case study on hospital management system

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• 11 Modules of Hospital Management System and their Benefits

Ever wondered what it is like to manage an entire hospital?  

It definitely sounds difficult.  

Well, if you’ve landed on this article, you know that efficiently running an entire hospital isn’t a walk in the park. Although it is essential, it gets overwhelming at times.  

Time is of the utmost importance when it comes to healthcare. Imagine the severity if there is even a minor delay or fault while sharing the results and diagnosis.  To simplify operations and efficiently manage patient records, leading hospitals use a hospital management system.  Employing hospital management software helps you reap the maximum benefits from your work.  

Before discussing in detail how you can leverage an HMS to the fullest, let us understand what an HMS is and why it is needed. 

What is a Hospital Management System?

A study showed that healthcare providers spend 35% of their time on documenting patient data. While paperwork is unavoidable in a hospital, you can automate the process and reduce the burden on the staff and doctors. Not just this, hundreds of other processes run parallel in a hospital. An HMS is a one-stop solution to manage all hospital processes and data transfer. You can use it to digitize and simplify activities like: 

• Patient record management 
• Tracking and managing appointments 
• Maintaining staff records 
• Billing and insurance claims 

Overall, an HMS helps you improve patient experience and the quality of service provided in the hospital.  At the same time it is also used to minimize operating expenses and improve the revenue cycle.  

In a nutshell, Hospital Management System (HMS) creates a frictionless approach to managing the entire hospital and solving operational complexities.  

However, HMS can be a complex system. For ease of understanding and implementation, it is divided into different modules. These modules are built depending on the needs of a department or a particular process. Let’s look at the 11 HMS modules that are essential for any hospital to improve end-to-end productivity. 

11 Essential Hospital Management System Modules

Below we have discussed the 11 hospital management system modules in the same order that a hospital would need them, according to a patient’s journey. 

1. Appointment Management   

Managing appointments manually is not only tedious but also increases the chances of human errors. Even patients are inclined to choose a hospital with an option to book appointments online. In a recent study, 68% of patient s said they would prefer to schedule, modify, or cancel appointments online.  

This hospital management system module enables you to add a scheduling option to your hospital’s website so that patients can easily schedule an appointment.  

Once your patient has booked an appointment, the HMS software for hospitals will match the patient’s illness to the doctor’s area of expertise. It will then assign them to the next available specialist or the one they prefer. It also updates the available slots in real-time to avoid any confusion at the hospital.    The next step in appointment booking is to collect medical documents. An HMS with a patient portal is used to collect documents and share the patient history with the doctors well in advance. If the patient requires assistance at his/her house, the system will check the doctors’ availability for the remote visit and allocate accordingly. In this way, you can create a smooth and error-free process by digitizing the appointment booking process.

2. Patient Management   

After the patient onboarding is completed, the patient is moved to an IPD or OPD. The patient management module of HMS caters to the needs of the inpatient and outpatient departments. It captures and stores the medical history, treatment required, details of their previous visits, upcoming appointments, reports, insurance details, and more.   

Patient management software also generates unique admissions numbers for each patient to easily manage admissions, discharges, and transfers. It also builds a comprehensive discharge summary to ensure smooth discharge. At the same time, it records and generates related documents, e.g., consent forms for electronic signature. 

When you start collecting and storing details on hospital software systems, by default you also eliminate the need to get these details on every visit. HMS enables doctors and staff to focus more on treatment than administrative work.  

Now, if you wish to automate other activities like patient communication, consider integrating your HMS with marketing automation software such as LeadSquared . It will enable you to automate communication with patients and doctors. You can send appointments and lab test reminders, or follow-ups, and build meaningful long-lasting relationships.  

3. Facility Management    

To provide a smooth experience for your patients, it is essential for your staff to have easy access to necessary hospital records. The facility management module of a healthcare management system helps you to maintain records of bed availability, occupancy status of rooms with specialized care, and more.  

Healthcare management systems collect all such information and make it readily available to your receptionist. 

If you have multiple facilities, then an HMS connects them to provide an overall picture. For example, doctors can access patient data from any hospital using an online hospital management system. Patients can visit any hospital according to their convenience, as all the records are available online. 

4. Staff Management    

The staff management module provides a concrete solution for the HR department. It contains records of your staff, job description, service domain, and other vital details.  

It helps you to know your staff without going through a heavy bundle of files. Additionally, it enables you to plan the hiring process based on the requirements of the hospital.   

5. Supply Management    

A hospital cannot afford to be short of medical supplies. Not having the medicine at the right time or a minor delay in refill can lead to severe results. The supply management component of the HMS tracks the availability of medical stocks. It helps you calibrate the minimum quantity of supplies required without any hassle. It records the purchase date, quantity consumed, and supplier details. This way, you can calculate or predict the next purchase and reorder before the stock falls short.  It also provides the details of the medicine available so that doctors can prescribe the ones in stock.   

6. Financial Management   

The financial management component of an HMS deals with the financial affairs of your hospital. It calculates, stores, and presents the billing information to the patients.   

Additionally, it also records the expenses incurred by the hospital, revenue data, and other financial details of the hospital.   

This consolidation saves you the trouble of analyzing a colossal pile of record books.

7. Insurance Management   

An HMS’ insurance management component records and stores patients’ insurance details. On requirement, it presents the policy number, insurance company, and other associated information.   

The hospital management software makes it easy to fetch these details, making insurance validation easier. 

8. Laboratory Management    

The laboratory management feature of hospital management software shows the details of various lab tests patients take. It furnishes reports when needed and maintains all records collectively.  The doctors can easily access it. It also notifies the doctor and the patients when the results are ready.

9. Report Management    

Report Management module, records and stores all the reports generated by the hospital.   

In the case of financial reports, it analyzes performance metrics to check the business profitability. It also provides a comparison between performance reports for different years. An authorized person can access these hospital management system reports whenever required. 

Furthermore, you can use healthcare dashboards to present these reports in an easy-to-read format. 

10. Vaccination Management  

A vaccination model of hospital management software keeps track of all the completed or upcoming vaccinations. It updates you about upcoming vaccinations and books a slot with the doctor. It also sends timely reminders to parents to ensure they don’t miss the slot. 

11. Support Management

Patient satisfaction is of utmost importance for any hospital. This segment records data like inquiries, complaints, requests, and feedback from patients. It also ensures that you handle these requests and problems appropriately and at the soonest. You can automate the feedback collection process to reduce the staff’s workload, and everyone could fill out the feedback form. 

If you are still thinking of whether or not to implement an HSM. To answer this let’s discuss the benefits you will observe after implementing an HMS. 

Benefits of a Hospital Management System

1. enhanced communication between the patient and the hospital.

59% of millennials are willing to switch doctors for better online access. An HMS will improve communication between patients and hospitals by allowing patients to access their medical records, book appointments, receive reminders, and communicate online with their doctors and nurses. You will have improved patient engagement, a reduction in waiting times, and increased patient satisfaction.   

2. Secured hospital data

Hospital management software must help you keep hospital data safe and secure. You can limit the access to authorized personnel only. Make sure to look for HIPAA Compliant software for PHI security.   

3. Improved access to patient data

You can have easy entry to all patient-related data on a system using an HMS. You can also access data such as patient history, doctors engaged, test results, billing information, and many more with just a few clicks.   

4. Reduced turnaround time

Streamline your hospital workflows by automating routine tasks like appointment or inventory management. This reduces the time and effort required to perform these tasks and the turnaround time. It also allows hospital staff to focus on more critical patient care areas.   

5. Cost-effectiveness

Implementing hospital management software can lead to significant cost savings for hospitals. It helps by reducing administrative overheads, improving resource allocation, and minimizing the wastage of medical supplies. An HMS can also optimize revenue streams by ensuring timely billing and reducing claim denials.    

6. Intelligent analytics with automatically generated reports  

An HMS can provide valuable insights regarding operations by generating real-time reports on various metrics, such as patient flow, occupancy rates, and revenue generation. This enables you to make data-driven decisions, improve processes, and optimize resources.    

7. Centralized administrative control

An HMS helps build a centralized platform for managing operations, allowing hospitals to streamline their administrative processes. It ensures consistency across departments. This can improve efficiency, reduce errors, and better overall patient care.   

8. Reduced medical errors

An HMS can help reduce medical errors by providing doctors and nurses with up-to-date patient information. It minimizes the risk of misdiagnosis, incorrect treatment, or adverse drug interactions.   

9. Reduced readmissions and rehospitalization rates  

An HMS can also reduce readmissions and rehospitalization rates by ensuring timely follow-ups. This improves patient outcomes and reduces the risk of complications.   

To get to know how effective a Hospital Management System can be for hospitals, let us have a look at the example of how Manipal Hospital benefited from it. 

How LeadSquared Helped Manipal Hospitals to Improve Reporting and Lead Management  

Manipal Hospitals is one of India’s largest healthcare providers, with over 27 multispecialty hospitals. They have multiple teams working together to enable a smooth patient experience.  

With a high patient volume and each team working on a different platform, keeping track of each patient’s journey and managing appointments became increasingly hard for Manipal Hospitals. They needed to centralize leads across India while securely managing patient information. LeadSquared provided an all-in-one solution integrated with their existing HIS. 

Key Results: 

• Zero Lead Leakage 
• 360° View Across Teams 
• Better Patient Management 

LeadSquared’s APIs and connectors help us collect detailed patient data and integrate it with our core HIS system. The dashboards and reports enable us to work with this data and derive great insights from it. Both these features help streamline processes, save time, and in turn boost team productivity. Kiran Ramakrishna, Assistant Manager, Manipal Hospitals

[Also read: Manipal Hospital Improves Reporting and Lead Management to know the complete story.]

Conclusion  

Hospital Management System (HMS) is essential to the delivery of modern healthcare. It can boost patient outcomes, lower medical errors, and improve the overall quality of care. It enables hospitals with a centralized platform to manage their operations, automate mundane processes, and enhance communication.   

Moreover, Healthcare CRM , when integrated with the Hospital Management System, helps you combine professional medical care with quality patient service.    

To experience the benefits of an integrated HMS and Healthcare CRM system, get in touch with our team today!  

