1–9. https://doi.org/10.1002/bip.10064

Kothari R, Jones V, Mena D et al (2021) Raman spectroscopy and artificial intelligence to predict the Bayesian probability of breast cancer. Sci Rep 11:6482. https://doi.org/10.1038/s41598-021-85758-6

Deore AB, Dhumane JR, Wagh R, Sonawane R (2019) The stages of drug discovery and development process. Asian J Pharm Res Dev 7:62–67. https://doi.org/10.22270/ajprd.v7i6.616

Hughes J, Rees S, Kalindjian S, Philpott K (2011) Principles of early drug discovery: principles of early drug discovery. Br J Pharmacol 162:1239–1249. https://doi.org/10.1111/j.1476-5381.2010.01127.x

Showell GA, Mills JS (2003) Chemistry challenges in lead optimization: silicon isosteres in drug discovery. Drug Discov Today 8:551–556. https://doi.org/10.1016/S1359-6446(03)02726-0

Vemula D, Jayasurya P, Sushmitha V et al (2023) CADD, AI and ML in drug discovery: a comprehensive review. Eur J Pharm Sci 181:106324. https://doi.org/10.1016/j.ejps.2022.106324

Kist R, Timmers LFSM, Caceres RA (2018) Searching for potential mTOR inhibitors: ligand-based drug design, docking and molecular dynamics studies of rapamycin binding site. J Mol Graph Model 80:251–263. https://doi.org/10.1016/j.jmgm.2017.12.015

Aparoy P, Kumar Reddy K, Reddanna P (2012) Structure and ligand based drug design strategies in the development of novel 5- LOX inhibitors. Curr Med Chem 19:3763–3778. https://doi.org/10.2174/092986712801661112

Kanakaveti V, Shanmugam A, Ramakrishnan C et al (2020) Computational approaches for identifying potential inhibitors on targeting protein interactions in drug discovery. In: Advances in protein chemistry and structural biology. Elsevier, pp 25–47

Chikhale RV, Gupta VK, Eldesoky GE et al (2021) Identification of potential anti-TMPRSS2 natural products through homology modelling, virtual screening and molecular dynamics simulation studies. J Biomol Struct Dyn 39:6660–6675. https://doi.org/10.1080/07391102.2020.1798813

Li K, Du Y, Li L, Wei D-Q (2019) Bioinformatics approaches for anti-cancer drug discovery. Curr Drug Targets 21:3–17. https://doi.org/10.2174/1389450120666190923162203

Yu T, Cheng L, Yan X et al (2020) Systems biology approaches based discovery of a small molecule inhibitor targeting both c-Met/PARP-1 and inducing cell death in breast cancer. J Cancer 11:2656–2666. https://doi.org/10.7150/jca.40758

Aldewachi H, Al-Zidan RN, Conner MT, Salman MM (2021) High-throughput screening platforms in the discovery of novel drugs for neurodegenerative diseases. Bioengineering 8:30. https://doi.org/10.3390/bioengineering8020030

Ferdowsian HR, Beck N (2011) Ethical and scientific considerations regarding animal testing and research. PLoS ONE 6:e24059. https://doi.org/10.1371/journal.pone.0024059

Achary PGR (2020) Applications of quantitative structure-activity relationships (QSAR) based virtual screening in drug design: a review. Mini-Rev Med Chem 20:1375–1388. https://doi.org/10.2174/1389557520666200429102334

Staszak M, Staszak K, Wieszczycka K et al (2022) Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship. WIREs Comput Mol Sci. https://doi.org/10.1002/wcms.1568

Raies AB, Bajic VB (2016) In silico toxicology: computational methods for the prediction of chemical toxicity: computational methods for the prediction of chemical toxicity. Wiley Interdiscip Rev Comput Mol Sci 6:147–172. https://doi.org/10.1002/wcms.1240

Workman P (2003) How much gets there and what does it do?: The need for better pharmacokinetic and pharmacodynamic endpoints in contemporary drug discovery and development. Curr Pharm Des 9:891–902. https://doi.org/10.2174/1381612033455279

Bouzom F, Ball K, Perdaems N, Walther B (2012) Physiologically based pharmacokinetic (PBPK) modelling tools: how to fit with our needs?: PBPK MODELLING TOOLS. Biopharm Drug Dispos 33:55–71. https://doi.org/10.1002/bdd.1767

Mahan VL (2014) Clinical trial phases. Int J Clin Med 05:1374–1383. https://doi.org/10.4236/ijcm.2014.521175

Gold M, Amatniek J, Carrillo MC et al (2018) Digital technologies as biomarkers, clinical outcomes assessment, and recruitment tools in Alzheimer’s disease clinical trials. Alzheimers Dement Transl Res Clin Interv 4:234–242. https://doi.org/10.1016/j.trci.2018.04.003

Bennett AV, Jensen RE, Basch E (2012) Electronic patient-reported outcome systems in oncology clinical practice. CA Cancer J Clin 62:336–347. https://doi.org/10.3322/caac.21150

Gong K, Yan Y-L, Li Y et al (2020) Mobile health applications for the management of primary hypertension: a multicenter, randomized, controlled trial. Medicine (Baltimore) 99:e19715. https://doi.org/10.1097/MD.0000000000019715

Gordon S, Crager J, Howry C et al (2022) Best practice recommendations: user acceptance testing for systems designed to collect clinical outcome assessment data electronically. Ther Innov Regul Sci 56:442–453. https://doi.org/10.1007/s43441-021-00363-z

Galsky MD, Shahin M, Jia R et al (2017) Telemedicine-enabled clinical trial of metformin in patients with prostate cancer. JCO Clin Cancer Inform. https://doi.org/10.1200/CCI.17.00044

Beg S, Handa M, Shukla R et al (2022) Wearable smart devices in cancer diagnosis and remote clinical trial monitoring: transforming the healthcare applications. Drug Discov Today 27:103314. https://doi.org/10.1016/j.drudis.2022.06.014

Patel VN, Kaelber DC (2014) Using aggregated, de-identified electronic health record data for multivariate pharmacosurveillance: a case study of azathioprine. J Biomed Inform 52:36–42. https://doi.org/10.1016/j.jbi.2013.10.009

Needamangalam Balaji J, Prakash S, Park Y et al (2022) A scoping review on accentuating the pragmatism in the implication of mobile health (mHealth) technology for tuberculosis management in India. J Pers Med 12:1599. https://doi.org/10.3390/jpm12101599

Holmén C, Piehl F, Hillert J et al (2011) A Swedish national post-marketing surveillance study of natalizumab treatment in multiple sclerosis. Mult Scler J 17:708–719. https://doi.org/10.1177/1352458510394701

Antonijević Z, Beckman RA (2019) Platform trial designs in drug development: umbrella trials and basket trials. CRC Press, Boca Raton

Battaglini D, Al-Husinat L, Normando AG et al (2022) Personalized medicine using omics approaches in acute respiratory distress syndrome to identify biological phenotypes. Respir Res 23:318. https://doi.org/10.1186/s12931-022-02233-0

Poirion OB, Jing Z, Chaudhary K et al (2021) DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data. Genome Med 13:112. https://doi.org/10.1186/s13073-021-00930-x

Tran KA, Kondrashova O, Bradley A et al (2021) Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med 13:152. https://doi.org/10.1186/s13073-021-00968-x

Turek C, Wróbel S, Piwowar M (2020) OmicsON—integration of omics data with molecular networks and statistical procedures. PLoS ONE 15:e0235398. https://doi.org/10.1371/journal.pone.0235398

Galeone C, Scelfo C, Bertolini F et al (2018) Precision medicine in targeted therapies for severe asthma: is there any place for “omics” technology? BioMed Res Int 2018:1–15. https://doi.org/10.1155/2018/4617565

Big data analytics in healthcare market. https://www.factmr.com/report/369/big-data-analytics-healthcare-market

Hemingway H, Asselbergs FW, Danesh J et al (2018) Big data from electronic health records for early and late translational cardiovascular research: challenges and potential. Eur Heart J 39:1481–1495. https://doi.org/10.1093/eurheartj/ehx487

Wang L, Alexander CA (2020) Big data analytics in medical engineering and healthcare: methods, advances and challenges. J Med Eng Technol 44:267–283. https://doi.org/10.1080/03091902.2020.1769758

Koppad S, B A, Gkoutos GV, Acharjee A, (2021) Cloud computing enabled big multi-omics data analytics. Bioinforma Biol Insights 15:1177932221. https://doi.org/10.1177/11779322211035921

Morris MA, Saboury B, Burkett B et al (2018) Reinventing radiology: big data and the future of medical imaging. J Thorac Imaging 33:4–16. https://doi.org/10.1097/RTI.0000000000000311

Ngiam KY, Khor IW (2019) Big data and machine learning algorithms for health-care delivery. Lancet Oncol 20:e262–e273. https://doi.org/10.1016/S1470-2045(19)30149-4

Ho D, Quake SR, McCabe ERB et al (2020) Enabling technologies for personalized and precision medicine. Trends Biotechnol 38:497–518. https://doi.org/10.1016/j.tibtech.2019.12.021

Sadler D, Okwuosa T, Teske AJ et al (2022) Cardio oncology: Digital innovations, precision medicine and health equity. Front Cardiovasc Med 9:951551. https://doi.org/10.3389/fcvm.2022.951551

Vajawat B, Varshney P, Banerjee D (2021) Digital gaming interventions in psychiatry: evidence. Appl Chall Psych Res 295:113585. https://doi.org/10.1016/j.psychres.2020.113585

Raijada D, Wac K, Greisen E et al (2021) Integration of personalized drug delivery systems into digital health. Adv Drug Deliv Rev 176:113857. https://doi.org/10.1016/j.addr.2021.113857

Montanhesi PK, Coelho G, Curcio SAF, Poffo R (2022) Three-dimensional printing in minimally invasive cardiac surgery: optimizing surgical planning and education with life-like models. Braz J Cardiovasc Surg. https://doi.org/10.21470/1678-9741-2020-0409

Jamróz W, Szafraniec J, Kurek M, Jachowicz R (2018) 3D printing in pharmaceutical and medical applications—recent achievements and challenges. Pharm Res 35:176. https://doi.org/10.1007/s11095-018-2454-x

Bedford J, Farrar J, Ihekweazu C et al (2019) A new twenty-first century science for effective epidemic response. Nature 575:130–136. https://doi.org/10.1038/s41586-019-1717-y

Canfell OJ, Davidson K, Woods L et al (2022) Precision public health for non-communicable diseases: an emerging strategic roadmap and multinational use cases. Front Public Health 10:256

Cubillos-Ruiz A, Guo T, Sokolovska A et al (2021) Engineering living therapeutics with synthetic biology. Nat Rev Drug Discov 20:941–960. https://doi.org/10.1038/s41573-021-00285-3

Davies JA (2016) Synthetic biology: rational pathway design for regenerative medicine. Gerontology 62:564–570. https://doi.org/10.1159/000440721

Sridhar S, Ajo-Franklin CM, Masiello CA (2022) A framework for the systematic selection of biosensor chassis for environmental synthetic biology. ACS Synth Biol 11:2909–2916. https://doi.org/10.1021/acssynbio.2c00079

Jain KK (2013) Synthetic biology and personalized medicine. Med Princ Pract 22:209–219. https://doi.org/10.1159/000341794

McNerney MP, Doiron KE, Ng TL et al (2021) Theranostic cells: emerging clinical applications of synthetic biology. Nat Rev Genet 22:730–746. https://doi.org/10.1038/s41576-021-00383-3

Zupanic A, Bernstein HC, Heiland I (2020) Systems biology: current status and challenges. Cell Mol Life Sci 77:379–380. https://doi.org/10.1007/s00018-019-03410-z

Lopatkin AJ, Collins JJ (2020) Predictive biology: modelling, understanding and harnessing microbial complexity. Nat Rev Microbiol 18:507–520. https://doi.org/10.1038/s41579-020-0372-5

Irmisch A, Bonilla X, Chevrier S et al (2021) The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support. Cancer Cell 39:288–293. https://doi.org/10.1016/j.ccell.2021.01.004

McEwen SC, Merrill DA, Bramen J et al (2021) A systems-biology clinical trial of a personalized multimodal lifestyle intervention for early Alzheimer’s disease. Alzheimers Dement Transl Res Clin Interv. https://doi.org/10.1002/trc2.12191

Wolstencroft K, Owen S, Krebs O et al (2015) SEEK: a systems biology data and model management platform. BMC Syst Biol 9:33. https://doi.org/10.1186/s12918-015-0174-y

Kobeissy FH, Guingab-Cagmat JD, Razafsha M et al (2011) Leveraging biomarker platforms and systems biology for rehabilomics and biologics effectiveness research. PM&R 3:S139–S147. https://doi.org/10.1016/j.pmrj.2011.02.012

Brown S-A (2015) Building SuperModels: emerging patient avatars for use in precision and systems medicine. Front Physiol. https://doi.org/10.3389/fphys.2015.00318

Cummins N, Schuller BW (2020) Five crucial challenges in digital health. Front Digit Health 2:536203. https://doi.org/10.3389/fdgth.2020.536203

Board of Governors in Supersession of the Medical Council of India (2020) Telemedicine practice guidelines-enabling registered medical practitioners to provide healthcare using telemedicine

Latifi R, Doarn CR (2020) Perspective on COVID-19: finally, telemedicine at center stage. Telemed E-Health 26:1106–1109. https://doi.org/10.1089/tmj.2020.0132

Gudi N, Lakiang T, Pattanshetty S et al (2021) Challenges and prospects in india’s digital health journey. Indian J Public Health 65:209. https://doi.org/10.4103/ijph.IJPH_1446_20

Dash S, Aarthy R, Mohan V (2021) Telemedicine during COVID-19 in India—a new policy and its challenges. J Public Health Policy 42:501–509. https://doi.org/10.1057/s41271-021-00287-w

Srivastava SK (2016) Adoption of electronic health records: a roadmap for India. Healthc Inform Res 22:261. https://doi.org/10.4258/hir.2016.22.4.261

Madanian S, Parry DT, Airehrour D, Cherrington M (2019) mHealth and big-data integration: promises for healthcare system in India. BMJ Health Care Inform 26:e100071. https://doi.org/10.1136/bmjhci-2019-100071