Also read:   

• What is Healthcare CRM?    
• EHR integration with healthcare CRM software    
• Patient satisfaction survey questions   
• Healthcare CRM – A 61-question checklist to help you make the right decision  

There are generally two types of HMS, cloud-based and on-premises. A cloud-based or web-based hospital management software is hosted on the provider’s server. In contrast, on-premises hospital management software is hosted on the hospital’s private server and data centers.  A cloud-based hospital management system is more popular as it is cost-effective, and the provider can handle it remotely.

While implementing an HMS, you may face the following challenges:  1. Cybersecurity  2. Lack of technical team support  3. Complex interface  4. Higher initial implementation cost  To overcome these challenges, you need the right provider. They will ensure data security and support the implementation and staff training.  

An off-the-shelf CRM is popular as it is cost-effective and quick to implement. It is a great option for small to medium sized organizations looking for basic and essential features. Whereas a custom-built HMS provides more control over the usage and features.  

Awantika is a healthcare marketer with LeadSquared. She has been a part of the content and product marketing game for almost 3 years. You can connect with her on LinkedIn or write to her at [email protected].

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CASE STUDY OF HOSPITAL MANAGEMENT SYSTEM (HMS

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Experiences of implementing hospital management information system (HMIS) at a tertiary care hospital, India

Vilakshan - XIMB Journal of Management

ISSN : 0973-1954

Article publication date: 19 November 2021

Issue publication date: 2 February 2023

Mumbai needs to be transformed into a world-class city as stated in the 2005–2025 development plan of Municipal Corporation. For this initiative, hospital management information system (HMIS) has to be implemented across 400+ health facilities in the city.

Design/methodology/approach

A case study methodology was adopted to study HMIS implementation. Wave 1 of Phase 1 implementation of HMIS is carried out as a pilot project at Film City’s Hospital, Mumbai, which “go-live” on 21st June 2018. The work for hardware and software implementation was awarded to HardSystems and Solutions Limited and SoftSolutions India Private Limited, respectively, through e-tender.

Provision of inadequate quantity of hardware, slowness of network or system, non-satisfactory training after observation confirmation and sign-off process, lack of data entry operators, mismatch in numbering systems in blood bank and many other challenges concerned with the specific departments had become a major impediment in the efforts to maximize number of patients registered into HMIS.

Practical implications

Even after providing many clinical and managerial benefits, being the first cloud-based centrally located HMIS in any of the hospitals in the city, it imposes a major challenge for the management in terms of resistance of employees toward technology and need for the adoption of theoretical models for implementing change for the overall organizational development.

Originality/value

To the best of the authors’ knowledge, no other teaching case study is conducted to study the HMIS implementation in large-scale public health-care services. This is a dummy case study for teaching exercises. The identity of the stakeholders, organizations and events has been masked to maintain confidentiality.

• Change management
• Organizational development
• Health-care services management
• Hospital management information systems
• Pilot project

Arora, L. and Ikbal, F. (2023), "Experiences of implementing hospital management information system (HMIS) at a tertiary care hospital, India", Vilakshan - XIMB Journal of Management , Vol. 20 No. 1, pp. 59-81. https://doi.org/10.1108/XJM-09-2020-0111

Emerald Publishing Limited

Copyright © 2021, Lakshya Arora and Feroz Ikbal.

Published in Vilakshan – XIMB Journal of Management . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode

It was a dewy evening of Mumbai in July 2018 and a biscuit falls into the hot coffee which Medical Superintendent of Film City’s Hospital, Mumbai was dunking into his huge vintage cup.

Deputy Medical Superintendent and hospital management information system (HMIS) Nodal Officer at Film City’s Hospital bought a letter sent by one of the Heads of a Clinical Department to his office. It was mentioned in the letter that in most domains of the HMIS, the providers have not completed the modules and required integration which they have requested them to make as per the departments’ clinical and documentation requirements. The letter added that the training team was helping them only with cursory skills which they could learn by themselves once the modules would be effectively designed and given. Hence, the representatives of SoftSolutions India Private Limited were suggested to be called in a meeting along with Heads of all the Departments and other users of the system in the presence of Medical Superintendent and Director to avoid financial losses to the health-care system.

The Deputy Medical Superintendent and the HMIS Nodal Officer discussed with the Medical Superintendent that it was only one among many letters received by HOD of many departments of the hospital where HMIS was implemented as a pilot project by the Director in the past few months.

Informatics involves information acquisition, organization, validation, storage, retrieval, integration, analysis, communication and presentation, using IT as a key resource ( Lifshitz et al. , 2007 ; Sinard, 2006 ). HMIS is defined as the “computer system designed to ease the management of all the hospital’s medical and administrative information and to improve the quality of healthcare” ( Degoulet and Fieschi, 1997 ). An EHR system comprises “the longitudinal collection of electronic health information for and about persons, where health information is defined as information pertaining to the health of an individual or health care provided to an individual. Critical building blocks of an EHR system are the electronic health records (EHR) maintained by provider…and by individuals” ( National Institutes of Health, 2003 ).

At present, most of the Indian hospitals are adopting HMIS as a way of automation and digitalization of their health-care records.

Film City’s Hospital, Mumbai

Bombay, the very first possession of Britishers in India, came to King Charles II of England in 1661, when he married the Portuguese queen, as part of the royal dowry. Through Corporation Resolution No. 512 which was dated August 12, 1996 under Maharashtra Act, XXV, 1996, the name “Bombay” has been changed as “Mumbai.”

Greater Mumbai is presently a metropolitan aggregation of around 18 million residents (world’s six largest and largest in India). The port city accounts for most of foreign trade in India as well as government revenues, being one of the major hubs for education, research, development and technology in India ( MCGM, 2019 ).

The Film City’s Hospital situated in the heart of Mumbai is a 1,000-bedded tertiary care facility with around 30 clinical departments where every year more than 55,400 patients are admitted and more than 280,000 patients (new and old) are treated in out-patient department. More than 21,000 operations (major and minor) are performed and 4,200 deliveries are done every year.

In addition to the routine medical services, it also offers various super-specialty services in nephrology, neurosurgery, endocrinology, gastroenterology, cardiology and cardiac vascular and thoracic surgery. This hospital has well-equipped intensive care units for medical, surgical, cardiac and neonate patients. The hospital has its own blood bank and component therapy unit, which provides services round the clock. A whole body CT scanner, cardiac catheterization system and spect camera, etc. are also installed at the hospital. It also has independent hyperbaric oxygen therapy chambers.

The hospital levies fees from the patients at subsidies rate and efforts are made to provide the best and excellent patient care ( MCGM Health Department, 2019 ).

Why hospital management information system…?

India’s 12th 5-year plan highlights the need to improve HMIS throughout the nation and a possible investment in health IT in the public health system (Twelfth Five year Plan Draft 2012, 2017). Multiple findings have reported the advantages of HMIS implementation ( Hillestad R et al. , 2005 ; Wang et al. , 2003 ; Frisse and Holmes, 2007 ; Shekelle et al. , 2006 ).

HMIS is considered to be the most promising instrument to improve the overall efficiency, safety and efficacy of the health service (Basit et al. , 2006). Wide and effective use of HMIS improves the quality of health care ( Frere, 1987 ); minimize adverse events; reduce the cost of medical care ( Lun, 1995 ); increase administrative productivity improvements ( Kuruvilla et al. , 2004 ); reduce documentation as well as enhance access to affordable treatment (Basit et al. , 2006; Yasnoff et al. , 2000 ).

Municipal Corporation aspires Mumbai to be transformed into a millennium and world-class city as stated in the development plan 2005–2025. For this to happen, Mumbai requires to be distinguished about the quality of life aspect by improving the quality of citizen welfare services. As part of this initiative, the HMIS has to be implemented across 400+ health facilities across the city.

There is the availability of digital access original data through HMIS which can be used as a strong tool in the decision support system for the Film City’s Hospital management. The HMIS data can be used for analysis as well as for forecasting purposes. The electronic medical records (EMRs) as well as picture archiving and communication system (PACS) generated can be of great use for the clinical purposes for better diagnosis and treatment. The HMIS data can also be used for drug calculations and better scientific inventory management practices at the hospital.

Hospital management information system implementation at Film City’s Hospital

Literature have shown that implementation and improvement in HMIS to guide policy and management decisions has found essential space in countries such as Peru, Tanzania, Solomon Islands, Caribbean, Lesotho, Honduras, India (Uttar Pradesh) and Kryragya Republic (World Bank Reports , 1993 , 1999, 2000, 2001; Commission on Health Research for Development, 1990 ).

The work of software implementation and post-implementation of HMIS in the film city covering 4 major hospitals, 1 dental hospital, 18 peripheral hospitals, 5 specialty hospitals, 28 maternity homes, 161 dispensaries and 183 health posts was awarded to SoftSolutions India Private Limited.

As per the directives, Wave 1 of Phase 1 implementation of HMIS is carried out at Film City’s Hospital as a pilot project. Wave 2 of Phase 1 was planned to be implemented at other three major hospitals in the city and thereafter at balance health-care locations ( Mukul, 2018 ).

It was decided to form a committee to commence the viability and feasibility of Wi-Fi services project at Film City’s Hospital and the standing committee sanction was received for awarding the work for hardware and network implementation at the Phase I Hospitals and the LOI of worth Rs. 50+ crores for the prestigious project is issued to HardSystems and Solutions Limited. Further, a pilot implementation is planned to be carried out at few departments in Film City’s Hospital which “go-live” on 21st June 2018 ( MCGM IT Department, 2019 ).

Bid document for hospital management information system

The HMIS software pilot project at Film City’s Hospital was awarded to SoftSolutions Private Limited through evaluation of technical and commercial bids by e-tender process, initiated in July 2016. SoftSolutions, also as part of their scope, conducted a site survey for hardware infrastructure for all health-care institutions in the city. The exact quantity and minimum specifications for various hardware and infrastructure have been provided by SoftSolutions post site survey.

The purpose of this bid document is to select an agency for not only the supply but also the hardware and network components’ installation, testing, commissioning and maintenance for the health institutions.

A Bid Evaluation Committee (BEC) was appointed to examine and assess the submitted technical as well as commercial bids. The BEC reviewed the bids to decide if they’re really complete, able to respond and if the bid format complies with the bid specifications. In a bid that does not represent a material variance, it was waived for any informality or nonconformity and the bidder with the lowest cost submitted (L1 rate) in the commercial bid opening was awarded the contract.