Al Dahdah M, Mishra RK (2023) Digital health for all: the turn to digitized healthcare in India. Soc Sci Med 319:114968. https://doi.org/10.1016/j.socscimed.2022.114968

Katyayan A, Katyayan A, Mishra A (2022) Enhancing India’s health care during COVID era: role of artificial intelligence and algorithms. Indian J Otolaryngol Head Neck Surg 74:2712–2713. https://doi.org/10.1007/s12070-020-02101-7

National Digital Health Mission. https://www.sanskritiias.com/current-affairs/national-digital-health-mission

Soman B (2014) Participatory GIS in action, a public health initiative from Kerala, India. ISPRS Int Arch Photogramm Remote Sens Spat Inf Sci XL–8:233–237. https://doi.org/10.5194/isprsarchives-XL-8-233-2014

Babu AN, Niehaus E, Shah S et al (2019) Smartphone geospatial apps for dengue control, prevention, prediction, and education: MOSapp, DISapp, and the mosquito perception index (MPI). Environ Monit Assess 191:393. https://doi.org/10.1007/s10661-019-7425-0

Chaudhary S, Soman B (2022) Spatiotemporal analysis of environmental and physiographic factors related to malaria in Bareilly district, India. Osong Public Health Res Perspect 10:10. https://doi.org/10.24171/j.phrp.2021.0304

Singh G, Mitra A, Soman B (2022) Development and use of a reproducible framework for spatiotemporal climatic risk assessment and its association with decadal trend of dengue in India. Indian J Community Med 47:50. https://doi.org/10.4103/ijcm.ijcm_862_21

Valson JS, Soman B (2017) Spatiotemporal clustering of dengue cases in Thiruvananthapuram district. Kerala’ Indian J Public Health 61:74

Sarma PS, Sadanandan R, Thulaseedharan JV et al (2019) Prevalence of risk factors of non-communicable diseases in Kerala, India: results of a cross-sectional study. BMJ Open. https://doi.org/10.1136/bmjopen-2018-027880

Ulahannan SK, Wilson A, Chhetri D et al (2022) Alarming level of severe acute malnutrition in Indian districts. BMJ Glob Health 7:e007798. https://doi.org/10.1136/bmjgh-2021-007798

Valson JS, Kutty VR, Soman B, Jissa VT (2019) Spatial clusters of diabetes and physical inactivity: do neighborhood characteristics in high and low clusters differ? Asia Pac J Public Health. https://doi.org/10.1177/1010539519879322

Mitra A, Soman B, Singh G (2021) An interactive dashboard for real-time analytics and monitoring of COVID-19 outbreak in india: a proof of Concept. arXiv:210809937Cs

Mitra A, Soman B, Gaitonde R et al (2023) Data science approaches to public health: case studies using routine health data from India

Singh G, Patrikar S, Sarma PS, Soman B (2020) Time-dependent dynamic transmission potential and instantaneous reproduction number of COVID-19 pandemic in India. medRxiv. https://doi.org/10.1101/2020.07.15.20154971

Singh G, Srinivas G, Jyothi EK et al (2020) Containing the first outbreak of COVID-19 in a healthcare setting in India: the sree chitra experience. Indian J Public Health 64:240. https://doi.org/10.4103/ijph.IJPH_483_20

Singh G, Soman B (2021) Spatiotemporal epidemiology and forecasting of dengue in the state of Punjab, India: study protocol. Spat Temp Epidemiol 39:100444. https://doi.org/10.1016/j.sste.2021.100444

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Naresh Kasoju, N. S. Remya, Renjith Sasi, S. Sujesh, Biju Soman, C. Kesavadas, C. V. Muraleedharan, P. R. Harikrishna Varma & Sanjay Behari

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Kasoju, N., Remya, N.S., Sasi, R. et al. Digital health: trends, opportunities and challenges in medical devices, pharma and bio-technology. CSIT 11 , 11–30 (2023). https://doi.org/10.1007/s40012-023-00380-3

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Telehealth and digital health innovations: A mixed landscape of access

Roles Conceptualization, Visualization, Writing – original draft, Writing – review & editing

Affiliation UW Medicine Research Information Technologies, University of Washington, Seattle, Washington, United States of America

Affiliation Department of Information Systems, University of Maryland Baltimore County, Baltimore, Maryland, United States of America

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Affiliation Paul G. Allen School of Computer Science, University of Washington, Seattle, Washington, United States of America

Affiliation The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America

Affiliation Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America

Roles Conceptualization, Writing – original draft, Writing – review & editing

Affiliations Paul G. Allen School of Computer Science, University of Washington, Seattle, Washington, United States of America, Taskar Center for Accessible Technology, Seattle, Washington, United States of America

Affiliations Johns Hopkins Disability Health Research Center, Baltimore, Maryland, United States of America, Johns Hopkins School of Nursing, Baltimore, Maryland, United States of America, Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America

Affiliation Meharry Medical College School of Medicine, Nashville, Tennessee, United States of America

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• Jimmy Phuong, 
• Patricia Ordóñez, 
• Jerry Cao, 
• Mira Moukheiber, 
• Lama Moukheiber, 
• Anat Caspi, 
• Bonnielin K. Swenor, 
• David Kojo N. Naawu, 
• Jennifer Mankoff

Published: December 15, 2023

• https://doi.org/10.1371/journal.pdig.0000401
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In the wake of emergent natural and anthropogenic disasters, telehealth presents opportunities to improve access to healthcare when physical access is not possible. Yet, since the beginning of the COVID pandemic, lessons learned reveal that various populations in the United States do not or cannot adopt telehealth due to inequitable access. We explored the Digital Determinants of Health (DDoHs) for telehealth, characterizing the role of accessibility, broadband connectivity and electrical grids, and patient intersectionality. In addition to its role as an existing Social Determinant of Health, Policies and Laws directly and indirectly affect these DDoHs, making access more complex for marginalized populations. Digital systems lack the flexibility, accessibility, and usability to inclusively provide the essential services patients need in telehealth. We propose the following recommendations: (1) design technology and systems using accessibility and value sensitive design principles; (2) support a range of technologies and settings; (3) support multiple and diverse users; and (4) support clear paths for repair when technical systems fail to meet users’ needs. Addressing these requires change not only from providers but also from the institutions providing these systems.

Citation: Phuong J, Ordóñez P, Cao J, Moukheiber M, Moukheiber L, Caspi A, et al. (2023) Telehealth and digital health innovations: A mixed landscape of access. PLOS Digit Health 2(12): e0000401. https://doi.org/10.1371/journal.pdig.0000401

Editor: Khumbo Kalua, Kamuzu University of Health Sciences: University of Malawi College of Medicine, MALAWI

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Funding: This work was partially supported by the National Institutes of Health (NIH/NCATS U24TR002306 and U24TR002306-04S3 to JP; NIH/NIBIB R01EB030362 to LM) and support from the University of Washington Center for Research and Education on Accessible Technology and Experiences (CREATE) (support provided to JM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction to telehealth

Telehealth, the modalities for remote didactic communication and healthcare access with healthcare providers [ 1 ], is undergoing a revolution to improve its reach and utility in healthcare. Promoted from the Affordable Care Act of 2008, telehealth is an umbrella term referring to analog and audio-video out-of-office visits as alternatives to in-person healthcare [ 1 , 2 ]. Telehealth includes Telemedicine and a variety of non-physician services (e.g., telenursing, telepharmacy, and linguistic interpretation) and can be discussed synonymously with integrated remote care modalities, such as mobile health and E-health platforms [ 1 ].

The need and implementation of telehealth services escalated with the Coronavirus Disease 2019 (COVID-19) pandemic [ 2 – 5 ], yet the results thus far illustrated both its potential to radically increase healthcare access and how it can disenfranchise certain groups. Apart from analog telecommunication, telehealth relies on remote meeting software and broadband infrastructure to facilitate communication [ 2 , 3 ]. Areas with unstable, unreliable electric and telecommunication services would be prone to connectivity issues. Overtime, repeated lag in device ownership and innovation diffusion contributes to the cultural digital divide, though these disproportionate health disparities originate from infrastructural factors and the lack of equitable tools designed for patients of diverse needs and care team settings [ 4 ].

In the following sections, we discuss the benefits and risks of telehealth, highlighting structural and intersectional factors that contribute to telehealth adoption. We aimed to understand the landscape of telehealth implementation, considerations for accessibility design to include patients with different functional disabilities, and the relationships with critical concepts such as digital divide and Social Determinants of Health (SDoHs).

The benefits of telehealth

While telehealth has many purported benefits, we highlight 3 major thematic advantages for telehealth. First, telehealth can improve healthcare access and reach to those who otherwise live in “healthcare deserts,” where in-person access to care requires physical travel and time expenditures that may be economically prohibitive [ 4 , 5 ]. Second, telehealth can improve healthcare access options for people with disabilities by reducing travel for medical visits [ 6 , 7 ]. Approximate 27% of US adults have a functional disability and face barriers accessing healthcare [ 8 ]. Telehealth circumvents mobility barriers related to travel-time and wait-times, allowing for improved engagement for healthcare [ 2 ]. Third, telehealth video conferencing may mask in-person characteristics that invoke provider bias. For example, people of over-average weight reported experiencing discriminating biases during healthcare encounters, though this phenomena may be shared and intersectional among multiple subgroup identities [ 9 ]. Over video, a healthcare provider may be less likely to respond negatively to certain identity characteristics.

The risks of telehealth

In contrast, telehealth may increase the risk of unequal access to care as it may ossify existing disparities [ 2 , 7 ]. Those who reside in “healthcare deserts” and experience unreliable broadband connectivity may not be able to access telehealth as an alternative to in-person healthcare. Healthcare access can be made worse when certain healthcare services with staff shortages shift towards online instead of in-person care, a concern with mental healthcare and counseling [ 10 , 11 ]. Moreover, telehealth systems may be predominantly designed for subpopulations who experience few barriers to adoption, excluding the needs of those who lack technology literacy, have unmet visual accessibility needs, or do not have the financial capabilities to pay for services [ 4 , 7 , 12 ].

Gradually, telehealth may incorporate complementary technologies, supporting measurements and diagnostic data collection at-home [ 6 , 7 , 13 ]. The lack of complementary and accessible in-home technology support may limit the benefits of healthcare services available. Even if a video call is accessible to a blind person, the telehealth experience may be limited if there are no accessible option to take measurements. However, paired technologies may fail to account for diverse users, such as a wheelchair user whose low step count does not reflect their actual activity level. These systems must be compliant with the Americans with Disabilities Act of 1990 (ADA) guidelines, not only for default settings but also settings allowing for personalization [ 14 ].

Telehealth may obscure or enhance visible presentations [ 15 ]. Video conferencing may offer a deeper window into the patient’s home environment and accidentally disclose private information more than the patient would have desired. Alternatively, visibility of symptoms and functional disabilities may be obscured, which can influence provider diagnostic coding and response for undiagnosed disabilities. A person with a chronic illness may present fatigued in-person, but video conference may obscure the fatigue, leading to the provider not taking symptoms seriously, which increases likelihood for misdiagnosis or delayed actions. Increased risk of severe COVID-19 and hospitalization actions varies depending on types of functional disabilities [ 16 ], in which visibility of symptoms and presentations can present a challenge in patient–provider decision-making.

Digital determinants of health

Akin to SDoHs, Digital Determinants of Health (DDoHs) highlight contemporary constructs about digital health innovations with significance towards healthcare equity. DDoHs highlight conceptually distinct facilitators and barriers from SDoHs for the diffusion of medical and public health digital innovation into the general populace, or the lack thereof when considering the effects of a digital divide.

Pre-pandemic, the World Health Organization defined Digital Literacy as “literacy in information and communication technologies and access to equipment, broadband and the internet” [ 17 ]. Since the onset of the COVID-19 pandemic, telehealth played a key role for emergency triage, access to medical professionals, medical follow-up, and the processes for referrals to in-person care, while abiding with social distancing recommendations [ 18 ]. Yet, the pandemic has also demonstrated how telehealth may not be a pragmatic option for certain patients or providers, serving as an example for implementing and adoption of many other digital innovations. While digital literacy may explain some of the nuances facilitating or barring telehealth technological adoption, recent studies have identified critical drivers of digital divide, factors which we refer to as DDoHs [ 4 , 17 ]. These DDoHs can be observed with telehealth adoption to further increase disparities in access to healthcare in marginalized communities.

The ability for healthcare systems to adopt telehealth is contingent on a myriad of factors ( Fig 1 ) [ 1 , 19 , 20 ], including broadband infrastructure and its reliance on the electrical grid. In that sense, the landscape geography and localities with stable broadband infrastructure can explain the resources available to key actors. Yet, for patients who experienced the intersection of unmet social needs, resource barriers, and different forms of discrimination, experiences with inequity can reduce ones’ willingness to engage in such technologies. Principles of Accessibility facilitate reach to marginalized segments of the population who experience diverse variations in human ability [ 3 , 14 ]. Value sensitive design is one such approach for designing solutions that incorporate the stakeholders, identifying values alignments and value conflicts, and envisioned scenarios within the design process [ 21 ]. We note that “Policy and Laws” can directly legislate access to care as a healthcare options, but it can also be viewed as an indirect influence upon telehealth by way of the digital determinants of health (i.e., usability and accessibility design, broadband infrastructure investments, and maintenance of public utilities). In the following sections, we elaborate on how these DDoHs influence telehealth.

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“Policies and laws” are SDoHs for access to telehealth care. “Policies and laws” intervene with the status quo and influence DDoHs for telehealth care; therefore, it can be considered a DDoH of telehealth.

https://doi.org/10.1371/journal.pdig.0000401.g001

Broadband infrastructure

For adoption of telehealth and other digital innovations to be facilitated, patients, healthcare providers, and health systems must adapt to adopt these technologies as part of their healthcare workflows [ 19 , 22 ]. Access to many digital health innovations is contingent on access to hardware components and reliable broadband connectivity [ 4 , 22 – 25 ]. Analog options may be preferred as feasible methods for continuation of core services in lieu of reliable broadband connectivity infrastructure or its dependence on the electrical grid [ 26 ]. In more rural areas, broadband internet may be unaffordable even if it were regionally available, leaving people behind in technological literacy and the digital divide [ 4 , 25 ]. Broadband infrastructure requires investments to maintain and update hardware and security, data access contracts, and various other costs felt by individuals and regional governance. Due to distance to care and unresolved internet connectivity, telehealth services may continue to be out of reach for many minority, rural, and low socioeconomic communities from telehealth services.