Submission of inception report.

Supply, installation and commission of various hardware and network components along with required accessories at health institution.

Undertake required passive structured cabling (including patch chord, faceplate with input/output connector, laying of LAN and fiber cable (if required) with proper labeling, testing certificate and others).

The device should be tested before mass-installation (operating system compatibility, software, drivers, etc.).

The supplier should take care of all installation and support issues that are faced by the end-user, for all hardware and software supplied as part of the purchase order. This would include installation and support for security functions, user configuration, LAN configuration, etc.

Addition of a desktop PC to the security device is to be done by the implementation agency.

In-warranty annual technical support for hardware and network components services for a period of five years.

The following are additional points for the scope of the implementation agency:

The Wi-Fi/network device shall be connected to the local area network.

The supplier shall disable unnecessary services, protocols and ports.

When installing software, ensure that only required software is installed and the latest versions of all software including all recommended security patches are updated.

Disable or remove redundant software/services (including program, machine utilities and network services).

Pre-requisites for hospital management information system pilot project

The Assistant Medical Officer (AMO) of the hospital was appointed as the HMIS implementation nodal person from Film City’s Hospital for coordinating with the internet service provider and hardware supplier appointed by HardSystems and Solutions Limited, implementation of software by SoftSolutions and coordinating with various departments for providing solutions to any challenges faced.

Site readiness – the representative of SoftSolutions visited each department of the hospital for finalizing the network points, in consultation with the Head of Departments (HODs).

For the implementation of HMIS, one server room and one room for hardware and software support staff for the hospital and UPS room per building were identified and subsequently handed over to HardSystems and Solutions Limited, as per the specification ( The Hindu, 2018 ).

The support staff room was used by HardSystems and Solutions Limited for storing the equipment during the installation.

The civil work, if any, required for the network installation, server room and UPS room readiness was carried out by the Hospital Assistant Engineer (AE), Civil Department.

The furniture, if any, required for the HMIS hardware was identified and procurement was carried out by the Hospital M&E department.

The electrical work for HMIS implementation was carried out through the Chief Engineer (M&E) department. The concerned M&E engineer from the hospital coordinated with the representative of SoftSolutions and HardSystems and Solutions Limited.

Hardware and software implementation

As the number of patients was increasing in the waiting areas of the clinical departments, Deputy Medical Superintendent took a round with HMIS nodal officer to locate any patient-free area or store rooms in IPD building.

After the functional requirement study and the hardware survey did from June to September, 2016, the Digital Laboratory and Security room on ground floor of IPD building of the Film City’s Hospital was allotted for hardware storage. A 24 × 7 helpdesk was also created to give instant solutions to the arising issues in the software or hardware.

As per directives, 200 customized portable computer trolleys (to be used for computer-on-wheels) were provided as per the requirements and storage area in the departments.

Under Software Research Survey (SRS) up to September 2016, software customization for medical specialties was done after studying the workflow of major hospitals of Mumbai, for surgical specialties, radiology and central sterile services. Sub-committees were formed in each of these hospitals to monitor the process of customization of software, and sessions to sensitize nursing staff, technicians, pharmacists, registration attendants, etc. were conducted across all the hospitals. Weekly or sometimes fortnightly review meetings were held at the Film City’s Hospital. Also, various teams visited multiple public, private and trust hospitals across the city to study already existing HMIS implemented in these hospitals ( MCGM IT Department, 2019 ).

User acceptance tests and finalization of hospital management information system modules

Documented literature suggests that the degree of end-user satisfaction is a pivotal factor of an information system’s success ( Bailey and Pearson, 1983 ). Many other studies have stressed the significance of levels of end-user satisfaction ( Doll and Torkzadeh, 1988 ; DeLone and McLean, 1992 ).

During the user acceptance test-1 (UAT-1), there were 517 observations noted in module testing which was carried out up to March 21, 2017, by the doctors and other representatives.

Thereafter, in April 2017, a UAT observation confirmation process (also known as system requirement specification reconfirmation) was carried out by SoftSolutions with representatives from various health-care facilities who were assigned for each module so as to prepare SRS 1.1 with more precise information and requirement to aid the development of HMIS.

With reference to the OPD module, about 318 proformas from 29 departments were handed over to SoftSolutions on 9th June 2017 for developing the EMR for the OPD module. Considering each proforma was unique and also an easy-to-use system is to be developed, SoftSolutions has developed a solution and the same was shown to a team of doctors of each department concerned with the OPD module to check the functionality and provide their inputs for the same, so that the precise requirement can be incorporated in the SRS 1.1.

Further, SoftSolutions have documented the information provided during and after the UAT 1 and UAT/SRS reconfirmation in the latest SRS version 1.1 and the same was ascertained by the team of representatives who had provided the information during the UAT/SRS reconfirmation and corrected the same if necessary and provided the sign-off for the respective module SRS 1.1. On completion of the activity, UAT-2 (inter-module) and thereafter UAT-3 (integrated) were planned to be conducted.

On the basis of all the three UAT and UAT observation confirmation processes conducted for different modules, there were a number of change requests made by concerned HODs/departments which after approval from nodal officers were incorporated through some policy decisions for requirements which were taken by the administration.

It was finalized by the management that the short message service (SMS) would be used for registration and inpatient referral only. It is not necessary to send SMS for every activity. For easy workflow of IT services, digital signatures were assigned for important decisions, for legal, medico-legal cases, birth and death certificates.

Recruitment of data entry operators and training of hospital staffs

Deployment of data entry operators (DEOs) for assisting the hospital staff related to the implementation of HMIS was done through prescribed norms of recruitment for different departments for three working shifts.

The training was well planned by a team of SoftSolutions and all the requirements including space and other resources were allocated. Training was done in two parts, which involved orientation lectures and hands-on session conducted in the first and second weeks of February 2018, respectively.

It was decided to use India’s first indigenous Web-based PACS Medsynapse for training doctors and staff of radiology department. It is developed on advanced technologies and provided a full range of features and tools for image processing, distribution and archival. It is very user-friendly, scalable and affordable PACS with more than 20,000 installations in 40 countries.

A training completion certificate on specific HMIS module was awarded to each employee after successful completion of training.

For the purpose of logging into HMIS computers and application, employee’s ID-based default login and password systems were generated, which were later allowed to reset by the users. Thus, all the resident doctors and other staff got access to the HMIS system.

An HMIS refreshment training with proper consultation with Team SoftSolutions was provided once again in October 2018 after proper implementation of all the 32 modules in the system.

Dry run and go-live

A dry run was conducted in the selected clinical and supportive services departments of Film City’s Hospital in Phase 1 from April to June 2018. After the required improvements needed the pilot project “go-live” for Phase 1 of Wave 1 from 21st June 2018 ( MCGM RTI, 2019 ).

Overcoming hospital management information system challenges

Provision of an inadequate quantity of hardware either because of lack of storage space or because of unavailability of furniture and computer trolleys had become a major impediment in the efforts to maximize the number of patients registered into HMIS at Film City’s Hospital, e.g. super-specialties such as nephrology and gastroenterology have an average outpatient load of around 100–150 patients per OPD. But only three computers have been provided for doctors and one for the nursing staff in the OPD of super-specialties.

Because of the slowness of the network or the system, particularly after 11:00 a.m., patients are inconvenienced as they have to wait for long periods till the EMRs are filled and prescriptions and laboratory/radiology requisitions are generated. At times, patients are reluctant to wait for the procedure to be completed. Consequently, only a few requisitions of laboratory and radiology investigations had been processed through the system. It was decided to put more LAN cables but when the issues persist, new Wi-Fi dongles were thought to be procured for every department in the future ( DNA, 2019 ).

Also, a major challenge is that integration of HMIS with various government and insurance schemes is to be undertaken and also a separate budget is to be allocated for HMIS consumables.

HMIS Nodal Officer conducted an immediate evaluation and the following challenges were reported to be faced by some important clinical and supportive services departments.

Department of gastroenterology

One of the issues of the gastroenterology was that all the hospitals in the film city were using different systems for capturing endoscopy reports. Also other investigations such as manometry, PH, fibroscan and breath hydrogen were intended to be managed well so that different reports and PDF can be uploaded in HMIS. The report’s structure given in HMIS was discussed with concerned IT team to check for the network link to the system.

Department of psychiatry

As soon as the recreational activities started for the admitted patients, the HOD of Psychiatry Department entered the IPD area. HMIS Nodal Officer was waiting for him to ask for required modifications.

He said, “Wires need to be covered to protect against damage by the psychiatric patients. Sub-departments like Psychology, Social worker and EEG are also to be included in the system.” HMIS Nodal Officer carefully noted the desired changes. When inquired about the psychiatric OPD, implementation of electronic queue management system monitor was suggested.

Pediatrics department

On meeting with the Professor of Pediatrics while he was checking the nutritional chart for a three-year-old child, the Nodal Officer asks her to raise the concerns regarding HMIS implementation. She swiftly enumerated that the weight, age and height data have to be integrated for making relevant WHO charts and growth curves for classifying patients with severe acute malnutrition or moderate acute malnutrition. She added, immunization record is also to be included in IPD paper. If a vaccine is missing as per national immunization program, a warning has to come on the system. Automatic calculation of surface area is required for prescribing certain drugs. Integration with certain government schemes is also required.

Opening her smart tablet, the HMIS Nodal Officer checked the relevant schemes available in the Film City’s Hospital and asked, “Should Janani Suraksha Scheme also be integrated?” for which she got the affirmative response.

Professor of Pediatrics explained to the Nodal Officer that daily reporting/monthly data have to be available disaggregated in terms of age, gender, notifiable diseases and monsoon-related illness. In addition, the multiple diagnoses have to get sited separately because they are not mutually exclusive. Also, referral list has to be made comprehensive to include physiotherapy, occupational therapy, dietetics and speech therapy in addition to clinical/lab departments.

Radiology department

With the use of Digital Imaging and Communications in Medicine standard and Health Level 7 communication protocol, vendors communicate with the radiology imaging management system termed PACS. Undoubtedly, a major concern in radiology department is to combine the images of each analysis with other important patient records and enhance interoperability with radiology information system and HMIS ( Cummings, 1995 ; Offenmuller, 1997 ).