Reports indicate that telehealth adoption in response to the COVID-19 public health emergency has met various social, practical, and accessibility of implementation challenges [ 2 , 5 , 18 , 23 ]. Pilot implementations of telehealth found that providing tablets alone could not overcome connection rate hurdles, requiring in-home hardware installations to strengthen broadband connectivity [ 1 , 2 , 22 ]. The highest rate of adoption were among Medicare and Medicaid recipients [ 2 ]. Specifically, telehealth enables continued access to care for most but has done little for the uninsured young adult populations who experience barriers in access to care and the highest rates of emotional distress from COVID-19 [ 2 , 27 ].

Accessible electrical grid after natural disasters

Natural disaster events destabilize and deteriorate electrical grid infrastructure and services that depend on them [ 28 , 29 ]. Climate-related disruptions to the electrical grids in California and Texas put patients who were reliant on electrical life support at risk [ 24 – 26 ]. Unreliable electrical sources can impact medication refrigeration and complicate chronic care management. In Puerto Rico, Hurricanes Irma and María in 2017 wiped out the electrical grid for over a year for the majority of the island, creating the longest blackout in the history of the Americas [ 30 ]. Delays in action to modernize the electrical grid allowed power outages to continue weekly across the island, leaving Puerto Ricans juggling with challenges at work, at school, and in managing health and wellness [ 30 ]. Downed electrical grid can have a direct impact on care management and disrupt access to telehealth and emergency response for broad geographic areas.

With modernization of the electrical grid and broadband infrastructure, increased availability may not translate to increased accessibility. Where critical infrastructure is unreliable or not cost-effective, it can be a source of digital health inequity. For some, newly built electrical grids may be available but financially unaffordable except to private entities and commercial services [ 4 , 31 ]. Mountainous terrains or flood-prone areas are not conducive to building sustainable infrastructure and often get low priority for development. In such circumstances, reliance on analog options persists and adoption of telehealth, other digital innovations, and technological literacy falters.

Capacity building for telehealth: A systems engineering approach

Medical systems are already complex, involving a highly connected system of people, resources, processes, and institutions. Telehealth is an attempt to improve care, but also involves disruption to the existing systems [ 18 , 19 , 23 ], with the potential for wide-ranging positive and negative consequences. As an improvement methodology, we should build capacity through every discrete part of the system (i.e., people, resources, processes, and institutionally) to properly equip and better manage both complexity and risk, understanding and accommodating patient needs for healthcare accessibility and understanding patient intersections ( Fig 2 ). Patients may have one or more functional disabilities, which can change their healthcare utilization over time. Social risk factors are intersectional and dynamic, changing what tools and resources are needed as one progresses in life and whether those needs are responsively and equitably met, meriting that social risk factors and functional abilities/disabilities are supportively inquired. Taking a holistic engineering systems approach may be beneficial, to avoid focusing on discrete interventions whose effects are narrowly monitored, particularly when they are used to address the needs of small subpopulations. Engineers have long understood that complex problems require a systems view and that attempts to make things better can themselves introduce new risks into a system. In the paragraphs below, we take an engineering systems approach to identify the capacity-building requirements needed to allow telehealth to improve healthcare for all patients.

https://doi.org/10.1371/journal.pdig.0000401.g002

Barriers in the patient–provider clinical workflow

Telehealth interactions, and health interactions in general, increasingly depend on technology to support all aspects of the process, from communicating with doctors to scheduling appointments and everything in between. Digital systems do not only impact the telehealth experience; there is significant overlap in all efforts before and after telehealth and in-person visits. However, digital systems, such as electronic health records and wearable monitoring devices, are used even during in-person appointments. They have become so ubiquitous as to frequently be unavoidable requirements for access to care. The United States has observed a shortage of mental health provider, where COVID has seen mental healthcare visits shift overwhelmingly towards telehealth visits [ 10 , 11 , 32 ]. Wherever possible, the shift towards telehealth can reach more patients, but the change makes digital literacy a requirement to receive care.

Throughout this transition towards telehealth, digital literacy and understanding about security and privacy are assumed. Patients often have to learn to interact with entirely new systems via email notifications, sometimes a different system for each clinician they see. In a study that compared healthcare experiences of those with and without disabilities, individuals with disabilities encountered unique challenges that were specific to their disability [ 33 ]. While not all participants in the study reported a lack of standard of care, some participants reported on how they felt that their symptoms were interrelated and that no one physician could treat all of their symptoms [ 33 ]. Despite the use of electronic health records (EHRs), patients felt that inter-provider communication was poor, even among those within the same network [ 33 ]. Further, many of these systems are inaccessible to people with disabilities, and disability-specific systems may not be familiar to clinicians [ 34 ]. Consent becomes perfunctory, less meaningful for every new account with a new software application downloaded. Patients rarely have control over these aspects of patient experience, and a paucity of these experiences are documented.

Unfamiliar and inaccessible digital systems

We defined digital systems to be inclusive of, but are not limited to, EHRs, patient health records, telehealth services, and wearable devices like glucose monitors. Most applications of telehealth now use custom, siloed messaging systems that protect privacy by keeping all communication within the system, though data retention policies may hinder patient access to information and private correspondences [ 35 ]. A patient may be required to log into a myriad of different websites, often with little guidance to establish and maintain these accounts. The awareness and preservation of documents related to these separate systems can be imperative for reporting legal discourse, reimbursement, determining coverage, and continued access to services. This can be further compounded by state-to-state legal requirements around patients’ rights, such as who can see an adolescent’s medical records.

Telehealth may reduce visibility or presentation of symptom severity over a video camera than in-person. The visibility affects provider belief in what patients self-report and, ultimately, on what gets actioned upon [ 36 ]. It is a matter of digital literacy to make sure that the systems do not expose too much and that the patients can retain the information they need.

While automation is typically viewed as a positive, automation that has built assumptions about “normal” abilities further perpetuates ableism [ 37 ]. For example, facial recognition software are increasingly used in medical care settings, though with less success among older adults with dementia [ 38 ]. Proprietary algorithms may be protected by intellectual property laws, obfuscating the reasoning for automated responses. Where automated algorithms are used to make determinations, patients and providers may have no recourse to understand or change the output.

These custom systems are rarely designed using Principles of Accessibility to accommodate the range of user needs. Even for someone with high digital literacy, they can be challenging to learn and navigate [ 7 , 39 ]. The system may be inaccessible for someone who only has a tablet or phone, not a desktop computer [ 6 ]. Similarly, support for screen reader use may be entirely lacking [ibid.]. Recent changes in California’s automating billing procedures for In-Home Supported Services require navigating inaccessible phone or online AI verification procedures, which can have built-in assumptions about how fast someone can respond, what accent they have, or other biometric characteristics [ 7 , 37 , 40 ]. Without direct testing with disabled users, the generated system may be compliant with web accessibility guidelines and laws but may be essentially unusable.

Impact of digital technologies on consent

The increasingly digital nature of the telehealth (and health) experience impacts the consent process. Society as a whole has abdicated the consent process to lawyers as the number of lines of text and technical jargon one must read to consent to digital life properly has exceeded what is reasonable [ 41 – 43 ]. Whether in-person or digitally signed, it is a common experience to skip over consent details, undermining the importance, visibility, and patient literacy for a meaningful consenting process [ 15 , 41 , 43 ].

Similarly, in telehealth, patients often treat consent as something to get past—a long scrolling window of text is rarely read but just signed. Consent is elided by the temporal gap between when a system is implemented and when it is used. Thus, if a programmer encodes gender as a binary or formats a hospital bracelet to prominently show a patient’s legal name even when they have communicated their consent and preferences, the programmed encodings are structurally embedded.

Complementary technologies

We often consider telehealth as a live video chat experience, perhaps complemented by interactive EHR systems such as MyChart. However, in practice, telehealth is gradually incorporating mobile, virtual, and wearable devices and sensors (e.g., vital signs, physical activity), continuous glucose monitors (e.g., blood insulin levels), and other technologies to support chronic care management and integrative therapies [ 13 , 23 ]. Sometimes, these are used by patients alone, sometimes reported verbally or digitally to healthcare providers, and sometimes to third parties such as when third-party surveillance is required for insurance to pay for a technology.

While telehealth inequities may exist for people with disabilities, there are critical data gaps that limit our ability to identify and address these disparities. Currently, disability status is not routinely collected as a core demographic element in EHRs, although some studies sought to understand the gaps in healthcare processes. This lack of information makes it impossible to either address telehealth gaps for people with disabilities or to meet the healthcare accessibility needs of patients [ 1 , 7 , 44 , 45 ].

Home medical test usability are perhaps the most recent publicly discussed example of inaccessible home telehealth technologies [ 7 ]. Physical interfaces and apps provided with these technologies may be partly or wholly accessible to people with disabilities. In addition, they may encode hidden assumptions, such as not allowing for certain heights above a certain age, excluding people with dwarfism [ 46 ]. They demarcate “what it means to be a legible human and whose bodies, actions, and lives fall outside… [and] remapping and calcifying the boundaries of inclusion and marginalization [ 37 ]. Again, these decisions are often made by programmers and challenging to change.

Home monitoring apps and devices such as Fitbit, Strava, and Continuous Glucose Monitors are another important example of inaccessible telehealth technologies. For example, Fitbits may not count exercise in a wheelchair as fitness time [ 46 ], encoding activity assumptions about what behaviors count. Furthermore, when digital health technologies are introduced, they often fail to reach the audience who would benefit from them the most due to limited awareness, resource or infrastructure constraints to support the technologies, and perceived benefits to adopt and continue use.

Surveillance is often built into these technologies. Automated tracking of use has been deployed with continuous positive airway pressure (CPAP) machines [ 47 ] and to track the use of prosthetic legs [ 37 ]. Such surveillance is used to design who is “compliant enough” to deserve continued device use, but the information collected lacks nuance of the circumstances and may encode infrastructural biases [ 40 ]. These systems may make assumptions about reliable network connectivity or that a person does not have interacting disabilities.

On a positive note, the ability to create custom solutions for health monitoring and in-home treatments are increasing with the availability of smartphones. Smartphone applications are now a powerful tool that can noninvasively monitor important vital signs, including respiration rate, blood oxygenation, variations in blood pressure, or medical conditions such as anemia, jaundice, or sleep apnea. With ease in collecting this health information, patients can better personalize their care with their healthcare providers. The advent of 3D printing has also allowed for custom attachments to the phone that opens the door to other screening and treatment processes [ 48 – 50 ]. A study has shown the feasibility of using a 3D-printed attachment to the smartphone to perform blood clot testing [ 49 ]. However, these technologies still must consider accessibility and control for people with disabilities and chronic health conditions. Given the long history of in-home health hacks that are part of disability communities [ 7 , 51 ], it should be a given that the specification of such technologies are supported not just for but by patients themselves.

Conclusions

To summarize, digital systems lack the flexibility, accessibility, and usability to inclusively provide the essential services patients need. This impacts not only access but also quality of care and consent. Addressing this requires change not only from providers but also from the companies providing these systems. We propose the following recommendations:

1. Design technology and systems using accessibility and value sensitive design principles

User interfaces must be intuitive, prioritizing understandability of icons and text, as well as streamlined navigation. Further, they must support best practices for user-centered design to reduce overall physical and cognitive burden and increase understandability to those with reduced digital literacy. This is especially important for consent, which must be accessible and visible to patients to be compliant with ADA guidelines [ 7 ]. They must consistently implement standards and plug-in solutions to enable sign language, closed captioning, or the appropriate interpretation on-screen as the services being provided, even for unscheduled appointments.

Usability design approaches, such as value sensitive design [ 21 ], require an iterative process that is responsive to users. This includes developing methods to capture user feedback from patients and providers, then creating systems to improve the technology based on this feedback.

2. Support a range of technologies and settings

We must support compatibility with mobile devices and tablets so that those without access to desktops and laptops can still use services. Moreover, we must support compatibility with external assistive technology and accessibility features native to all operating systems, such as sensing technologies and other complementary technologies. They must support multiple available modes of communication to allow patients to select the mode that is most accessible to them (e.g., ability to send voice-based messages through patient portals and text-based messages during a telemedicine encounter). Finally, applications of privacy preserving technologies, such as video background blurring and access via headphones, would preserve patient privacy and enable alternative options for use across the range of realistic settings.

3. Support multiple and diverse users

Full support for diverse users requires enabling multiple users of the same account through proxy status as well as the ability for multiple individuals to join a telemedicine encounter, if more than one type of assistance is required, like with a qualified sign language interpreter and a family member. Similarly, it is important to support preferred names and pronouns. Support for this must extend beyond the technology. Providers–organizations must train for providers to help them to overcome biases that are more likely to be expressed in telehealth settings.

4. Support clear paths for repair when technical systems fail to meet user needs

We must collect data to determine the impact of these systems and technology, including for groups that experience healthcare inequities. Further, we must provide transparency and control over IT decisions and algorithms, including support for preferred names and pronouns, interpretable machine learning, information about why decisions are made, and legal recourse for understanding and modifying decisions.

This burden should not solely be on the shoulders of users. We must create systems of accountability to ensure that the data make actual changes to technology and close gaps for oppressed/marginalized groups. We must use the information learned from this process to educate and enhance the next generation of developers/designers and technologies.

Policies and laws have been identified as a social detriment of health and have the potential to address the determinants to digital health. Policies and laws can create pathways for people from vulnerable populations whose healthcare would be significantly improved through telehealth. Thus, policies and laws are both a social and digital determinant to health.