According to recommendations of PACS Support Engineer given to HMIS Nodal Officer of Film City’s Hospital, “open office” does not support PACS reporting. In addition, the automatic transfer of stored images from USG machine to HMIS was not taking place. Therefore, the HOD of Radiology requested that the licensed access to 3D-MPR viewing be provided to all the radiology employees, including CT/MRI technicians. Furthermore, with the view of additional CT and MRI machines being instilled with additional workload in the near future, approximately 70 licensed accesses needed to be made available to increase the ease, efficiency and speed of reporting. The licensed MS office is also preferred to maintain the integrity and uniformity of the departmental work.

Also, while reporting the patient on PACS, considerable time was consumed in logging in as well as in opening a particular patient. It was difficult to interpret whether the slowness could be attributed to the slow speed of the network or slowness of the operating software.

In addition to this, there was the need for early integration of revenue counter and the central laboratory with the HMIS system for the better functioning.

Laboratory and diagnostics services

Diagnostics is a data-intensive specialty, and laboratory data is often used in addition to patient services to record continuous improvement, performance management, outcome analyses and research studies ( Cowan, 2005 ; Young, 2000 ). At the center of most laboratory activities is the laboratory information system. Workflow management, specimen monitoring, data entry and reporting, regulatory enforcement assistance, code acquisition, interfacing with several other applications, archiving, inventory management and provision of billing information are its features (Eleveitch and Spackman, 2001; Pearson et al. , 2006 ).

For appointment generation counter: token generation facility for the same-day blood collection of patients has to be incorporated in the system. For the token generation, a fast printer device was required as a large number of patients need to be handed over in a short period of time.

For labeling counter

Quality of bar code labels need to be improved. Printouts sometimes are not readable and may face problem in scanning. The problem was discussed with the Project Director, HMIS.

Consumables such as printer roll, appropriate sized labels are not easily available in the hospital.

For collection table: It was discussed with the IT in charge, SoftSolutions, that wall-mounted all-in-one PC units with bar code scanner facility or tablets with in-built scanner need to be installed in OPD for scanning the collected blood samples.

Blood sample processing: Appropriate diagnostic equipment such as blood cell counter and automated biochemistry analyzer have to be procured, which can be integrated with HMIS.

Blood bank services

The blood bank system consists of an autonomous blood center responsible for human blood procurement, storage and distribution ( Li et al. , 2007 ). Because blood bank services are vital segment of the Film City’s Hospital and there were major concerns raised by the employees in the department, Medical Superintendent called for an urgent board meeting ( Tables 1 ).

A unique number was given to each blood bag in the blood bank. This number is followed through the life of that blood bag, i.e. the same number applies at blood group, serological tests, stock taking, cross-matching and issue of blood bag to patients. As on 30th July 2018, the blood bank numbers were at “Indoor 905,” “Outdoor 9888” and “Brought from i.e. BF 1186.”

The HMIS data entries in Blood Bank were attempted since 26th July 2018; however, the HMIS software is unable to match the actual bag numbers because it begins by default 001, 002, 003, etc. Because of this error, the outdoor bag number 8434 may be entered in HMIS as bag number 0004, indoor bag number 894 entered in HMIS as bag number 0005 and so on.

This numbering system, if continued, could have created utter chaos at all levels. Online bloodstock will show wrong bag numbers available to technicians for a cross-match. Issued bags will not correspond to the actual blood bag issued, thus resulting in confusion at a blood bank and clinician level.

In addition, serious mistakes in identifying and discarding of seropositive bags (HIV, Hepatitis B, etc.) can occur because of an incorrect numbering system.

Given the sensitive nature of blood bank work, the slightest error in numbering can cause disastrous results for the patient’s life. Any kind of dual numbering system, as suggested by the HMIS technical team, will further compound the problem, double the workload and invite severe adverse remarks from the FDA.

Because Film City’s Hospital is stationed for the pilot study, any errors can get carried forward and adversely affect the working of other hospitals and other blood banks too. In view of this serious medico-legal and ethical implications, it is essential that HMIS number entries have to categorically match with available numbering for blood bags.

Pharmacy prescriptions and dispensary services

In outpatient health care, the drug management process is a multifaceted relationship between patients, prescribers and pharmacists, which is also enabled by HMIS ( Tamblyn, 2004 ). Electronic medication management has the ability to allow a secure process, but errors may also be created ( Bates et al. , 2001 ).

At Film City’s Hospital, after consultation with head pharmacist, HMIS Nodal Officer noted that a standard prescription format should include name of the drug, preparation, strength, dose, route of administration, frequency and number of days. The route of drug administration should be comprehensive and must also include intradermal, intra-thecal and intra-ocular routes.

It was recommended that the prescriptions need to be in terms of both generic and brand names. Allergies must be a mandatory field, which needs to be pop out during prescriptions. Starting and end dates should be integrated especially for drugs with progressive decreasing doses. At once, no medicines should be prescribed for more than one month.

It was suggested to improvise the SAP system, based on the positive features of government’s “e-Aushadi program” which includes:

Need for surplus and shortage alerts.

Rigorous quality control of medicines should be mandatory and built-in using impaneled NABL-accredited laboratory.

Achieving the milestones

The HMIS is being implemented to improve the quality and responsiveness of health-care services in health-care network in the film city ( Tables 2 and 3 ).

Features of hospital management information system implementation at Film City’s Hospital

The unique features of the HMIS system at Film City’s Hospital are that this system is first of its kind in any of the city’s hospitals that uses a cloud-based centrally located system in which as much as 32 clinical and supportive services HMIS modules are covered. It is made possible to achieve inter-departmental and intra-departmental connectivity in Film City’s Hospital through this system. In addition, this cloud-based system also allows central access to data through any city’s health-care systems, thus enhancing inter-hospitals connectivity ( MCGM RTI, 2019 ).

Hospital management information system implementation – the road ahead

There is a lack of DEOs in some departments. To enhance the time and cost-effectiveness and to achieve digitization through increasing reach to more number of patients, it was decided to implement “Speech to Text” software in the OPDs based on the principle of “machine learning.” The SoftSolutions team has already started taking voice samples of the doctors in the OPDs, and to test the effectiveness of the software, the trial run has been started in the Psychiatry and General Medicine OPD of Film City’s Hospital.

Also, at the registration department, issue of digitalized health card to every patient with Unique Hospital Identification Number and bar coding on it has been started. In the future, the bar scanners will be incorporated to save time at various points in the hospital.

Most of the users are still very resistant in the use of technology in the hospital as they are adapted to traditional manual data entry and calculation methods. The percentage of EMR completion still has to be improved.

Deputy Medical Superintendent along with the HMIS Nodal Officer discussed with the Medical Superintendent, Film City’s Hospital that there is a need for adoption of “John Kotter’s Eight-Step Plan” for implementing change for user acceptability for the overall organizational development and to reinforce the future dream which she had seen of digitalized health-care systems in digitalized India.

Several studies on implementation of HMIS in developed countries ( Ash et al. , 2003 ; Ball, 2003 ; Berg, 2001 ; Benson, 2002 ; Little Johns et al. , 2003 , Joel Rodrigues, 2009 ; Lippeveld et al. , 1992 ; Dudeck et al. , 1997 ) had reported various challenges, including those in managing infrastructure, integration, inter-departmental issues, technical requirements, data and software issues, end-user contribution, standardization of terminologies, training needs and ignorance of hospital administration. In developing nations, numerous health-care professionals associate information systems with filling of infinite registers, collecting information and submitting reports without sufficient input, making HMIS “data-driven” instead of “action-driven” ( Sandiford et al. , 1992 ; Smith et al. , 1988 ). Similarly, in this case study, although being an Indian hospital, managing infrastructure in terms of space for computers, trolleys and other accessories became a major challenge. Allocating areas for installing LAN and rooms for information technologist in a crowded hospital was not that easy task. In this case study, the hospital also faced inter-departmental and inter-hospitals issues with respect to integration and standardization of clinical domains and report structures, respectively. Even after adopting the HMIS principles in several trainings, many employees, especially elder age, felt the need for technical assistance. In addition, the poor doctor–patient ratio and the downtime of the server made the work more complicated as in some of the departments, employees started doing dual entries (both in register and computer) to prevent loss of any data.

Several issues have been identified in the review of reports and studies in low-income countries ( Gladwin, 1999 ), such as general organizational and management difficulties ( Campbell et al. , 1996 ; Braa et al. , 1997 ; Azubuike and Ehiri, 1999 ); data acquisition and processing concerns ( Robey and Lee, 1990 ; Jayasuiriya, 1999 ; Lippeveld et al. , 2000 ); inadequate use of information (WHO, 1994b, 1999; Braa et al. , 1997 ); over-reliance on epidemiological data or specific surveys ( Husein et al. , 1993 ; Sapirie and Orzeszyna, 1995 ); and paucity of an integrated information strategy for the organization ( Van Der Lei et al. , 1993 ). In a similar way, in this case study also, many departments in the hospitals faced challenges around complexity, inconsistency and poor integrity of the system. Although the management tried to ensure the effectiveness, incidents such as mismatch in blood bag numbering in HMIS posed a major ethical issue. There were multiple concerns around data acquisition at revenue and cost centers of the hospital. Although management took corrective and preventive actions, it was reflective of a strategy which would have been well integrated prior with clinical understanding and principles of change management.

Several studies have been conducted on interface design methodologies ( Shearer et al. , 1997 ; Arreola et al. , 1997 ), and among the unidirectional, bidirectional and integrated workstations ( Levine, 1990 ), the interface with more consistent information base is most preferred ( Veader, 1997 ). Studies have reported that an integrated radiology network enhances the efficacy of physicians, minimizes costs, decreases the amount of repetitive or unnecessary tests and increases the quality of care ( Gibby and Mciff, 1997 ). In addition, owing to the extensive adoption of electronic radiology reporting systems, filmless radiology systems and speech recognition, there have also been considerable radiology workflow efficiency improvements ( Mariani et al. , 2006 ; Gay et al. , 2002 ; White, 2005 ; Ralston et al. , 2004 ). Similarly, in this case study, it was observed that with administrative efforts and understanding employee training needs, the number of repetitive tests was reduced. There was a direct benefit in lowering turnaround time and publishing more reports. The better integration and consistency of the PACS will help in increasing the profit per unit volume for the radiology department.