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• 2. Karimi M, Lee EC, Couture SJ, Gonzales A, Grigorescu V, Smith SR, et al. National survey trends in telehealth use in 2021: disparities in utilization and audio vs. video services. US Department of Health Human Services. 2022.
• 6. Valdez RS, Lemke MR, Smith GR, Wellbeloved-Stone CA. Accessibility and Beyond: Designing Consumer Health IT for Individuals with Disabilities. Proceedings for the Home Healthcare, Hospice, and Information Technology Conference. Chicago, IL: Maryland Health Information Technology LLC; 2016. pp. 46–47. Available from: https://h3it.org/_static/assets/docs/proceedings/H3IT-2016-Proceedings.pdf#page=15 .
• 14. Section 508 of the Rehabilitation Act of 1973. Sect. 508, 29 U.S.C § 794 (d). Available from: https://www.section508.gov/manage/laws-and-policies/ .
• 21. Friedman B, Kahn PH, Borning A, Huldtgren A. Value sensitive design and information systems. Early engagement and new technologies: Opening up the laboratory Philosophy of Engineering and Technology,. Dordrecht: Springer; 2013. pp. 55–95. https://doi.org/10.1007/978-94-007-7844-3_4

Digital health, as a specialty in medicine and other health professions, applies information and communications technologies to manage illnesses and health risks and to promote wellness. Digital health has a broad scope, which includes the use of wearable devices, mobile devices, telehealth and telemedicine, health information technology, and big data efforts. These studies have been selected as examples from the NIH Social, Behavioral and Economic Impacts of COVID-19 initiative catalog of projects; the full set of projects can be viewed under  PAR-20-043

These projects use digital healthcare interventions to address the secondary health effects related to social, behavioral, and economic impact of COVID-19. They focus on the role and impact of digital health interventions to address access, delivery, effectiveness, scalability, and sustainability of health assessments and interventions for secondary effects (e.g., behavioral health or self-management of chronic conditions) that are utilized during the pandemic. This is particularly important for populations who experience health disparities which have been exacerbated by the pandemic.

Bridging Gaps in Healthcare Services for New Families Due to COVID-19

Family-focused vs. Drinker-focused Smartphone Interventions to Reduce Drinking-related Consequences of COVID-19

Telehealth 2.0: Evaluating Effectiveness and Engagement Strategies for Asynchronous Texting-based Trauma Focused Therapy for PTSD

Autonomous AI to Mitigate Disparities for Diabetic Retinopathy Screening in Youth During and After COVID-19

Assessing the Effectiveness of a Digital Platform to Support the Mental Health of Healthcare Workers in the Response and Recovery Phases of COVID-19

mHealth Mindfulness Intervention for Pregnant Black and Latina Women at Risk of Postpartum Depression

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Digital Healthcare Equity

• A Practical Guide for Implementing the Digital Healthcare Equity Framework

The Agency for Healthcare Research and Quality (AHRQ) developed an Evidence- and Consensus-Based Digital Healthcare Equity Framework and a Practical Implementation Guide to help organizations intentionally consider equity in the development and use of digital healthcare technologies and solutions.

The Guide, which is based on the Framework, serves as a resource to digital healthcare developers and vendors, and healthcare systems, clinical providers, and payers and includes checklist of steps and real-world examples for how to advance equity across phases of the Digital Healthcare Lifecycle. Regardless of the size of an organization or the complexity of the solution this expert Guide can help.

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Future Direction: Digital Health

Selected examples of progress.

Establishing Cancer-focused Telehealth Research Centers of Excellence

In the United States, there has been a substantial increase in telehealth use in recent years. Research has shown that telehealth can improve healthcare access and quality, patient-provider communication, and health outcomes. Importantly, many aspects of cancer care can be delivered through telehealth, such as the promotion of positive health behaviors and cancer screening, remote patient monitoring and management of symptoms during cancer treatment, and virtual survivorship follow-up care. In response, DCCPS, supported by the Cancer Moonshot, launched the Telehealth Research Centers of Excellence (TRACE) initiative to ensure cutting-edge findings are quickly adopted into effective and equitable practice.

Over the next 5 years, four centers will focus on improving people’s lives by

• Rapidly developing an evidence base of telehealth approaches to cancer care, spanning prevention to survivorship 
• Identifying and addressing disparities in access to and use of telehealth services for cancer-related care
• Fostering innovations to improve cancer care delivery using new tools, research methods, and technologies
• Evaluating the changing policy, payment, and communication environments and their impact on the delivery of telehealth for cancer care 

Digital Health as a Solution for Patient-Provider Communication

DCCPS continues to diversify the digital health funding landscape through the use of different NCI funding mechanisms. In spring 2023, DCCPS solicited administrative supplements via NOT-CA-23-041 to better understand the effects of digital health tools and interventions on patient-provider communication across the cancer control continuum , with the aim of developing an evidence base to inform future development, modification, and delivery of digital tools/intervention for effective cancer prevention and control. The NOSI received a robust response, with 12 funded projects focused on leveraging diverse digital tools and interventions, including remote symptom monitoring, electronic medical records, mobile applications, and artificial intelligence (AI) to improve patient-provider communication in areas such as cancer survivorship and lung cancer screening.

Exploring Emerging Technology with Government-wide Support

DCCPS is partnering within NCI and across NIH and the federal government to contribute to more than 15 funding opportunities focused on digital health and evolving technologies. For example, as a partner in the NIH-National Science Foundation (NSF) Smart Health initiative , DCCPS aims to accelerate the development and use of innovative approaches that partner technology and data science-based solutions with biomedical and behavioral health research. The program supports high-risk, high-reward research focused on improving fundamental understanding of biomedical and behavioral health-related processes across a variety of areas, including information science, data science, technology, health disparities, behavior, sensors, imaging, and engineering. DCCPS is also currently soliciting applications via NOT-CA-22-037 to validate digital health tools and AI technologies that are currently or have the potential to be adopted and implemented in real-world settings across the cancer control continuum .

Leveraging Collaboration to Move the Digital Health Needle

Digital health innovation requires collaboration throughout the government. DCCPS program staff lead and participate in the Digital Health R&D Interagency Working Group , which aims to improve the health of Americans by advancing digital health technologies that support personalized health screening, monitoring, diagnosis, and treatment.

Additionally, DCCPS staff collaborated with the Office of the National Coordinator for Health Information Technology (ONC) to contribute subject matter expertise to the United States Core Data for Interoperability Version 4 (USCDI v4), published in July 2023 (PDF, 0.5MB) , which included physical activity data elements as Core Measures (see page 16 of PDF), alongside other new data elements that focus on improving equity across the healthcare ecosystem. Finally, DCCPS program staff also lead and participate in the NIH Telehealth Interest Group, a collective of institutes and centers dedicated to promoting telehealth research and practice across NIH.

Boosting Innovation in the Fight Against Cancer

Announced by the Cancer Moonshot SM in February 2023, CancerX is a public-private partnership to boost innovation in the fight against cancer. With a mission to identify, support, grow, and implement world-class digital solutions to reduce the burden of cancer for all people, CancerX aligns with the goals set forth in the National Cancer Plan and DCCPS’s mission.

The inaugural project is developing evidence, best practices, and a toolkit focused on improving equity and reducing financial toxicity in cancer care and research through digital health technologies. Building on this work, CancerX will be launching a demonstration project in 2024 that will combine the implementation and evaluation of a digitally enabled cancer care model with the development of an associated alternative payment model to combine clinical decision support, virtual-first care, and navigation programs at scale to improve patient access and reduce financial toxicity. DCCPS staff represent NCI as inaugural members on this groundbreaking initiative to champion high-quality innovation in cancer care, address methodological gaps where they exist, and define best practices for successful and equitable implementation. 

Planning for the Future

Digital health plays a pivotal role in the future of cancer control research, enabling integration, analysis, and interpretation of patient data. As we think toward the future, it is important to understand how digital health approaches can advance the assessment, monitoring, and understanding of multilevel cancer risk factors and determinants; increase participant reach and engagement in clinical, behavioral, and epidemiological cancer research; and improve the delivery of cancer-related care.

In addition to identifying and addressing multilevel barriers to equitable access to, engagement with, and use of digital health technologies across constituent groups and cancer-related settings, we must also address the need for training and workforce development to foster the dissemination and adoption of digital health research and technology in cancer control.

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AI improves accuracy of skin cancer diagnoses in Stanford Medicine-led study

Artificial intelligence algorithms powered by deep learning improve skin cancer diagnostic accuracy for doctors, nurse practitioners and medical students in a study led by the Stanford Center for Digital Health.

April 11, 2024 - By Krista Conger

Artificial intelligence helped clinicians diagnose skin cancer more accurately, a Stanford Medicine-led study found. Chanelle Malambo/peopleimages.com   -  stock.adobe.com

A new study led by researchers at Stanford Medicine finds that computer algorithms powered by artificial intelligence based on deep learning can help health care practitioners to diagnose skin cancers more accurately. Even dermatologists benefit from AI guidance, although their improvement is less than that seen for non-dermatologists.

“This is a clear demonstration of how AI can be used in collaboration with a physician to improve patient care,” said professor of dermatology and of epidemiology Eleni Linos , MD. Linos leads the Stanford Center for Digital Health , which was launched to tackle some of the most pressing research questions at the intersection of technology and health by promoting collaboration between engineering, computer science, medicine and the humanities.

Linos, associate dean of research and the Ben Davenport and Lucy Zhang Professor in Medicine, is the senior author of the study , which was published on April 9 in npj Digital Medicine . Postdoctoral scholar Jiyeong Kim , PhD, and visiting researcher Isabelle Krakowski, MD, are the lead authors of the research.

“Previous studies have focused on how AI performs when compared with physicians,” Kim said. “Our study compared physicians working without AI assistance with physicians using AI when diagnosing skin cancers.”

AI algorithms are increasingly used in clinical settings, including dermatology. They are created by feeding a computer hundreds of thousands or even millions of images of skin conditions labeled with information such as diagnosis and patient outcome. Through a process called deep learning, the computer eventually learns to recognize telltale patterns in the images that correlate with specific skin diseases including cancers. Once trained, an algorithm written by the computer can be used to suggest possible diagnoses based on an image of a patient’s skin that it has not been exposed to.

Eleni Linos

These diagnostic algorithms aren’t used alone, however. They are overseen by clinicians who also assess the patient, come to their own conclusions about a patient’s diagnosis and choose whether to accept the algorithm’s suggestion.

An accuracy boost

Kim and Linos’ team reviewed 12 studies detailing more than 67,000 evaluations of potential skin cancers by a variety of practitioners with and without AI assistance. They found that, overall, health care practitioners working without aid from artificial intelligence were able to accurately diagnose about 75% of people with skin cancer — a statistical measurement known as sensitivity. Conversely, the workers correctly diagnosed about 81.5% of people with cancer-like skin conditions but who did not have cancer — a companion measurement known as specificity.

Health care practitiones who used AI to guide their diagnoses did better. Their diagnoses were about 81.1% sensitive and 86.1% specific. The improvement may seem small, but the differences are critical for people told they don’t have cancer, but do, or for those who do have cancer but are told they are healthy.

When the researchers split the health care practitioners by specialty or level of training, they saw that medical students, nurse practitioners and primary care doctors benefited the most from AI guidance — improving on average about 13 points in sensitivity and 11 points in specificity. Dermatologists and dermatology residents performed better overall, but the sensitivity and specificity of their diagnoses also improved with AI.

“I was surprised to see everyone’s accuracy improve with AI assistance, regardless of their level of training,” Linos said. “This makes me very optimistic about the use of AI in clinical care. Soon our patients will not just be accepting, but expecting, that we use AI assistance to provide them with the best possible care.”

Jiyeong Kim

Researchers at the Stanford Center for Digital Health, including Kim, are interested in learning more about the promise of and barriers to integrating AI-based tools into health care. In particular, they are planning to investigate how the perceptions and attitudes of physicians and patients to AI will influence its implementation.

“We want to better understand how humans interact with and use AI to make clinical decisions,” Kim said. 

Previous studies have indicated that a clinician’s degree of confidence in their own clinical decision, the degree of confidence of the AI, and whether the clinician and the AI agree on the diagnosis all influence whether the clinician incorporates the algorithm’s advice when making clinical decisions for a patient.

Medical specialties like dermatology and radiology, which rely heavily on images — visual inspection, pictures, X-rays, MRIs and CT scans, among others — for diagnoses are low-hanging fruit for computers that can pick out levels of detail beyond what a human eye (or brain) can reasonably process. But even other more symptom-based specialties, or prediction modeling, are likely to benefit from AI intervention, Linos and Kim feel. And it’s not just patients who stand to benefit.

“If this technology can simultaneously improve a doctor’s diagnostic accuracy and save them time, it’s really a win-win. In addition to helping patients, it could help reduce physician burnout and improve the human interpersonal relationships between doctors and their patients,” Linos said. “I have no doubt that AI assistance will eventually be used in all medical specialties. The key question is how we make sure it is used in a way that helps all patients regardless of their background and simultaneously supports physician well-being.”

Researchers from the Karolinska Institute, the Karolinska University Hospital and the University of Nicosia contributed to the research.

The study was funded by the National Institutes of Health (grants K24AR075060 and R01AR082109), Radiumhemmet Research, the Swedish Cancer Society and the Swedish Research Council.

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Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu .

Artificial intelligence

Exploring ways AI is applied to health care

PERSPECTIVE article

A perspective on digital health platform design and its implementation at national level.

• 1 HPC-Medical & Bioinformatics Application Group, Centre for Development of Advanced Computing (C-DAC), Pune, India
• 2 National Supercomputing Mission, Centre for Development of Advanced Computing (C-DAC), Pune, India
• 3 Department of Technology, Savitribai Phule Pune University (SPPU), Pune, India

Accessible and affordable health services and products including medicines, vaccines, and public health are an important health agenda of all countries. It is well understood that without digital health technologies, countries will face difficulties in tackling the needs and demands of their population. Global agencies including the World Health Organization (WHO), United Nations (UN), International Telecommunication Union (ITU), etc. have been instrumental in providing various tools, and guidance through digital health strategies in improving health and digital health maturity of the countries. The Digital Health Platform Handbook (DHPH) is a toolkit published by WHO and ITU to help countries create and implement a digital health platform (DHP) to serve as the underlying infrastructure for an interoperable and integrated national digital health system. We apply the foundational principles of DHPH and provide a perspective of DHP components in a layered, enterprise architecture of a digital health infrastructure. India has rolled out the blueprint of its National Digital Health Mission (NDHM) to address the emerging needs for digitization of healthcare in the country. In this paper, we also illustrate the design and implementation of WHO-ITU DHP components at the national level by exploring India's digital health mission implementation utilizing various digital public goods to build a digital health ecosystem in the country.

1 Introduction

Technology usage and innovation in healthcare are continuously improving the delivery and quality of health services by aiding in diagnosis and treatment in preventive, curative, and palliative care. Accessible and affordable health services and products including medicines, vaccines, and public health services are an important health agenda of all countries and the World Health Organization (WHO). Health technology is one of the key drivers for achieving sustainable development goals for health. Technology has played an extremely important role during the COVID-19 pandemic in reporting, analyzing, and planning health interventions during the outbreak along with the distribution and rollout of vaccines. It is well understood that without technological health interventions, countries will face difficulties in tackling the needs and demands of their population.

Health is the third key goal of the United Nations (UN) Sustainable Development Goals (SDGs). Many countries have evolved eHealth strategies to improve health services in their countries. To harmonize and support the use of technology in health across the world in a standardized and interoperable manner, WHO along with the International Telecommunication Union (ITU) has, over time, announced different eHealth strategies aligned with the UN SDGs. The National eHealth Strategy Toolkit by WHO and ITU provides a set of basic components and processes to focus upon while developing a national eHealth strategy ( 1 ). It provides a broader vision of health system development and enables countries to shape the development of a national eHealth framework. The toolkit works as a guide for countries to develop their eHealth strategies and a benchmark for assessment of the implementation progress ( 2 – 7 ). Liaw et al. prepared a list of indicators to describe a country's digital health profiles along with the digital health maturity assessment tool that uses criteria co-developed with country stakeholders for Pacific Island Countries referring to the Global Digital Health Index (GDHI) and MEASURE (Monitoring and Evaluation to Assess and Use Results) Evaluation and Health Data Collaborative Toolkits and Maturity Models ( 8 – 10 ). Further, in 2020, the WHO developed a Global Strategy for Digital Health for 2020–2025. The vision of the strategy was to improve health for everyone, everywhere by accelerating the development and adoption of sustainable person-centric digital health solutions to prevent, detect and respond to epidemics; developing infrastructure and applications that enable countries to use data to promote health and wellbeing, and to achieve the health-related UN SDGs ( 11 ). It provides a comprehensive definition of digital health as “the field of knowledge and practice associated with any aspect of adopting digital technologies to improve health, from inception to operation,”. United Nations through SDG 3.8.2 have set universal healthcare coverage (UHC) as a target, to be achieved by 2030 ( 12 ). Only the appropriate use of digital technologies can accelerate the development of sustainable health systems through digital health initiatives guided by a robust national digital health strategy ( 13 ). The ability to exchange and use information between different systems (interoperability) is a fundamental requirement to accomplish healthcare goals. With fragmented, incomplete, and isolated health systems, a lack of interoperability would lead to the loss of continuity of care. Developing countries facing a lack of adequate infrastructure have the fundamental need to develop nationwide e-health agendas to achieve sustainable implementations ( 14 ).

While WHO's Global Strategy on Digital Health outlines the overarching vision, core principles, and strategic objectives that should guide national digital health initiatives, there is a definite possibility of applying different methodologies and design principles for building a national health system. The Digital Health Platform Handbook (DHPH) has been announced by WHO and ITU as a toolkit to help countries create and implement a digital health platform (DHP) to serve as the underlying infrastructure for an interoperable and integrated national digital health system ( 15 ). DHPH is more implementation and interoperability-focused and complements the top-level vision of global strategy by providing a detailed walkthrough of the design and implementation of the digital health platform for countries ( 16 ). This paper provides a perspective of the high-level blueprint of the DHP, and its common components, and compares it with the rolled-out blueprint and plan of the National Digital Health Mission in India.

2 Digital health platform (DHP)

WHO and ITU define a digital health platform as a common digital health information infrastructure (“infostructure”) having an integrated set of common and reusable components that external digital health applications and systems can use to deliver digital health services in a standardized, interoperable, and integrated manner ( 16 ). The broader goal is to simplify the information exchange within the health sector, promote re-usability, reduce the complexity of implementation, and enable seamless healthcare service delivery. The DHPH envisages three major stages i.e., context analysis, design, and implementation in developing a DHP with multiple individual tasks.

The context analysis stage requires identifying the business process requirements and improvements. Prioritizing and mapping these requirements based on the survey of the current health system, healthcare actors, and existing digital health assets through literature review and stakeholder interviews. The DHP design stage involves defining clear, concrete, and concise DHP principles, outlining the enterprise architecture, identifying DHP components based on the business processes, and identifying and adopting appropriate standards. To foster interoperability and adoption, a few of the recommended DHP design principles referred to in the handbook from Open Group Architecture Principles include; the use of APIs, collaborative decision-making, use of open standards, promoting open-source development for wide adoption, re-usability, and innovation, data quality, and integrity, etc. A summary of the common DHP components required for any national/regional implementation is discussed further in the sub-section. A DHP implementation approach could be either a ground-up approach—design before you build, or a hub approach—build while you design following different models of implementations i.e., centralized, decentralized, or hybrid. The suitability of the model depends upon the identified business processes and priorities, infrastructure, and data policies and practices ( 17 ). The handbook emphasized the importance of establishing the governance framework for DHP implementation and institutionalizing it with the designated eHealth entity responsible for its execution and maintenance. The handbook also provides various instances of context analysis, design, and implementation of infrastructure with inline vision by various countries and regions.

2.1 Common DHP components

DHP components are services/protocols, internal to the digital health platform, that allow external digital health applications and systems to provide and access information. The DHP components are expected to be—sharable and reusable across the platform and external systems; use-case independent to be applicable across different business processes such as maternal health programs, immunization programs, elderly care, HIV treatment, etc. as required by various SDG goals, and always scalable to cater to the dynamic and futuristic requirements.

Figure 1 provides the stack of the DHP components along with the various standards. The DHPH envisages two categories of components; enabling components and functional components. In our view, apart from the common components discussed in the handbook, the standards required for building the interoperable components should also be considered as the enabling components for a DHP. The handbook provides various characteristics and examples of these components.

Figure 1 . WHO-ITU common DHP components ( 16 ).

2.2 National digital health mission implementation in India

The Ministry of Health & Family Welfare (MoHFW), Govt. of India released the National Digital Health Blueprint (NDHB) in April 2019 following the visions of National Health Policy 2017 ( 18 , 19 ). The document provides a plan to achieve the digitalization of health records across the country; the creation of unique digital health IDs; building and maintaining registries of healthcare providers, patients, important diseases, and drugs; linking health records; payment gateways; and provision standards and regulations within the operating framework regarding data management and security. The blueprint also recommends the mandate of the National Digital Health Mission (NDHM) for designing, developing, and realizing universal technology building blocks useful for the implementation of the mission.

NDHM, renamed now as Ayushman Bharat Digital Mission (ABDM), is guided by the NDHB, and follows the National Health Stack 2018 document released by the National Institute for Transforming India- NITI Aayog, which is an apex public policy think-tank of the Government of India ( 20 ). The NDHB adopts a layered framework for digital health infrastructure, various building blocks, standards, policies, etc. The ABDM vision is to— create a national digital health ecosystem that supports universal health coverage in an efficient, accessible, inclusive, affordable, timely, and safe manner, that provides a wide range of data, information, and infrastructure services, duly leveraging open, interoperable, standards-based digital systems, and ensures the security, confidentiality, and privacy of health-related personal information ( 21 ). ABDM has stated business processes that aim to develop the backbone network to support the integrated digital health infrastructure of the country with the adoption of specific policy and technology principles such as building a single source of truth (registries), adopting federated architecture, adopting India Enterprise Architecture Framework (IndEA), use of open standards and open-source software, inclusivity, wellness-focused implementation, etc. ( 21 ).

The various building blocks identified and implemented in ABDM (adapted from ABDM Architecture published by NHA) are depicted in Figure 2 .

Figure 2 . ABDM building blocks.

Among the multi-sectoral national standards and services, Aadhaar [“Foundation” in English] is a biometric-based unique identity system provided by the Unique Identification Authority of India (UIDAI. It offers unique Aadhaar IDs to the citizens that can be used by any e-governance system to verify the identity of a person for her any digital interactions across local regions as well as states ( 22 ). While Aadhaar is not mandatory for creating ABDM Unique Health ID or Ayushman Bharat Health Account (ABHA), it can be used or linked for various purposes. ABDM uses only ABHA to track various artifacts of an individual in the system.

The Unified Payment Interface (UPI), created by the National Payment Corporation of India (NPCI) is a next-generation mobile-based payment system that enables round-the-clock, real-time bank payments. UPI is designed to promote digital payments in India ( 23 ). It leverages the high teledensity in India by making the mobile phone a primary payment device for both consumers and merchants and universalizing digital payments in the country ( 24 ). With the successful implementation, the UPI model has been adopted in many e-governance initiatives as well as in ABDM. There was a significant increase in the usage of digital payment using UPI in healthcare, pharmaceutical, and insurance during and after the COVID-19 pandemic ( 25 ).

The DigiLocker is an initiative of the Ministry of Electronics & IT (MeitY) under its Digital India programme. It provides a mobile application (a digital document wallet) that provides access to authentic digital documents to the citizen. It supports the storage, sharing, and verification of documents & certificates including Aadhaar and other proofs of identity and address, educational certificates, and recently the COVID-19 vaccine certificate, etc. ( 26 , 27 ). The architecture of DigiLocker is further adopted for developing the Health Locker under the ABDM.

The country has adopted a national consent management technical framework for consent-taking in all the e-governance programmes and initiatives. ABDM Consent Manager building block also leverages the same consent management framework along with standard ISO/TS 17975:2015 Health Informatics - Principles and data requirements for consent in the collection, Use or Disclosure of personal health information .

The online electronic signature service commonly referred to as eSign services of Govt. of India is one of the initiatives of moving to fully paperless citizen services. The services licensed by the Controller of Certifying Authorities (CCA) can be integrated into any service delivery applications to facilitate eSign for digitally signing documents ( 28 ). This infrastructure has been further planned for usage in ABDM for verification of health care providers and enabling them to sign the health records digitally for authentication and non-repudiation.

The standards have been followed by applying a minimalistic approach to data sharing i.e., mandate only the essential and minimum health information for supporting continuity of care. The various health data standards adopted include HL7 FHIR as structural data standard, DICOM for medical image representation and sharing, SNOMED CT as clinical terminology standard, ICD-10 for reporting and classification, LOINC for laboratory observations and measurements, and Common Drug Codes for India developed by the National Resource Centre for EHR Standards (NRCeS) ( 29 , 30 ). ABDM has a Health Data Management Policy that provides guidance and a framework for the secure processing of health records of individuals under ABDM ( 31 ).

The enabling and functional building blocks include the digital registries and FHIR Implementation Guide for ABDM, which serve as a single source of truth for all the components ( 32 – 35 ). The Terminology service is planned and the Drug Registry which uses the Common Drug Codes for India is piloted and not yet implemented in production ( 30 , 36 ).

Under information and mediation services, the Health Information Exchange (HIE) works as a Gateway for all the Health Information Provider (HIP) and Health Information User (HIU) entities for the exchange of health data. The HIE stores the indexes of the health records for searching and forwarding the requests based on the patient consent through the consent manager. Unified Health Interface (UHI) and Health Claim Exchange (HCX) are separate gateways implemented using an open protocol. UHI enables health service discovery and delivery by utilizing the enabling and functional building blocks of ABDM. UHI ensures interoperability in health services offered by a variety of participating providers from any application ( 37 ). The HCX aims to automate the health claim-related information exchange between payers, providers, beneficiaries, and other relevant entities. HCX gateway is currently in the initial stage of the implementation while UHI is not mandated. Their progressive adoption timelines have been set by NHA to balance the adoption burden amongst implementers. The GIS Service, Analytics, and Anonymizer are currently at the conceptual stage as the implementation of these services requires sufficient data and data-sharing policies. Currently, the DHP only refers to a broad metadata of the health records which is required for identification and indexing of the health records in HIE.

Several reference applications are developed to demonstrate the capabilities of these building blocks including PHR application, Health Locker, ABHA application, WebEMR for healthcare providers, etc. The other functional services such as health GIS, anonymizer service, health analytics service, etc. are also developed and planned as envisaged in NDHB.

3 Discussion

The ABDM has been rolled out in the country with its major building blocks and services including Patient, Provider, and Facility registries, Health Information Exchange, and FHIR based health records from 2021. The mission is in the middle of its implementation and has enabled the linking of over 300 million health records across various hospitals in the country, thus far ( 38 , 39 ). Despite being designed earlier, the ABDM building blocks and the architecture largely follow DHPH guidelines. The ABDM implementation is focused on wellness and continuity of care. The architecture is based on the IndEA framework, which is based on the most comprehensive, widely used, and accessible enterprise architectures, The Open Group Architecture Framework (TOGAF), a standard of the Open Group ( 40 ). DHPH provides guidelines to define the DHP design principles such as privacy and security, use of APIs, collaboration, open standards, open source, usability, Scalability, data custodianship, and policy adherence. The ABDM design is based on the major principles divided clearly into Technology Principles including a single source of truth; federated architecture; open APIs; standards and interoperability; and adherence to national IndEA framework; and the Policy Principles including privacy and security by design; user inclusivity; wellness centric; and voluntary participation. ABDM implementation is also governed by a strong framework through the establishment of NHA, stakeholder engagement via regular consultations for implementation of registries and ABDM building blocks, development of the network infrastructures across the country, and reusable nationwide services like Aadhar, DigiLocker, eSign, etc.

ABDM uses a decentralized architecture for data management, where the data resides at the healthcare facilities, and patient consent-based access is provided to the requesting healthcare provider through HIE APIs. The decentralized architecture has its challenges as they are costly to implement, always requires the availability of every participant, is resource-intensive to maintain, and is not scalable to accommodate changes, especially across the entire ecosystem of healthcare. They also pose challenges to trust in the network and hence may require exploring technologies such as blockchain ( 41 , 42 ). It should also be noted that DHP implementation may vary based on the country's priorities, policy environment, and available resources. Another good early example of a large-scale, nationwide DHP design is developed by Canada Health Infoway, called EHRS Blueprint. This blueprint depicts the information system architecture for various healthcare applications using shared infrastructure ( 43 ).

The current structure of the health records uses FHIR R4 Documents format. The health records are designed to support historical data i.e., PDFs and scans, structured text data, as well as terminology encoded data using SNOMED CT, ICD, and LOINC ( 44 ). This approach has helped in the quick adoption of the standard structure. However, most of the hospitals and health systems are currently supporting only the first format of data. Such an approach further requires technologies like OCR, AI-ML, etc., with high precision for machine processability of health records. Real interoperability can only be achieved when all the health systems in the ecosystem adopt the third form of data sharing. This requires the components such as the terminology services to be implemented and made available for data capture and validation.