HMIS is important in its ability to resolve issues such as increasing laboratory volume with outreach programs; intensified EMRs integration; and the subsequent need to combine fragmented information systems, laboratory resource shortages, patient safety, cost control, central control of subspecialties, rising demand for laboratory diagnostics and customized intervention ( Becich et al. , 2004 ; Sinard and Morrow, 2001 ). In this case study also, HMIS-integrated EMR played a significant role in decreasing the average waiting time for the patients for receiving the laboratory reports.

Child clinicians frequently feel that there is little utility of health information systems in pediatrics because they tend to be structured for adult services ( Johnson, 2001 ). There are several functional areas, such as immunization records ( Smith, 1988 ), growth monitoring ( Rosenbloom et al. , 2006 ), drug dosing ( American Academy of Pediatrics, 2004 ), patient recognition ( Kuther, 2003 ) and decision support systems ( Miller et al. , 2001 ), which are so vital to the treatment of children and adolescents that their omission contributes to the system hindering quality pediatric care. In this case study, with the discussion with HMIS Nodal Officer, the pediatric department was able to design a customized module which had unique characteristics as compared to any adult-based systems. Drug dosage and calculations, immunizations and growth-monitoring systems were integrated successfully.

Literatures have shown that implementation of computerized blood bank inventory and emergency services ( Catassi and Petersen, 1967 ) and blood bag system ( Ali et al. , 2017 ) plays a significant part in hospital’s decision-making systems ( Li et al. , 2008 ). Similar results were observed in this case study also.

Mohapatra (2009) notes that combining in-patient, pathological and inventory management of hospital pharmaceutical stores enables to enhance the quality of service and efficiency while reducing operating costs. This economic benefits can be reflected in the price, which gives customers more good value. The use of HMIS has been proposed as a way to minimize prescription errors by increasing the readability, standardization and availability of information or providing automatic controls for possible drug-related issues, but the findings are inconsistent ( Huckvale et al. , 2010 ; McKibbon et al. , 2011 , 2012 ). In this case study, the findings suggested that the use of HMIS was helpful in inventory management once the employee got well trained in inventory modules and it generated profitability for the hospital.

Literature shows that during the process of automation, important performance variables involved in the phase of change management are organizational structure, technology infrastructure and implementation approach ( Galliers and Sutherland, 1991 ; Lubitz and Wickramasinghe, 2006 ; Nolan, Norton and CO, 1992 ). Emergent philosophy is more complex ( Markus and Robey, 1988 ) than imperative perspectives ( Robey and Boudreau, 1999 ), stressing a reciprocal instead of a one-way relationship involving technology and organization. Findings of this case study suggest that the management should have strategically thought about the change management perspectives in a visionary sense before taking the step for HMIS implementation. Most of the elder employees were resistant to change and found the system more complex. In terms of ease of use of HMIS, more than half of the employees were either neutral or disagreed in their responses.

Mumbai city map

Picture showing patient health card with UHID and bar coding

Discussion in the meeting conducted at Medical Superintendent’s office, Film City’s Hospital between authorities and the users on the HMIS challenges of blood bank

Progress of HMIS implementation at Film City’s Hospital up to February 2019

Digitization through electronic medical records (EMRs) at Film City’s Hospital

Table showing distribution of customized computer trolleys at Film City’s Hospital

Changes in key performance indicators (KPIs) at Film City’s Hospital after HMIS implementation

Average time spent per service

Average gain per unit volume of the services

Employees ( n = 75) responses for HMIS

Ali , R.S. , Hafez , T.F. , Ali , A.B. and Abd-Alsabour , N. ( 2017 ), “ Blood bag: a web application to manage all blood donation and transfusion processes ”, Paper presented at the 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) .

American Academy of Pediatrics ( 2004 ), “ Committee on fetus and newborn. Age terminology during the perinatal period. Pediatrics ”, Vol. 114 , pp. 1362 - 1364 .

Arreola , M. , Neiman , H.L. , Sugarman , A. , et al. ( 1997 ), “ Implementation of a radiology electronic imaging network: the community teaching hospital experience ”, Journal of Digital Imaging , Vol. 10 No. S1 , pp. 146 - 149 .

Ash , J.S. , Gorman , P.N. , Lavelle , M. , et al. , ( 2003 ), “ A Cross-Site qualitative study of physician order entry ”, Journal of American Medical Informatics Association ,

Azubuike , M.C. and Ehiri , J.E. ( 1999 ), “ Health information systems in developing countries: benefits, problems and prospects ”, Journal of the Royal Society for the Promotion of Health , Vol. 119 No. 3 , pp. 180 - 184 .

Bailey , J.E. and Pearson , S.W. ( 1983 ), “ Development of a tool for measuring and analyzing computer user satisfaction ”, Management Science , Vol. 29 No. 5 , pp. 530 - 545 .

Ball , M.J. ( 2003 ), “ Hospital information systems: Perspectives on problems and prospects ”, International Journal of Medical Informatics , Vol. 69 Nos 2/3 .

Bates , D.W. , Cohen , M. , Leape , L.L. , Overhage , J.M. , Shabot , M.M. and Sheridan , T. ( 2001 ), “ White paper – reducing the frequency of errors in medicine using information technology ”, Journal of the American Medical Informatics Association , Vol. 8 No. 4 , pp. 299 - 308 .

Benson , T. ( 2002 ), “ Why general practitioners use computers and hospital physicians do not – part 1: Incentives ”, British Medical Journal ,

Berg , M. ( 2001 ), “ Implementing information systems in health care organizations: myths and challenges ”, International Journal of Medical Informatics , Vol. 64 No. 2-3 .

Becich , M.J. , Gilbertson , J.R. , Gupta , D. , et al. ( 2004 ), “ Pathology and patient safety: the critical role of pathology informatics in error reduction and quality initiatives ”, Clinics in Laboratory Medicine , Vol. 24 No. 4 , pp. 913 - 943 .

Braa , J. , Heywood , A. and Shung King , M. ( 1997 ), “ District level information systems: two cases from South Africa ”, Methods of Information in Medicine , Vol. 36 No. 02 , pp. 115 - 121 .

Campbell , B. , Adjei , S. and Heywood , A. ( 1996 ), From Data to Decision Making in Health: The Evolution of a Health Management Information System , Royal Tropical Institute , Amsterdam .

Catassi , C.A. and Petersen , E.L. ( 1967 ), “ The blood inventory control SystemHelping blood bank management through computerized inventory control ”, Transfusion , Vol. 7 No. 1 , p. 196 .

Commission on Health Research for Development ( 1990 ), Health Research: essential Link to Equity in Development , Oxford University Press , New York, NY .

Cowan , D.F. ( 2005 ), “ Laboratory informatics and the laboratory information system ”, in Cowan , D.F. (Ed.), Informatics for the Clinical Laboratory , Springer , New York, NY , p. 1 - 20 .

Cummings , B.A. ( 1995 ), “ RIS integration strengthens PACS. PACS under managed care ”, Diagn Imaging , Vol. 17 , pp. 13 - 14 .

Degoulet , P. and Fieschi , M. ( 1997 ), Introduction to Clinical Informatics , Springer , New York, NY .

DeLone , W. and McLean , E. ( 1992 ), “ Information systems success: the quest for the dependent variable ”, Information Systems Research , Vol. 3 No. 1 , pp. 60 - 95 .

DNA ( 2019 ), “ HMIS delayed: Mumbaikars won't get rid of hospital queues soon ”, available at: www.dnaindia.com/mumbai/report-hmis-delayed-mumbaikars-won-t-get-rid-of-hospital-queues-soon-2710654 (accessed 7 March 2019 ).

Doll , W.J. and Torkzadeh , G. ( 1988 ), “ The measurement of end-user computing satisfaction ”, MIS Quarterly , Vol. 12 No. 2 , pp. 259 - 274 .

Eleveitch , F.R. and ( 2001 ), Spackman , KA. “ Clinical laboratory informatics ”, in Burtis , C.A. and Ashwood , E.R. (Eds), ( 2001 ), Tietz Fundamentals of Clinical Chemistry , 5th ed. , WB Saunders Company , pp. 262 - 271 .

Frere , J.J. ( 1987 ), Health and Management Information System for Child Survival Project in Pakistan , Technologies for Primary Health Care Project, United States Agency for International Development , Washington, DC , 1 - 23 .

Frisse , M.E. and Holmes , R.L. ( 2007 ), “ Estimated fi nancial savings associated with health information exchange and ambulatory care referral ”, Journal of Biomedical Informatics , Vol. 40 No. 6 , pp. S27 - 32 .

Galliers , R.D. and Sutherland , A.R. ( 1991 ), “ Information systems management and strategy formulation: the ‘stages of growth’ model revisited ”, Information Systems Journal , Vol. 1 No. 2 , pp. 89 - 114 .

Gay , S.B. , Sobel , A.H. , Young , L.Q. and Dwyer , S.J. ( 2002 ), “ Processes involved in reading imaging studies: workflow analysis and implications for workstation development ”, Journal of Digital Imaging , Vol. 15 No. 3 , pp. 171 - 177 .

Gibby , W.A. and Mciff , E.B. ( 1997 ), “ Lnformactics: Radiology networking fosters quality services: Diagn lmaging ”, Vol. 19 , pp. 145 - 159 .

Gladwin , J. ( 1999 ), “ An informational approach to health management in low-income countries ”, Ph.D. Thesis, University of Sheffield .

Hillestad , R. , Bigelow , J. , Bower , A. , Girosi , F. , Meili , R. , Scoville , R. , et al. ( 2005 ), “ Can electronic medical record systems transform health care? Potential health benefits, savings, and costs ”, Health Affairs) , Vol. 24 No. 5 , pp. 1103 - 1117 .

Huckvale , C. , Car , J. , Akiyama , M. , et al. ( 2010 ), “ Information technology for patient safety ”, Quality and Safety in Health Care , Vol. 19 No. S2 , pp. i25 - 33 .

Husein , K. , Adeyi , O. , Bryant , J. and Cara , N.B. ( 1993 ), “ Developing a primary health care management information system that supports the pursuit of equity, effectiveness and affordability ”, Social Science and Medicine (1982) , Vol. 36 No. 5 , pp. 585 - 596 .

Dudeck , J. , Blobel , B. and Lordieck , W. ( 1997 ), New Technologies in Hospital Information Systems , IOS Press .