The ABDM implementation aims to transform the health sector into an open, collaborative, interconnected ecosystem structured around an architecture of integrated digital services, the fact that participation in ABDM is voluntary for the healthcare providers as well as patients, poses challenges to its nationwide adoption. The availability of infrastructure and critical health data also lays the requirements for ensuring strict data safety. The data management policies and consent framework help in advocating the safety and legitimate use, storage, and access of data, however a strong data protection bill as part of the legislation is necessary to safeguard the patient's privacy and rights on data usage. Although regulations as powerful, countries often try to balance the needed regulations. They take a long time to implement and are hard to change over time ( 45 , 46 ). On the other hand, the flexible nature of policies is often better suited to the fast-changing field of digital technology. Hence with the constantly changing technology, in digital health, a balance of regulations and policies should be sought. The regulations should be implemented where patients' privacy and data protection could be compromised. Policies should be developed on the mechanisms and methods of handling data and processes. For example, for data protection, there is a Digital Personal Data Protection Act implemented in India ( 47 ) while data storage and handling guidelines are provided through a data management policy by NHA which will be updated from time to time.

There are strong requirements of compliance and certification for the integration of interoperability standards in ABDM for onboarding healthcare software. The platform requires different levels of compliance in terms of meeting the Minimum Viable Product (MVP), milestones and further the health systems go through a voluntary functional evaluation to ensure its meaningful use to improve the quality of healthcare processes and services ( 48 ). The research shows even after going through the mandatory certification process, due to different design choices, feature offerings, and computerization focus, the in-field experience of usability of the health systems may be completely different and may affect the performance of healthcare facilities ( 49 , 50 ). As a step towards institutionalizing the adoption of digital health platforms, policymakers should consider strengthening the certification scheme to minimize such functional differences or enable mechanisms to differentiate the systems based on their functions.

Establishing the governance framework and institutionalization are the next key steps after the DHP implementation. ABDM currently stands at the last step and there are multiple activities undertaken for institutionalization such as the implementation of the Digital Health Incentive Scheme (DHIS) ( 51 ), demonstration of smart digital hospitals at Primary Health Centers (PHCs) through PPP models called microsite implementations, developing multiple use cases under ABDM to showcase accessibility and delivery of healthcare services to the stakeholders, etc. Despite the rapid progress of the mission, there is a need for larger participation from the public as well as private healthcare providers. The mission should also develop a road map for its maturity through capacity building, monitoring, and periodic impact assessment ( 38 ). Success in healthcare innovation and transformation is possible only if the health workforce is ready to adopt the technology. The common barriers to the adoption of digital health technologies in the healthcare workforce in countries including India are health providers' skills, knowledge, confidence, and fears of technological separation affecting healthcare service delivery ( 52 , 53 ). While the solutions are onboarded, the policies, initiatives, and programs for digital health workforce development are particularly important for the widespread adoption of digital health applications ( 54 ). Promoting digital health translation through investment in capacity-building programs at hospitals, medical colleges, and universities along with demonstrating the key benefits of the digital health ecosystem in practice is crucial for the successful adoption of the mission.

Effective utilization of healthcare technology also requires a certain level of digital literacy in patients, which is unevenly distributed across the country. Presently, although India has the highest rates of smartphone usage globally, gender and age disparities and the digital literacy divide still exist ( 55 ). Addressing the digital divide through digital literacy policies and implementation in a collaborative approach involving public, private, and non-profit entities is crucial for the successful implementation of the mission.

This paper presents an approach to implementing DHP components in a layered, reusable, and holistic manner. The WHO-ITU Common DHP Components, Figure 1 can be used as a reference architecture for evaluating and designing a digital health enterprise architecture at any scale. This is one of the ways of depicting the WHO components and there might be several other depictions possible. The layered enterprise architecture of DHP enables the reusability of the technology components while digital health standards such as HL7 FHIR, DICOM, SNOMED CT, ICD, and LOINC enable the reusability of data in a federated digital health ecosystem. A well-defined policy and technology principles with appropriate governance in place can help in providing a clear vision for implementing a DHP. A step-by-step approach to the adoption of digital health services is a feasible mechanism to avoid the burden of implementation. Countries promote incentive schemes for digital health adoption which many times pose challenges to the data quality. This necessitates the use and implementation of Information Quality Frameworks which is currently missing ( 56 ). While promoting adoption, appropriate majors must be taken to ensure the completeness and correctness of health records through certification schemes, MVP guidelines, IQ frameworks, etc. so that the data can be used for secondary healthcare purposes. This work focuses mainly on the design and implementation of a DHP, and this can further be extended by defining a common framework for governance and institutionalization referring to DHPH guidelines.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Author contributions

MM: Conceptualization, Investigation, Methodology, Project administration, Writing – original draft. GS: Project administration, Writing – review & editing. SK: Supervision, Writing – review & editing. AA: Supervision, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article.

This work is carried out under the National Resource Centre for EHR Standards (NRCeS) project set up at C-DAC, Pune, and funded by the MoHFW, India.

Acknowledgments

The authors would like to acknowledge the MoHFW and the National Health Authority (NHA) for making all the information public for reference which helped in studying and comparing the DHP components with the ABDM building blocks. MM would like to acknowledge Dr. Rajendra Joshi, Head of the Department, HPC-Medical and Bioinformatics Applications Group for his continuous support and guidance and to the institution for the opportunity to contribute to this body of knowledge.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1. WHO, ITU. National eHealth Strategy Toolkit Overview . World Health Organization and International Telecommunication Union (2012). p. 9. https://www.who.int/publications/i/item/national-ehealth-strategy-toolkit (Accessed January 01, 2024).

Google Scholar

2. Hamilton C. The WHO-ITU national eHealth strategy toolkit as an effective approach to national strategy development and implementation. Stud Health Technol Inform . (2013) 192:913–6. doi: 10.3233/978-1-61499-289-9-913

PubMed Abstract | Crossref Full Text | Google Scholar

3. Tsai CH, Koch S. Towards a framework for national eHealth evaluation and monitoring: a combined top-down and bottom-up approach using Sweden as example. Stud Health Technol Inform . (2019) 264:954–8. doi: 10.3233/SHTI190365

4. Shouli SA, Mechael PN. The state of Saudi digital health by the global digital health index. Comput Methods Programs Biomed . (2019) 171:8. doi: 10.1016/J.CMPB.2018.12.018

Crossref Full Text | Google Scholar

5. Riazi H, Jafarpour M, Bitaraf E. Towards national eHealth implementation-a comparative study on WHO/ITU national eHealth strategy toolkit in Iran. Stud Health Technol Inform . (2014) 205:246–50. doi: 10.3233/978-1-61499-432-9-246

6. Foster R. Assessing the development process of the eHealth strategy for South Africa against the recommendations of the WHO/ITU national eHealth strategy toolkit. J Int Soc Telemed eHealth . (2013) 1:62–72. https://journals.ukzn.ac.za/index.php/JISfTeH/article/view/36

7. Saunders C, Scott RE. Applying change management metaphors to a national e-health strategy. Stud Health Technol Inform . (2014) 206:62–9. doi: 10.3233/978-1-61499-456-5-62

8. Liaw ST, Zhou R, Ansari S, Gao J. A digital health profile & maturity assessment toolkit: cocreation and testing in the Pacific Islands. J Am Med Inform Assoc . (2021) 28:494–503. doi: 10.1093/JAMIA/OCAA255

9. HealthEnabled, Global Development Incubator (GDI). Global Digital Health Index Indicator Guide (2020).

10. MEASURE Evaluation. Health Information Systems Interoperability Maturity Toolkit — MEASURE Evaluation. Available online at: https://www.measureevaluation.org/resources/tools/health-information-systems-interoperability-toolkit.html (Accessed August 17, 2022).

11. WHO. Global Strategy on Digital Health 2020–2025 (2021).

12. UN. The Sustainable Development Goals. United Nations sustainable development (2020). Available online at: https://www.un.org/sustainabledevelopment/sustainable-development-goals/ (Accessed August 16, 2022).

13. Hellberg S, Johansson P. Ehealth strategies and platforms—the issue of health equity in Sweden. Health Policy Technol . (2017) 6:26–32. doi: 10.1016/j.hlpt.2016.09.002

14. Luna D, Almerares A, Mayan JC, de Quirós FGB, Otero C. Health informatics in developing countries: going beyond pilot practices to sustainable implementations: a review of the current challenges. Healthc Inform Res . (2014) 20:3. doi: 10.4258/HIR.2014.20.1.3

15. Ibeneme S, Karamagi H, Muneene D, Goswami K, Chisaka N, Okeibunor J. Strengthening health systems using innovative digital health technologies in Africa. Front Digit Health . (2022) 4:51. doi: 10.3389/fdgth.2022.854339

16. WHO, ITU. Digital Health Platform Handbook: Building a Digital Information Infrastructure (Infostructure) for Health (2020).

17. Jalal-Karim A, Balachandran W. The optimal network model’s performance for sharing electronic health record. IEEE INMIC 2008: 12th IEEE International Multitopic Conference—conference Proceedings (2008). p. 149–54. doi: 10.1109/INMIC.2008.4777726

18. MoHFW. National Health Policy 2017 (2017). p. 1–31.

19. MoHFW. National Digital Health Blueprint (2019).

20. NITI Aayog. National Health Stack Strategy and approach (2018).

21. NHA. Official website—Ayushman Bharat Digital Mission (2020). Available online at: https://abdm.gov.in/abdm (Accessed September 12, 2022).

22. Rao U, Nair V. Aadhaar: governing with biometrics. South Asia: J South Asian Stud . (2019) 42:469–81. doi: 10.1080/00856401.2019.1595343

23. NPCI. UPI: Unified Payments Interface—Instant Mobile Payments, NPCI (2016). Available online at: https://www.npci.org.in/what-we-do/upi/product-overview (Accessed April 12, 2023).

24. Gochhwal R, Gochhwal R. Unified payment interface—an advancement in payment systems. Am J Ind Business Manag . (2017) 7:1174–91. doi: 10.4236/AJIBM.2017.710084

25. Devanesan MD, Tholath DI, Vakkayil S. Influence of promotions and change in buyer behaviour due to COVID 19 on the intent to increase usage of digital payment systems. Acad Mark Stud J . (2021) 25. https://api.semanticscholar.org/CorpusID:234598662

26. Ministry of Electronics and IT (MeitY). DigiLocker: An Initiative Towards Paperless Governance (2015). Available online at: https://www.digilocker.gov.in (Accessed April 12, 2023).

27. Petare P, Mohite P, Joshi M. Digilocker (digital locker—ambitious aspect of digital India programme). Int J Mater Res . (2015) 108:342–3. doi: 10.3139/146.017051

28. CCA. eSign, CCA. Available online at: https://cca.gov.in/eSign.html (Accessed April 12, 2023).

29. National Health Authority. National Digital Health Mission Strategy Overview (2020).

30. C-DAC. Common Drug Codes for India, NRCeS (2019). Available online at: https://nrces.in/services/national-releases#drug_codes (Accessed April 12, 2023).

31. NHA. NHA, Ayushman Bharat Digital Mission Publications. Available online at: https://abdm.gov.in/publications (Accessed December 7, 2022).

32. NHA. Healthcare Professionals Registry (2020). Available online at: https://hpr.ndhm.gov.in/en (Accessed December 2, 2022).

33. NHA. Health Facility Registry (2021).

34. NHA. ABHA—Ayushman Bharat Health Account (2020). Available online at: https://healthid.ndhm.gov.in/ (Accessed December 2, 2022).

35. NRCeS. FHIR Implementation Guide for ABDM (2020). Available online at: https://nrces.in/ndhm/fhir/r4/index.html (Accessed April 12, 2023).

36. NHA. ABDM Drug Registry Consultation Paper (2022). 1–44. Available online at: https://abdm.gov.in:8081/uploads/Drug_Registry_Consultation_Paper_092e75a15e.pdf (Accessed February 18, 2024).

37. NHA. ABDM, Unified Health Interface. (2022). Available online at: https://uhi.abdm.gov.in/ (Accessed December 8, 2022).

38. Sharma RS, Rohatgi A, Jain S, Singh D. The ayushman bharat digital mission (ABDM): making of India’s digital health story. CSI Transactions on ICT . (2023) 11(1):3–9. doi: 10.1007/S40012-023-00375-0

39. NHA. ABDM Insights (2023). Available online at: https://dashboard.abdm.gov.in/abdm/ (Accessed February 15, 2024).

40. Pallab S. India’s National Digital Health Mission: A New Model to Enhance Health Outcomes—The Open Group Blog (2020). Available online at: https://blog.opengroup.org/2020/08/19/indias-national-digital-health-mission-a-new-model-to-enhance-health-outcomes/ (Accessed April 12, 2023).

41. Kotsiuba I, Velvkzhanin A, Yanovich Y, Bandurova IS, Dyachenko Y, Zhygulin V. Decentralized e-health architecture for boosting healthcare analytics. Proceedings of the 2nd World Conference on Smart Trends in Systems, Security and Sustainability, WorldS4 2018 (2018). p. 44–50. doi: 10.1109/WORLDS4.2018.8611621

42. Pandey P, Litoriya R. Implementing healthcare services on a large scale: challenges and remedies based on blockchain technology. Health Policy Technol . (2020) 9:69–78. doi: 10.1016/J.HLPT.2020.01.004

43. Sinha P, Sunder G, Bendale P, Mantri M, Dande A. Canada’s EHRS blueprint. Electronic Health Record: Standards, Coding Systems, Frameworks, and Infrastructures . chapter 22. (2012). p. 211–20. doi: 10.1002/9781118479612.ch22

44. NRCeS. FHIR Implementation Guide for ABDM (2020). Available online at: https://nrces.in/ndhm/fhir/r4/index.html (Accessed February 18, 2024).

45. MeitY. Notice on Public Comments on DRAFT PDP Bill (2020). Available online at: https://www.meity.gov.in/writereaddata/files/Notice—Public Consultation on DPDP 2022_1.pdf (Accessed April 12, 2023).

46. MeitY. Constitution of a Committee for Data Protection Framework for India. (2017).

47. Ministry of Law and Justice. The Digital Personal Data Protection Act. India (2023).

48. NHA. QCI Certification of ABDM Compliance (2022). Available online at: https://abdm.gov.in/qcicertified (Accessed May 16, 2023).

49. Ratwani RM, Benda NC, Zachary Hettinger A, Fairbanks RJ. Electronic health record vendor adherence to usability certification requirements and testing standards. JAMA . (2015) 314:1070–1. doi: 10.1001/JAMA.2015.8372

50. Holmgren AJ, Adler-Milstein J, McCullough J. Are all certified EHRs created equal? Assessing the relationship between EHR vendor and hospital meaningful use performance. J Am Med Inform Assoc . (2018) 25:654–60. doi: 10.1093/jamia/ocx135

51. NHA. Digital Health Incentive Scheme. (2023) Available online at: https://abdm.gov.in/DHIS (Accessed December 4, 2023).

52. Pote H, Rees A, Holloway-Biddle C, Griffith E. Workforce challenges in digital health implementation: how are clinical psychology training programmes developing digital competences? Digit Health . (2021) 7. doi: 10.1177/2055207620985396

53. Borges do Nascimento IJ, Abdulazeem H, Vasanthan LT, Martinez EZ, Zucoloto ML, Østengaard L, et al. Barriers and facilitators to utilizing digital health technologies by healthcare professionals. npj Digital Med . (2023) 6:1–28. doi: 10.1038/s41746-023-00899-4

54. Mumtaz H, Riaz MH, Wajid H, Saqib M, Zeeshan MH, Khan SE, et al. Current challenges and potential solutions to the use of digital health technologies in evidence generation: a narrative review. Front Digit Health . (2023) 5:1203945. doi: 10.3389/FDGTH.2023.1203945/BIBTEX

55. Tewathia N, Kamath A, Ilavarasan PV. Social inequalities, fundamental inequities, and recurring of the digital divide: insights from India. Technol Soc . (2020) 61:101251. doi: 10.1016/J.TECHSOC.2020.101251

56. Fadahunsi KP, O’Connor S, Akinlua JT, Wark PA, Gallagher J, Carroll C, Car J, Majeed A, O’Donoghue J. Information quality frameworks for digital health technologies: systematic review. J Med Internet Res (2021) 23:e23479. doi: 10.2196/23479

Keywords: digital health platform, WHO, ITU, interoperability, standards, India, NDHM

Citation: Mantri M, Sunder G, Kadam S and Abhyankar A (2024) A perspective on digital health platform design and its implementation at national level. Front. Digit. Health 6:1260855. doi: 10.3389/fdgth.2024.1260855

Received: 18 July 2023; Accepted: 2 April 2024; Published: 11 April 2024.

Reviewed by:

© 2024 Mantri, Sunder, Kadam and Abhyankar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Manisha Mantri [email protected] ; [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Digital health in the era of COVID-19: Reshaping the next generation of healthcare

Emnet getachew.

1 Center for Innovative Drug Development and Therapeutic Trials for Africa (CDT-Africa), College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia

2 Department of Public Health, College of Health Science, Arsi University, Asella, Ethiopia

Tsegaye Adebeta

3 Outpatient Department, Ethiopian Airlines Medical Unit, Addis Ababa, Ethiopia

Seke G. Y. Muzazu

4 Enteric Diseases and Vaccines Research Unit, Centre for Infectious Disease Research in Zambia (CIDRZ), Lusaka, Zambia

Loveness Charlie

5 KNCV TB Foundation, Challenge TB Project, Blantyre, Malawi

6 Outpatient Department, Kibong'oto National Tuberculosis Hospital, Moshi, Kilimanjaro, Tanzania

Hanna Amanuel Tesfahunei

7 Department of Public Health, Hager Biomedical Research Institute, Asmara, Eritrea

Catherine Lydiah Wanjiru

8 Outpatient Department, Pope John's Hospital Aber, Atapara, Uganda

Violet Dismas Kajogoo

Samrawit solomon.

9 School of Public Health, Saint Paul's Hospital Millennium Medical College, Addis Ababa, Ethiopia

Mary Gorret Atim

10 Soroti Regional Referral Hospital, Soroti, Uganda

Tsegahun Manyazewal

Associated data.

Publicly available datasets were analyzed in this study. This data can be found here: https://osf.io/gvm2z/ .

COVID-19 is one of the most deadly diseases to have stricken us in recent decades. In the fight against this disease, governments and stakeholders require all the assistance they can get from various systems, including digital health interventions. Digital health technologies are supporting the tracking of the COVID-19 outbreak, diagnosing patients, expediting the process of finding potential medicines and vaccines, and disinfecting the environment, The establishment of electronic medical and health records, computerized clinical decision support systems, telemedicine, and mobile health have shown the potential to strengthen the healthcare system. Recently, these technologies have aided the health sector in a variety of ways, including prevention, early diagnosis, treatment adherence, medication safety, care coordination, documentation, data management, outbreak tracking, and pandemic surveillance. On the other hand, implementation of such technologies has questions of cost, compatibility with existing systems, disruption in patient-provider interactions, and sustainability, calling for more evidence on clinical utility and economic evaluations to help shape the next generation of healthcare. This paper argues how digital health interventions assist in the fight against COVID-19 and their opportunities, implications, and limitations.

Introduction

The World Health Organization has suggested that countries keep maximizing the opportunities for digital health interventions (DHIs) to accelerate sustainable health development and universal health coverage. DHIs are applications of smartphones, health information technology, wearable devices, telemedicine, and personalized medicine to facilitate healthcare and attain intended health outcomes ( 1 , 2 ). They enhance patient care by facilitating treatment adherence and monitoring, person-centered care, laboratory diagnosis, data management, disease surveillance, drug-safety monitoring, and professional development ( 3 – 5 ). Healthcare institutions are looking for digital health innovations to improve care quality by integrating various technologies. However, there are impediments that countries, especially those with limited resources, face when implementing digital health, calling for a good grasp of how to develop appropriate strategies to benefit the most of digital health-enabled patient-centered health systems ( 5 , 6 ).

Even though COVID-19 has caused massive problems in the healthcare system, it has forced the majority of countries to bridge the DHI gap ( 7 ). Different studies across the globe assessed the potential of DHIs in the fight against COVID-19, including their role in service delivery, health literacy, disease surveillance, treatment and vaccination, and program follow-up ( 7 – 10 ). At the start of the pandemic, innovative digital health-based analysis of social media data and news reports assisted in forecasting the spread of the disease. Social media platforms and features open up new avenues for educating people including hard-to-reach, recruiting participants in therapeutic trials, and remote-delivering of healthcare. However, there are several digital health implementation challenges and opportunities across countries and territories that need to be compiled to inform policy and practice as the disease is not yet over. This perspective paper argues how digital health interventions assist in the fight against COVID-19 and their opportunities, implications, and limitations.

Opportunities for implementing digital health interventions in the era of COVID-19

The COVID-19 pandemic served as a veritable testing ground for emerging digital health concepts and practices. DHIs provide enormous support in the social distancing time that interrupted healthcare service delivery. The application of telemedicine has facilitated service continuity with great potential to protect both patients and care providers ( 11 ). Hospitals' closure forced the public to seek and practice alternative digital health solutions such as smartphones to connect with their clinicians and follow-up routine care ( 8 , 12 ). The use of digital health for COVID-19 screening reduced the number of visits to emergency departments while also improving healthcare system organization ( 13 ). mHealth, telemedicine, eHealth, and a variety of other mobile applications rose to prominence during lockdowns and were widely used for diagnosis, clinical care, and patient follow-up, demonstrating their potential beyond serving marginalized and underprivileged communities ( 14 ).

Virtual communication platforms facilitated remote interactions between healthcare professionals and patients, as well as the creation of operational management dashboards for optimizing workflows, resources, and patient-centered care. Several healthcare institutions have been drawn to cloud technologies in order to implement discrete COVID-19-related functionality such as testing, diagnostics, monitoring, triage, and patient consultations. large numbers of research papers accessible through the COVID-19 Open Research Database can be analyzed quickly using machine learning to extract relevant knowledge about drugs that might be beneficial for the treatment of COVID-19. By generating data summaries from multiple sources, artificial intelligence platforms enabled real-time monitoring of patients in high-risk settings for COVID-19. Insurance companies use of health-tracking reward programs that encourage the application of wearable health technologies, though implementation has been straightforward.

We have included case studies from three different countries to demonstrate how DHI has been used to support the healthcare system during the pandemic.

Case 1: China

In response to the pandemic, a hospital in China's Guangdong province that is well-known for its smart services used an existing platform to launch COVID 19 responsive services. These included information hubs, e-Consultation and screening, remote symptom monitoring, and psychological support. The system was web-based and linked users from social media sites (WeChat Facebook/Twitter equivalent), a phone App, and a website. The hospital reported that they saw a drastic drop in outpatient visits during the pandemic lockdowns but recorded high usage of the online services even at the height of the pandemic. This implies that more people had been restricted access to physical health services but they still got the healthcare services through DHIs. They also reported that the system allowed them to better triage patients who needed emergency response by providing remote care and hence, reducing the workload on clinicians and encouraging social distancing ( 15 ).

Case 2: Kenya

A provider-to-provider (P2P) asynchronous telemedicine model developed and implemented during the second year of COVID-19 in Kenya facilitated the delivery of essential health services ( 16 ). Since 2011, the Addis Clinic telemedicine platform has been providing access to specialized medical experts for frontline healthcare providers treating patients in low-resource settings. Healthcare providers heavily used this digital health platform in Kenya during the outbreak, indicating they found it very useful to them during the outbreak. Despite some of the infrastructure and network connectivity challenges present in the country, the provider-to-provider telemedicine platform was a viable option for receiving clinical recommendations from medical experts located remotely and sustaining essential health services.

Case 3: Ethiopia

A COVID-19 e-health educational intervention in Ethiopia targeting healthcare workers delivered a series of three 1-h medical seminars on COVID-19 prevention and treatment. The study collected post-seminar evaluation data from the participants using a questionnaire. The findings demonstrated the promising potential of transitioning healthcare training and delivery from an in-person to a digital medium in low-resource settings like Ethiopia ( 17 ). COVID-19 had a significant burden on patients, healthcare providers, and the healthcare system in general in Ethiopia, where the Ethiopian government and its partners intervened to sustain healthcare services ( 18 – 20 ).

Case 4: Vietnam

Since the beginning of the COVID-19 pandemic, seven major digital applications have been implemented in Vietnam ( 21 ). They have been classified into four categories based on their main purpose, including surveillance and contact tracing, health communication, telemedicine, and Artificial Intelligence to support diagnosis and treatment. The eCDS software was primarily focused on reporting case-based hospital admissions via an electronic system. Furthermore, two mobile apps (NCOVI and Vietnam Health Declaration) were created to record electronic health declaration forms for domestic and international travelers for case monitoring and surveillance ( 21 ).

Challenges and limitations to implementing digital health technologies

Despite a history of public health crises, data-sharing agreements and transactional standards do not exist uniformly between institutions, impeding a foundational infrastructure to meet data-sharing and integration needs for public health advancement. COVID-19 has revealed not only the need for data sharing but also the importance of serious evaluation and ethical considerations in the emerging field of digital health ( 22 , 23 ). One of the main challenges in the biomedical research community and one of the contributors to international information sharing is maintaining control over the constantly generated data while simultaneously promoting their active use for generating scientific discoveries. Obtaining informed consent has been also a significant challenge in providing transparency about the kind of documents collected and which third parties able to access patient's data. Procedures were carried out remotely and/or via electronic consent during the pandemic; however, not all healthcare facilities were prepared to provide digital consent, prompting some scientists to create their own way of acquiring consent and electronic signature for participants in therapeutic trials. Issues such as potential participants' access to technology and an absence of user-friendly functionalities to interpret consent documents exacerbated the preceding problem of having complicated, lengthy, and technical consent documents in studies that used e-consent platforms.

There is a desperate need for strengthening resource capacity for effective implementation and evaluation of digital health technologies, taking into account the needs and priorities of countries ( 24 – 26 ). Inequalities in infrastructure and access to the internet and electricity are among the major challenges in the implementation and scale-up of DHIs in resource-limited settings. Digital health technologies were recommended to help patients adhere to their treatment; however, for optimal implementation of such technologies, trials evaluating the effectiveness of remote treatment are critically needed ( 19 , 27 – 29 ).

Digital health literacy in the general population often determines the acceptability and adaptability of digital health solutions in a given country and this has been witnessed in the era of COVID-19. Low levels of digital health literacy were found to have a significant association with an individual's COVID-19 precautionary practices, information accuracy, vaccine hesitancy, and subjective wellbeing ( 30 – 34 ). The lack of qualified and skilled professionals in digital health is one of the main barriers to digital health applications, especially in resource-constrained settings ( 35 ).

In many resource-limited countries, the lack of policies and strategies, governance structures standard operating procedures, and financial resources hamper the successful deployment and implementation of DHIs ( 35 ). Sufficient and sustainable financial mechanisms are not in place for large-scale deployment of approved DHIs. Significant number of such technologies are not culturally adapted to incorporate the local context and facilitate easy understanding by patients and providers ( 36 ). The move away from traditional face-to-face care and treatment to digital health-enabled remote care and monitoring was not straightforward and the pros and cons vary by country, program, and the type of digital health technology employed ( 37 ).

During the COVID-19 pandemic, there were a large number of patients seeking healthcare, putting a significant strain on healthcare providers. As a result, remote patient monitoring and the use of mHealth applications became an essential part of healthcare delivery. Telemedicine for remote consultation and the use of health devices such as pulse oximetry was a pivotal DHI in the COVID-19 pandemic. As mentioned in the case studies, applications designed for patient surveillance and contact tracing were crucial in reducing the pandemic's impact. The pandemic brought all stakeholders' priorities in various aspects of digital health adoption in line, including expedited regulatory approval of clinical studies. However, the widespread use of DHIs has been hampered by a lack of infrastructure, equitable access to the internet and electricity, and evidence-based digital health standards and data governance systems.

In the COVID-19 era, electronic medical records provide large amounts of data that can be used to generate research evidence, but these data require quality assurance and valid sampling procedures. Such disadvantages reduce the quality of research driven by data captured through digital health. Privacy is a concern in the digitalization of health care, where data generated by digital health must be safeguarded. It is also important to clearly explain to all stakeholders how their data will be used and protected.