Jayasuiriya , R. ( 1999 ), “ Managing information systems for health services in a developing country: a case study using a contextualist framework ”, International Journal of Information Management , Vol. 19 , pp. 335 - 349 .

Rodrigues , J. ( 2009 ), “ Health information systems: Concepts, methodologies, tools, and applications, volume 1. Medical information science reference ”,

Johnson , K.J. , Ravert , R.D. and Avertan , A. ( 2001 ), “ Hopkins teen central: assessment of an internet based support system for children with cystic fibrosis ”, PEDIATRICS , Vol. 107 No. 2 , pp. E24 .

Johnson , K.B. ( 2001 ), “ Barriers that impede the adoption of pediatric information technology ”, Archives of Pediatrics and Adolescent Medicine , Vol. 155 No. 12 , pp. 1374 - 1379 .

Kuruvilla , S. , Dzenowagis , J. , Pleasant , A. , Dwivedi , R. , Murthy , N. , Samuel , R. and Scholtz , M. ( 2004 ), “ Digital bridges need concrete foundations: lessons from the health internetwork India ”, BMJ , Vol. 328 No. 7449 , pp. 1193 - 1196 .

Kuther , T.L. ( 2003 ), “ Medical decision-making and minors: issues of consent and assent ”, Adolescence , Vol. 38 No. 150 , pp. 343 - 358 .

Levine , B. ( 1990 ), “ A tilo of interfaces pilots communication networks ”, Diagn Imaging Focus on PACS 12: I .

Li , B.N. , Chao , S. and Dong , M.C. ( 2007 ), “ SIBAS: a blood bank information system and its 5-year implementation at Macau ”, Computers in Biology and Medicine , Vol. 37 No. 5 , pp. 588 - 597 .

Li , B.N. , Dong , M.C. and Chao , S. ( 2008 ), “ On decision making support in blood bank information systems ”, Expert Systems with Applications , Vol. 34 No. 2 , pp. 1522 - 1532 .

Lifshitz , M.S. , Blank Ge . and Schexneider , K. ( 2007 ), “ Clinical laboratory informatics ”, in McPherson , R.A. and Pincus , M.R. (Eds), Henry’s Clinical Diagnosis and Management by Laboratory Methods , 21st ed. , WB Saunders Elsevier . pp. 112 - 121 .

Lippeveld , T.J. , Foltz , A. and Mahouri , Y.M. ( 1992 ), “ Transforming health facility-based reporting systems into management information systems: lessons from the Chad experience ”, Development Discussion Papers, No. 430 , Harvard Institute of International Development , Cambridge, MA , pp. 1 - 27 .

Lippeveld , T. , Sauerborn , R. and Bodart , C. ( 2000 ), Design and Implementation of Health Information Systems , World Health Organization , Geneva .

Little Johns , P. Wyatt , J.C. and Garvican , L. ( 2003 ), “ Evaluating computerized health information systems: Hard lessons still to be learnt ”, BMJ Publishing Group Ltd. BMJ.com .

Lubitz , D.V. and Wickramasinghe , N. ( 2006 ), “ Healthcare and technology: the doctrine of network centric healthcare ”, International Journal of Electronic Healthcare , Vol. 2 No. 4 , pp. 322 - 344 .

Lun , K.C. ( 1995 ), “ The role of information technology in health care cost containment ”, Singapore Med J , Vol. 36 No. 1 , pp. 32 - 34 .

Mariani , C. , Tronchi , A. , Oncini , L. , Pirani , O. and Murri , R. ( 2006 ), “ Analysis of the x-ray work flow in two diagnostic imaging departments with and without a RIS/PACS system ”, Journal of Digital Imaging , Vol. 19 , No. S1 , pp. 18 - 28 .

Markus , M.L. and Robey , D. ( 1988 ), “ Information technology and organizational change: causal structure in theory and research ”, Management Science , Vol. 34 No. 5 , pp. 583 - 598 .

McKibbon , K.A. , Lokker , C. , Handler , S.M. , et al. ( 2012 ), “ The effectiveness of integrated health information technologies across the phases of medication management: a systematic review of randomized controlled trials ”, Journal of the American Medical Informatics Association , Vol. 19 No. 1 , pp. 22 - 30 .

McKibbon , K.A. Lokker , C. Handler , S.M. et al. ( 2011 ),. “ Enabling medication management through health information technology (health IT) ”, Evid Rep Technol Assess (Full Rep) , 1 - 951 .

MCGM ( 2019 ), “ History of municipal corporation of greater mumbai ”, available at: https://portal.mcgm.gov.in/irj/portal/anonymous?NavigationTarget=navurl://e73b30fff440950441693f945ad3cba7 (accessed 7 March 2019 ).

MCGM Health Department ( 2019 ), “ Municipal corporation of greater Mumbai- Health department ”, available at: https://portal.mcgm.gov.in/irj/portal/anonymous?NavigationTarget=navurl://0a06009b86958aea412e367434b40c52 (accessed 7 March 2019 ).

MCGM IT Department ( 2019 ), “ Municipal corporation of greater Mumbai- Information technology department ”, available at: https://portal.mcgm.gov.in/irj/portal/anonymous/qlitdp?guest_user=english (accessed 7 March 2019 ).

MCGM RTI ( 2019 ), “ Municipal corporation of greater Mumbai- Citizen forum (right to information) department manuals ”, available at: https://portal.mcgm.gov.in/irj/portal/anonymous?NavigationTarget=navurl://14ebe3fd2ffb65c2dd97ceeda2158e11 (accessed 7 March 2019 ).

MCGM Bid Document ( 2018 ), “ Bid notification no.: 7100134255: Bid document for supply, installation, testing, commissioning and maintenance of hardware and network components for MCGM’s hospital management information system.’ law insider ”, available at: https://www.lawinsider.com/documents/lHp622wvpZJ (accessed 7 March 2019 ).

Miller , P.L. , Frawley , S.J. and Sayward , F.G. ( 2001 ), “ Issues in computer-based decision support in public health illustrated using projects involving childhood immunization ”, Journal of Public Health Management and Practice : Jphmp , Vol. 7 No. 6 , pp. 75 - 86 .

Mohapatra , S. ( 2009 ), Cases in Management Information System , PHI , New Delhi .

Mukul ( 2018 ), “ MCGM leveraging technology to boost healthcare delivery system ”, available at: https://ehealth.eletsonline.com/2019/02/mcgm-leveraging-technology-to-boost-healthcare-delivery-system/ (accessed 7 March 2019 ).

National Institutes of Health ( 2003 ), “ Committee on data standards for patient safety. Key capabilities of an electronic health record system: Letter report ”, National Academies Press , Washington, DC .

Nolan, Norton and CO ( 1992 ), Ondernemingsstrategie eninformatie technologie. (NNC and VSB) , The Hague .

Offenmuller , W. ( 1997 ), “ Expectations and solutions for HIS/R1S/PACS dataflow and workflow ”, J Digit Imaging , Vol. 10 , pp. 95 - 98 .

Pearson , S. , Balis , U.J. , Fuller , J. , et al. ( 2006 ), “ Managing and validating laboratory information systems; approved guideline ”, Clinical and Laboratory Standards Institute Document AUTO8-A , Vol. 26 No. 36 .

Ralston , M.D. , Coleman , R.M. , Beaulieu , D.M. , Scrutchfield , K. and Perkins , T. ( 2004 ), “ Progress toward paperless radiology in the digital environment: planning, implementation, and benefits ”, Journal of Digital Imaging , Vol. 17 No. 2 , pp. 134 - 143 .

Robey , D. and Boudreau , M.-C. ( 1999 ), “ Accounting for the contradictory organizational consequences of information technology: theoretical directions and methodological implications ”, Information Systems Research , Vol. 10 No. 2 , pp. 167 - 185 .

Robey , J.M. and Lee , S.H. ( 1990 ), “ Information system development in support of national health programme monitoring and evaluation: the case of the Philippines ”, World Health Statistical Quarterly , Vol. 43 , pp. 37 - 46 .

Rosenbloom , S.T. , Qi , X. , Riddle , W.R. , et al. ( 2006 ), “ Implementing pediatric growth charts into an electronic health record system ”, Journal of the American Medical Informatics Association , Vol. 13 No. 3 , pp. 302 - 308 .

Sandiford , P. , Annett , H. and Cibulskis , R. ( 1992 ), “ What can information systems do for primary health care? An international perspective ”, Social Science and Medicine , Vol. 34 No. 10 , pp. 1077 - 1087 .

Sapirie , S.A. and Orzeszyna , S. ( 1995 ), “ Selecting and defining national health indicators. Strengthening country health information unit, division of epidemiological surveillance and health situation and trend assessment ”, World Health Organization , Geneva , available at: www.who.int/healthservices-delivery/information/20000629d.htm

Shearer , S.O. , Miller , T. and Gillen , S. ( 1997 ), “ Picture archiving and communication system implementation: the practical considerations of adapting the technology to the real world of health care operations ”, Journal of Digital Imaging , Vol. 10 , No. S1 , pp. 158 - 160 .

Shekelle , P. Morton , S.C. and Keeler , E.B. ( 2006 ), “ Costs and benefits of health information technology ”, Rockville (MD): Agency for Healthcare Research and Quality (US); (Evidence Reports/Technology Assessments, No. 132) .

Sinard , J.H. and Morrow , J.S. ( 2001 ), “ Informatics and anatomic pathology: meeting challenges and charting the future ”, Human Pathology , Vol. 32 No. 2 , pp. 143 - 148 .

Sinard , J.H. ( 2006 ), “ Practical pathology informatics ”, Demystifying Informatics for the Practicing Anatomic Pathologist , Springer . New York, NY , pp. 1 - 380 .

Smith , D.L. , Hansen , H. , ( 1988 ), and., and Karim , M.S. “ Management information support for district health systems based on primary health care ”, in Wilson RG et al. (Eds), Management Information Systems and Microcomputers in Primary Health Care , Aga Khan Foundation , Geneva , 89 - 110 .

Smith , M.H. ( 1988 ), “ National childhood vaccine injury compensation act ”, Pediatrics , Vol. 82 No. 2 , pp. 264 - 269 .

Tamblyn , R. ( 2004 ), “ Improving patient safety through computerized drug management: the devil is in the details ”, HealthcarePapers , Vol. 5 No. 3 , pp. 52 - 68 .

The Hindu ( 2018 ), “ Dynacons systems bags contract worth ₹58 cr ”, available at: www.thehindubusinessline.com/markets/stock-markets/dynacons-systems-solutions/article24856706.ece (accessed 7 March 2019 ).

Twelfth Five year Plan Draft 2012 ( 2017 ), “ Social sectors, vol. 3.Govt. of India ”, available at: http://planningcommissiongov.in/plans/planrel/12thplan/welcome.html

Van Der Lei , J. , Duisterhout , J.S. , Westerhof , H. , Boon , W. , Cromme , P.V.M. and Van Bammel , J.H. ( 1993 ), “ The introduction of computer based patient records in The Netherlands ”, Annals of Internal Medicine , Vol. 119 No. 10 , pp. 1036 - 1041 .

Van Hartevelt , J.H.W. ( 1993 ), “ Information management in international development as an area for information services with a case in the field of health care in Ghana ”, International Forum on Information and Documentation , Vol. 18 , pp. 32 - 36 .

Veader , P.C. ( 1997 ), “ The missing links ”, February 16, 1997; Online: Peter C. Veader: Internet: August , Vol. 30, 1997

Wang , S.J. , Middleton , B. , Prosser , L.A. , Bardon , C.G. , Spurr , C.D. , Carchidi , P.J. , et al. ( 2003 ), “ A cost-benefit analysis of electronic medical records in primary care ”, The American Journal of Medicine , Vol. 114 No. 5 , pp. 397 - 402 .

White , K.S. ( 2005 ), “ Speech recognition implementation in radiology ”, Pediatric Radiology , Vol. 35 No. 9 , pp. 841 - 846 .

World Bank ( 1993 ), World Development Report 1993: Investing in Health , Oxford University Press , New York, NY .

World Bank ( 1999 ), World Development Indicators 1999 , The World Bank , Washington, DC .

Yasnoff , W.A. , O’Carroll Patrick , W. , Koo , D. , Linkins Robert , W. and Kilbourne , E.M. ( 2000 ), “ Public health informatics: Improving and transforming public health in the information age ”, Journal of Public Health Management and Practice : Jphmp , Vol. 6 No. 6 , pp. 67 - 75 .

Young , K.M. ( 2000 ), The World of Informatics: informatics for Healthcare Professionals , F.A. Davis Company , Philadelphia , p. 11 - 24 .

Further reading

Chaudhry , B. , Wang , J. , Wu , S. , Maglione , M. , Mojica , W. , Roth , E. , Morton , S.C. and Shekelle , P.G. ( 2006 ), “ Systematic review: Impact of health information technology on quality, efficiency and costs of medical care, improving patient care ”, Annals of Internal Medicine , Vol. 144 No. 10 , pp. 742 - 752 .

Connelly , D.P. , Sielaff , B.H. and Willard , K.E. ( 1996 ), “ The clinical workstation as the means of improving laboratory use ”, Clinica Chimica Acta , Vol. 248 No. 1 , pp. 51 - 64 .

Kallinikos , J. ( 2005 ), “ The order of technology: Complexity and control in a connected world ”, Information and Organization , Vol. 15 No. 3 , pp. 185 - 202 .

Kallinikos , J Contini and Lanzara , ( 2008 ), “ Institutional complexity and functional simplification: the case of money claim online service in England and Wales ”, in (Eds) ICT and Innovation in the Public Sector. European Studies in the Making of E-Governmen , Palgrave Macmillan , Basingstoke , pp. 174 - 210 .

Lanzara , G.F. ( 2008 ), “ Building digital institutions: ICT and the rise of assemblages in government ”, in Contini and Lanzara (Eds) , ICT and Innovation in the Public Sector. European Studies in the Making of E-Government , Palgrave Macmillan , Basingstoke , pp. 9 - 48 .

Margolis , C.Z. , Warshawsky , S.S. , Goldman , L. , Dagan , O. , Wirtschafter , T. and Pliskin , J.S. ( 1992 ), “ Computerized algorithms and pediatricians management of common problems in a community clinic ”, Acad Med , Vol. 67 , pp. 282 - 284 .

Schoenbaum , S.C. and Barnett , G.O. ( 1992 ), “ Automated ambulatory medical records systems: an orphan technology ”, International Journal of Technology Assessment in Health Care , Vol. 8 No. 4 , pp. 598 - 609 .

Schriger , D.L. , Baraff , L.J. , Buller , K. , et al. ( 2000 ), “ Implementation of clinical guidelines via a computer charting system: effect on the care of febrile children less than three years of age ”, Journal of the American Medical Informatics Association , Vol. 7 No. 2 , pp. 186 - 195 .

Scott , J.C. ( 1988 ), Seeing like a State: How Certain Schemes to Improve the Human Condition Have Failed , Yale University Press .

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Hospital Management Software: A Case Study 

• Post author: Maryliya M J
• Post published: January 12, 2024
• Reading time: 11 mins read

Hospital Management Software (HMS): A Case Study 

Table of contents.

Hospital Management Software (HMS) has revolutionized the way healthcare organizations operate, streamlining administrative tasks, improving patient care, and enhancing overall efficiency.

Introduction to Hospital Management Software (HMS)

What is hospital management software.

Hospital management software, also known as HMS, is a digital solution that helps healthcare institutions streamline their administrative and operational processes. It provides a centralized system for managing various aspects of a hospital, including patient registration, appointment scheduling, billing, pharmacy management, and electronic health records.

Importance of HMS in the Healthcare Industry

In an industry where time is of the essence and accuracy is crucial, hospital management software plays a vital role. It eliminates the need for manual paperwork and reduces the chances of errors, improving overall efficiency. HMS provides real-time insights into patient data, allows seamless communication between departments, and enables hospitals to deliver better patient care. With the ever-increasing complexity of healthcare, HMS has become an indispensable tool for modern hospitals.

Key Features and Benefits of HMS

Streamlining patient registration and admission.

Gone are the days of long queues and paperwork during patient registration. HMS simplifies the process by digitizing patient information, streamlining the admission process, and reducing wait times. It ensures accurate data entry, minimizes errors, and improves the overall patient experience.

Efficient Appointment Scheduling and Management

Efficient appointment scheduling is essential for both patients and healthcare providers. HMS allows patients to book appointments online, check availability, and receive reminders. It also helps hospitals optimize their scheduling, reduce no-shows, and effectively manage their resources.

Simplifying Billing and Revenue Management

Billing and revenue management can be complex and time-consuming. HMS automates the billing process, generates accurate invoices, and integrates with insurance providers. It ensures transparency, reduces billing errors, and helps hospitals manage their finances effectively.

Enhancing Inventory and Pharmacy Management

Keeping track of inventory and managing pharmacy operations can be a daunting task. HMS enables hospitals to track stock levels, automate reordering, and ensure seamless supply chain management. It improves medication safety, reduces wastage, and enhances overall efficiency.

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Improving electronic health records ( ehr ) and documentation.

Maintaining accurate electronic health records is essential for providing quality healthcare. HMS digitizes patient records, making them easily accessible, securely stored, and retrievable when needed. It improves data accuracy, enhances collaboration between healthcare professionals, and simplifies documentation processes.

About the Client

Our client, a large hospital, faced challenges in patient record management, appointment scheduling, and interdepartmental coordination. Recognizing the need for a comprehensive solution, they sought a Hospital Management Software (HMS) to enhance patient care and administrative efficiency. 

Project Overview

The project aimed to develop a robust Hospital Management Software using .NET to address the client’s challenges. The primary objectives included electronic health records, appointment scheduling, inventory management of medical supplies, and seamless communication between various departments for a more integrated healthcare system. 

The Challenges

• Patient Record Management: The existing systems struggled with efficient and secure management of electronic health records. 
• Appointment Scheduling: Inefficient appointment scheduling processes led to long waiting times and patient dissatisfaction. 
• Interdepartmental Coordination: Lack of seamless communication hindered collaboration between different departments. 

The Solution

Our experienced team of developers and project managers collaborated to design and implement a comprehensive .NET-based Hospital Management Software (HMS). The solution incorporated features such as electronic health records, appointment scheduling, and inventory management, promoting seamless communication between departments. 

Key Features of the HMS

• Electronic Health Records (EHR): The HMS enabled efficient and secure management of electronic health records, ensuring easy access for authorized personnel. 
• Appointment Scheduling: Advanced scheduling algorithms optimized appointments, reducing waiting times and improving patient satisfaction. 
• Inventory Management: The software facilitated real-time tracking and management of medical supplies, preventing shortages and optimizing inventory levels. 
• Interdepartmental Communication: Robust communication tools were integrated to enhance collaboration and coordination between different hospital departments. 

The Outcome

The Hospital Management Software was successfully deployed, resulting in significant improvements in patient care and administrative efficiency. Efficient EHR management, optimized appointment scheduling, and seamless communication between departments enhanced overall healthcare services. 

Our team’s expertise in developing a tailored Hospital Management Software using .NET technologies addressed the client’s challenges effectively. The implementation of features like EHR, appointment scheduling, and interdepartmental communication contributed to a more integrated and efficient healthcare system. 

In conclusion, Hospital Management Software (HMS) has proven to be a game-changer in healthcare management, revolutionizing the way hospitals operate and improving patient care. The successful case study of this hospital showcases the transformative power of HMS, highlighting its benefits, challenges, and lessons learned throughout the implementation process.

As we look ahead, the future of HMS holds promising advancements, including artificial intelligence, cloud-based solutions, and interoperability with other systems. With continued innovation and adoption of HMS, we can expect further improvements in hospital operations, patient experiences, and overall healthcare outcomes. Embracing the potential of Hospital Management Software is crucial for healthcare organizations seeking to thrive in a rapidly evolving industry.

Are you facing challenges in patient record management and hospital administration? Contact us today to explore how our expertise in HMS development can transform your healthcare operations and enhance patient care. 

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The client was a prominent multispecialty hospital with separate departments for everything from pediatrics to plastic surgery. Their problem was that individual departments had their workflows and systems, which created separate data silos. And they wanted to bring together all this data under a single platform for better management.

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Once we spoke to the different department heads, doctors, other healthcare professionals, and the hospital, we knew what we had to do.

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We created a solution that produces our client an estimated saving of 1 million USD annually.

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To We used our proprietary HIPAA compliant EMR technology to store patient details securely.