DHI implementation during COVID-19 faces some unique challenges in resource-constrained settings. There were reservations and uncertainties regarding the use, adaptation, and integration of DHIs into the wider scope healthcare system before COVID-19, which slowed information dissemination and prompt responses. In these settings, insufficient financial resources, electricity supply, internet connectivity, and a trained workforce impede DHI implementation during COVID-19.

Because of their diverse nature and types, the lack of homogeneous interventions creates a major obstacle in implementing DH interventions. Such distinctions may limit model generalization and understanding of DH effectiveness (13, 78). During the pandemic, most low- and middle-income countries faced serious issues with the practicability and desirability of digital technology by care providers. This is due to a shortage of training on new technology tools, insufficient technical assistance, internet connectivity problems, and also other administrative complexities. Thus, care providers in those countries find it difficult to adopt and use digital health solutions in the healthcare system. Acknowledging that many patients need recommendations or guidance, the use of digital technologies in providing guidance services has already been previously deployed in some countries and can be adopted as an alternative to physical visits in hospitals.

We can conclude from the preceding discussion that DHIs have enabled countries to mitigate the impact of the COVID-19 pandemic, paving the way for and reshaping the next generation of healthcare. COVID-19 has aided in the massive deployment of DHIs for immediate outbreak response. To better move the DHIs momentum forward and sustainably mitigate COVID-19, equitable access to approved digital health technologies, political commitment, collaboration, stakeholder cooperation, and workforce capacity building are required. Beyond COVID-19, there may be an opportunity to set an additional focus and implement policies throughout the health system to support the potential use of DHI solutions.

The implication of this paper for policy and practice is summarized in Table 1 .

Implications of the paper for digital health interventions policy and practice in the era of COVID-19.

Data availability statement

Author contributions.

Study conception, data acquisition, synthesis, and first draft of the manuscript: EG and TA. Data acquisition and synthesis: SM, CW, VK, HT, LC, JA, BS, SS, MA, and TM. Resource acquisition and critical review of the draft manuscript: TM. All authors reviewed and approved the final version for publication.

Acknowledgments

The authors would like to thank and appreciate the technical support given by the Center for Innovative Drug Development and Therapeutic Trias for Africa (CDT-Africa), College of Health Sciences, Addis Ababa University, Ethiopia.

Abbreviations

DHIs, digital health interventions.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

AI powered 'digital twin' models the infant microbiome

The gut microbiome has a profound impact on the health and development of infants. Research shows that dysbiosis -- or imbalances in the microbial community -- is associated with gastrointestinal diseases and neurodevelopmental deficits. Understanding how gut bacteria interact, and how these interactions may lead to some of these problems, however, is difficult and time consuming through traditional laboratory experiments.

Researchers at the University of Chicago have developed a new generative artificial intelligence (AI) tool that models the infant microbiome. This "digital twin" of the infant microbiome creates a virtual model that predicts the changing dynamics of microbial species in the gut, and how they change as the infant develops. Using data from fecal samples collected from preterm infants in the neonatal intensive care unit (NICU), researchers used the model, called Q-net, to predict which babies were at risk for cognitive deficits with 76% accuracy.

"You can only get so far by looking at snapshots of the microbiome and seeing the different levels of how many bacteria are there, because in a preterm infant, the microbiome is constantly changing and maturing," said Ishanu Chattopadhyay, PhD, Assistant Professor of Medicine and senior author of the new study, published in Science Advances . "So, we developed a new approach using generative AI to build a digital twin of the system that models the interactions of the bacteria as they change."

Just like other forms of AI, the digital twin concept is a potentially transformative technology, bridging the fields of computer science, engineering, mathematics, and life sciences to replicate the behavior of biological systems. In the case of microbiome dynamics, Chattopadhyay says it's a matter of scale. Typical wet lab experiments that test the interactions of bacteria are time consuming. Testing all the two-way interactions of a typical colony with 1,000 species would take more than 1,000 years -- not to mention that more complex interactions of three, four, or more species are common.

The Q-net model drastically speeds up the time of testing out these interactions, highlighting those that may be of interest for links to a particular outcome. Chattopadhyay and his colleagues trained the model using fecal sample data from infants at UChicago's Comer Children's Hospital. Next, they validated its predictions about how the microbiome would develop using sample data from Beth Israel Deaconess Medical Center in Boston. The model predicted which babies were at risk for cognitive deficits, as measured by head circumference growth, with 76% accuracy.

The model also indicated that interventions like restoring the abundance of a particular bacterial species could reduce the developmental risk of about 45% of the babies. The authors caution, however, that the model also showed that incorrect interventions can make the risk worse.

"You can't just give probiotics and hope that the developmental risk is going to go down," Chattopadhyay said. "What you are supplanting is important, and for many subjects, you also have to time it precisely."

Since Q-net can identify potentially interesting combinations of bacteria, it vastly narrows the search for potential treatment targets. If the gut microbiome is the proverbial haystack, Q-net can give researchers the one-inch squares where they can find the needles.

Chattopadhyay's research partners, like co-author Erika Claud, MD, Professor of Pediatrics and Director of the Center for the Science of Early Trajectories are working with bioreactors that simulate the live gut microbiome environment where they can test out potential interventions and see what happens.

At its core, Q-net models large numbers of variables that interact with each other, so Chattopadhyay believes it can be used for other systems beyond the microbiome, such as the evolution of viruses, or even social phenomena like public opinions.

"If you have a large amount of data, you can train this system well and it will figure out what the connections are," he said. "It can capture very subtle differences, so it has a really large number of applications."

• Infant's Health
• Pharmacology
• Gastrointestinal Problems
• Down Syndrome
• Wounds and Healing
• Sudden infant death syndrome
• Cerebral palsy

Story Source:

Materials provided by University of Chicago . Original written by Matt Wood. Note: Content may be edited for style and length.

Journal Reference :

• Nicholas Sizemore, Kaitlyn Oliphant, Ruolin Zheng, Camilia R. Martin, Erika C. Claud, Ishanu Chattopadhyay. A digital twin of the infant microbiome to predict neurodevelopmental deficits . Science Advances , 2024; 10 (15) DOI: 10.1126/sciadv.adj0400

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Studies highlight impact of social media use on college student mental health

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When Kyle Palmberg set out to design a research study as the capstone project for his psychology major at St. Mary’s University of M i nnesota in Winona, he knew he wanted his focus to be topical and relevant to college students.

His initial brainstorming centered around the mental health impact of poor sleep quality. 

“I wanted to look at college students specifically, to see the different ways that sleep quality can be harmed and how that can impact your mental health,” he said. As he reviewed the scientific literature, one variable kept appearing. “The topic that kept coming up was social media overuse,” he said. “It is such an important thing to my target demographic of college students.”

Palmberg, 22, grew up surrounded by social media. He’d heard plenty of warnings about the downsides of spending too much time online, and he’d seen many of his peers seemingly anchored to their phones, anxious or untethered if they had to put them down for more than a few minutes at a time.

“I think from my perspective as someone who’s been really interested in psychology as an academic discipline, social media addiction is also something I’ve been aware of personally,” Palmberg said. “I can tell within myself when things can become harmful or easy to misuse. I often see the hints of addictive behaviors in peers and coworkers.”

Palmberg found much of the published research on the topic inspiring, particularly a 2003 study on internet gambling addiction. 

“They were looking at how internet gambling addiction permeates a person’s behavior,” he said. Palmberg hypothesized that there may be behavioral similarities between people addicted to online gambling and those addicted to social media. 

“Social media provides this convenient platform for users to interact with others,” he said. “As users grow addicted, they learn that they can come back to that social platform more and more to get their needs met. The tolerance users have for gratifying that social need grows. Then they have to use social media more and more often to get those benefits.”

The negative impact of a growing dependence on social media is that time spent online takes away from real in-person interactions and reduces the time a person has available for basic personal care needs, like sleep and exercise, Palmberg said. This can ultimately have a negative impact on mental health.

“As a person builds a high tolerance for the use of social media it causes internal and external conflict,” he said. “You know it is wrong but you continue to use it. You relapse and struggle to stop using it.” Palmberg said that social media use can be a form of “mood modification. When a person is feeling down or anxious they can turn to it and feel better at least for a moment. They get a sense of withdrawal if they stop using it. Because of this negative side effect, it causes that relapse.”

Palmberg decided he wanted to survey college students about their social media use and devise a study that looked at connections between the different motivations for that use and potential for addictive behaviors. He ran his idea by his academic advisor, Molly O’Connor, associate professor of psychology at Saint Mary’s, who was intrigued by his topic’s clear connections to student life.

“We often notice social media addiction with our student population,” O’Connor said. She knew that Palmberg wouldn’t have a hard time recruiting study participants, because young people have first-hand experience and interest in the topic. “He’s looking at college students who are particularly vulnerable to that addiction. They are tuned into it and they are using it for coursework, socialization, entertainment, self-documentation.”

O’Connor said she and her colleagues at the university see signs of this addiction among many of their students. 

“They’ll be on their phones during class when they are supposed to pay attention,” she said. “They can’t help themselves from checking when a notification comes through. They say they had trouble sleeping and you’ll ask questions about why and they’ll say they were scrolling on their phone before they went to bed and just couldn’t fall asleep.”

The entertainment-addiction connection

Once his study was given the go-ahead by his advisor and approved by the university for human-subjects research, Palmberg had two months to recruit participants. 

To gather his research subjects, he visited classes and gave a short speech. Afterward, students were given an opportunity to sign up and provide their emails. Palmberg recruited 86 participants this way, and each was asked to fill out an anonymous survey about their social media habits.

Palmberg explained that the main framework of his study was to gain a deeper understanding of why college students use social media and the circumstances when it can become addictive and harmful to their mental health and well-being. He also hypothesized that perceived sleep quality issues would be connected to social media addiction.

After collecting the surveys, Palmberg said, “We essentially threw the data into a big spreadsheet. We worked with it, played with it, analyzed it.” He explained that his analysis focused on motivations for social media use, “including building social connections and self-documentation.”

What Palmberg discovered was that his subjects’ most popular motivation for social media use was for entertainment. While some participants listed other motivations, he said the most “statistically significant” motivation was entertainment.

“Not only was entertainment the most highly endorsed reason to use social media in the study,” Palmberg said, “for college students it was the only motivation we analyzed that was statistically connected to social media addiction and perceived stress. The entertainment motivation was also related to poor sleep quality.”

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He found connections between a reliance on social media for entertainment and addictive behaviors, like an inability to shut down apps or put a phone away for an extended period of time. “If a person is using social media for entertainment, they are more likely to be addicted to social media than someone who is not using it for entertainment,” Palmberg said.

The structures of popular social media platforms reinforce addictive behaviors, he said. “Current trends in social media lean more toward entertainment platforms like TikTok or Instagram. People are going on there just to pass time,” Palmberg said. These brief and repetitive formats encourage addiction, he said, because the dopamine high they create is short-lived, causing users to keep visiting to get those fleetingly positive feelings. 

O’Connor supports Palmberg’s conclusions. A reliance on social media platforms for entertainment encourages addiction, she said. This is backed up by student behavior.

“My big takeaway was the interest in the entertainment variable was the key predictor of addiction. It’s not necessarily the students that are using it to communicate with each other, but the ones that say, ‘I need to kill time between classes,’ or, ‘I’m bored before bed,’ or, ‘I am trying to relieve stress after working on homework.’” The addictive aspect comes in, O’Connor said, “because users want to be entertained more and more. They are constantly looking for the next thing to talk about with their friends.”

Palmberg said he believes that not all social media use among college students has to be addictive. “It is important for people to view social media as not only something that can be harmful but also something that can be used as a tool. I like to emphasize with my study that it’s not all negative. It is more of an emphasis on moderation. It is possible to use social media responsibly. But just like almost anything, it can be addictive.”

An emphasis on digital well-being

Twice a year, in an effort to get out ahead of digital addiction, students at Gustavus Adolphus College in St. Peter are encouraged to take a deeper look at their social media use and its impact on their mental health. Charlie Potts, the college’s interim dean of students, heads the effort: It’s a clear match with his job and his research interests.

During the semiannual event, known as “Digital Well-Being Week,” Gustavus students learn about the potentially negative impact of social media overuse — as well as strategies for expanding their social networks without the help of technology.

Potts said that event has been held four times so far, and students now tell him they anticipate it. 

“We’ve gotten to the point where we get comments from students saying, ‘It’s that time again,’” he said. Students say they appreciate the information and activities associated with Digital Well-Being Week, Potts continued, and they look forward to a week focused on spending less time with their phones.

“They remember that we put baskets on every table in the dining hall with a little card encouraging them to leave their phones there and instead focus on conversations with others,” he added. “We even include  a card in the basket with conversation starters. Students are excited about it. They know the drill. It is something they like to do that feels good.”

Potts’ own academic research has focused on mental health and belonging. Each fall, he also heads up a campus-wide student survey focused on digital well-being and how to balance phone use with other aspects of mental and physical health.

In the survey, Potts said, “We ask students, ‘How much time do you spend every day on social media? How does it make you feel?’ Students are blown away when they see the number of hours that the average Gustie spends online. The vast majority are in the 4-7 hours a day on their phone range.”

The survey, which uses a motivational style of interviewing to help participants get at the root of why altering their social media behaviors may be valuable to their overall health and well-being, focuses on small changes that might reduce participants’ reliance on technology in favor of face-to-face interaction. 

“We do a lot of conversations with students about strategies they could use,” Potts said. “Things like plugging your phone in across the room while you sleep, leaving it behind while you go to work out at the rec center, subtle changes like that. We also talk about mental health and mindfulness and how…you discern your values about what you are consuming and how that might affect you.”

Though Potts said he has encountered some resistance from students (“You roll with that and help them understand the value of that and think about how they are going to make that change,” he said), he’s also heard a lot of positive student feedback about his survey — and the twice-yearly focus on digital well-being.  

“What we found with our students is they realize deep down that their relationship with their phones and social media was not having a positive impact on their life,” Potts said. “They knew change would be good but they didn’t know how to make change or who to talk to about that or what tools were at their disposal. These options help them understand how to do that.”

Andy Steiner

Andy Steiner is a Twin Cities-based writer and editor. Before becoming a full-time freelancer, she worked as senior editor at Utne Reader and editor of the Minnesota Women’s Press. Email her at  [email protected] .

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