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Reduced workload for the hospital staff

98% of the hospital staff reported a massive decrease in their daily workload.

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Mar 7, 2024

USACS Announces Annual Clinical Excellence Award Winners

CANTON, Ohio, March 7, 2024 – US Acute Care Solutions (USACS), the nation’s largest physician-owned provider of hospital-based emergency and inpatient medicine, is pleased to announce eight clinicians who have been named recipients of the National Clinical Governance Board’s (NCGB) Clinical Excellence Award. These recipients were honored during USACS’ annual Assembly meeting held in Denver, CO, last month. The Clinical Excellence Award was created to recognize outstanding clinical care by individual physicians and advanced practice providers (APPs) who do not serve in leadership or management roles and represent each USACS service line. Nominations are submitted year-round by clinical colleagues and are reviewed at the beginning of each calendar year. Recipients are selected and notified before the annual spring Assembly meeting. Congratulations to the following recipients of the 2024 Clinical Excellence Award: Matthew Baltz, MD, Bon Secours Memorial Regional Medical Center— Mechanicsville, VA Calen Hart, MD, AdventHealth Tampa—Tampa, FL Waleed Hussein, MD, Hazel Hawkins Memorial Hospital—Hollister, CA Omar Naji, MD, StoneSprings Hospital Center—Dulles, VA Ryan Nguyen, PA-C, Dell Children's Medical Center of Central Texas—Austin, TX James (Ian) Richardson, DO, Bon Secours Memorial Regional Medical Center—Mechanicsville, VA Nathan Scherer, DO, AdventHealth ER and Urgent Care at Meridian—Parker, CO Melissa Volpe, PA-C, Sentara Martha Jefferson Hospital—Charlottesville, VA National Director of Clinical Education and Vice Chair of the NCGB, Roya Caloia, DO, MPH, FACEP, shared, “These are the people you work alongside who make you want to be a better physician or APP. Their efforts and commitment to high-quality patient care remind you of why you chose to go into medicine in the first place. Congratulations to each recipient, I am honored to call each of you colleagues!” About USACS Founded by emergency medicine and inpatient physicians across the country, USACS is solely owned by its physicians and hospital system partners. The group is a national leader in integrated acute care, including emergency medicine, hospitalist, and critical care services. USACS provides high-quality care to approximately ten million patients annually across more than 400 programs and is aligned with many of the leading health systems in the country. Visit usacs.com for more. ### Media Contact Marty Richmond Corporate Communications Department US Acute Care Solutions 330.493.4443 x1406 [email protected]

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How Digital Transformation Can Improve Hospitals’ Operational Decisions

• Song-Hee Kim

It can help with patient flow, staffing, scheduling, and supply-chain management.

The use of digital technologies in clinical decision-making has received the most attention. But they also have the potential to help hospitals make better decisions in many areas of operations.  Research and hospitals’ experiences show that they can make a big difference in such areas as the management of the patient flow, staffing, scheduling, and the supply chain. The result can be improvements in the quality and efficiency of care and patients’ access to it.

Many companies are interested in digital transformation — using digital technologies to create or modify business processes, culture, and customer experiences — to grow and stay ahead of the competition, and hospitals are no exception.

• Song-Hee Kim is an associate professor of operations management at the SNU Business School at Seoul National University. Her research focuses on data-driven decision-making within health care systems, especially how to design human-algorithm interactions to improve quality, efficiency, and access to care in hospitals.
• Hummy Song is an assistant professor of operations, information, and decisions at the University of Pennsylvania’s Wharton School. Her research focuses on how operations can be designed to help health care providers work more efficiently and effectively.

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Hospital Case Management: A Review: 2019-2022

Affiliations.

• 1 Mary McLaughlin Davis, DNP, MSN, ACNS-BC, NEA-BC, CCM, is a certified case manager, clinical nurse specialist, and senior director for Case Management Cleveland Clinic Main Campus and Akron General Hospital. She served as an executive board member of the Case Management Society of America from 2013 to 2019 and president from 2016 to 2018.
• 2 Colleen Morley, DNP, RN, CCM, CMC, CMCN, ACM-RN, FCM, is a certified case manager and the associate chief clinical operations officer, Care Continuum, University of Illinois Health System. She is president of Chicago Case Management Society of America, served on the national executive board of directors from 2019 to the present and is the national president elect.
• PMID: 35901252
• DOI: 10.1097/NCM.0000000000000565

Purpose/objectives: In June 2019, a Case Management Society of America (CMSA) task force published "The Practice of Hospital Case Management: A White Paper." This was an important first step to outline the value of hospital case managers (HCMs) and to put forward recommendations for how to operationalize a major change in most hospitals for how case managers can practice.The SARS-CoV2 (COVID-19) pandemic drastically changed the practice of all interdisciplinary work within hospitals. The White Paper recommended that HCMs follow a select patient population through the hospital. Hospital case manager leaders realized that HCMs can work remotely and communicate with patients because meeting them in person was not an option. Hospital case managers are still resistant to leaving the hospital unit-based model, even after they experienced the value of this concept during the height of the pandemic.

Primary practice setting: Acute care hospitals.

Findings/conclusions: The White Paper recommended separating HCMs from utilization management. One unintended consequence is the loss of necessary knowledge and competencies. These are related to compliance with the Centers for Medicare & Medicaid Services Conditions of Participation and regulatory mandates that can affect patient care and financial well-being. Hospital case manager leaders must stay current with these government requirements for hospitals and for all levels of care and keep the case managers informed, proficient, and fluent when coordinating the care of patients.

Implications for case management practice: Hospital case manager practice is evolving; change is the single constant in health care. This review of the CMSA Hospital Case Management Whitepaper demonstrates that in just three short years, the landscape of health care can change dramatically.Today's HCM leader must proactively address a multigenerational workforce, lack of title protection, and the COVID-19-induced "Great Resignation." The value of the HCM has never been more apparent as during the pandemic as the need to "empty beds" is critical, and the HCM is the professional who has the skill to provide efficient and patient-centered care coordination. The HCM leader practices positive leadership techniques that benefit the leader, the HCM, and most importantly the patient.

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Volume 11, Issue 11 (November 2022)

Evolution, prospects, and challenges in hospital management information system: case studies.

• Article Download / Views: 584
• Authors : Yashodhan Gharote , Raashi Jatakia , Dr. Gajanan Nagare
• Paper ID : IJERTV11IS110082
• Volume & Issue : Volume 11, Issue 11 (November 2022)
• Published (First Online): 28-11-2022
• ISSN (Online) : 2278-0181
• Publisher Name : IJERT

Yashodhan Gharote Department of Biomedical

Engineering, Vidyalankar Institute of Technology, Mumbai, India

Raashi Jatakia

Department of Biomedical Engineering, Vidyalankar Institute

of Technology, Mumbai, India

Dr. Gajanan Nagare Department of Biomedical Engineering, Vidyalankar Institute of Technology, Mumbai, India

Abstract Nowadays, a Hospital Management Information System (HMIS) is one of the requirements for every private or public hospital. An HMIS is desired for several reasons such as efficient operations management of the hospital, time optimization, reducing the paperwork produced in the process, etc. The HMIS also helps maintain patient and hospital records for a long period of time. Most importantly the HMIS ensures quality assurance and patient satisfaction by delivering at very high standards. Finally, the data collected and generated through the HMIS is based on collective information from various departments and components of the hospital and can enable a person to analyze this data and make intelligent decisions for the betterment of the hospital, its staff, and the patients. The data generated can be visualized to seek insightful information, and these insights can be plotted or researched further to make relevant conclusions and help the medical world to make innovations and advancements in the field. This study aims to enlighten the reader on the various communities in a hospital setting. It also presents case studies mapping the progress made by the HMIS over the years. Moreover, it also presents the prospects and challenges in the domain.

Keywords Artificial Intelligence; Hospital Management Information System (HMIS); Electronic Health Records; Patient Records.

INTRODUCTION

In referring to the past archives, a lot of those archives suggest that developed countries like the United States first introduced the Hospital Management Information System (HMIS) in the early 1960s and since then the HMIS has evolved over the years. In the year 1965, at the Los Angeles County General Hospital, an identification file that included patient name, birthplace, etc. for 1,00,000 patients was created using a punched card system. In the 1970s object- oriented databases started being used. By the 1980's the volume and complexity of patient data, and health records increased substantially. The time between 1970-1990 was the one that shaped the major evolution of the HMIS. In the late 1980s new technologies like the Health Level 7 (HL7) played a detrimental role in advancing the then HMIS system and Technology [1]. Post the 1990s the HMIS started being adopted by more and more countries, in the hospital setting. As the world entered the 21st century, the number of services and the complexity of tasks that the HMIS could provide, and the handle had increased drastically. With fast advancements in the field of information technology, slowly and steadily in the first decade of the 21st century, the HMIS

OVERVIEW OF HMIS

Managing the Hospital operation is one of the most critical tasks and nowadays it is taken care of by a hospital management information system or a hospital information system. It is a series of software systems that are used to govern important tasks like collecting and managing information, managing billing and hospital schedule, maintaining Electronic Health Records (EHR) [3], etc, and most importantly keeping all things digital and organized. To add more, an HMIS also links various units of the hospital internally, for instance linking doctors to the pharmacy and laboratories [4,5]. For the time before the use of an HMIS, all these tasks of managing and maintaining information were performed manually and there was tremendous paperwork that was done. This paperwork was vulnerable in several ways, not only for the hospital but also for the patient, and most importantly above all inefficient use of the data that was being generated. With the HMIS in place, hospitals are operating seamlessly, and patient satisfaction has also been impacted positively. The factors like patient safety, trustworthiness, etc have also been incorporated. In a certain way, an HMIS can be termed as a product that is used by hospitals or a product available in the medical world.

COMPONENTS OF HMIS

As seen in figure 1, the various components managed by an HMIS in the hospital setting include Laboratories, nursing and wards, consultancy, patient admission/ registration, blood banks, stores, pharmacy, and others like the operation theatre and the radiology unit. All these important services are linked to each other via the HMIS and collective information gathering, and processing happens in the system. An HMIS links all these factors in a hospital, along with interlinking these parameters with the hospital structure. The interlinking of the health professional, the radiology community, the pharmaceutical community, the patient care community, and the administrative community with the services in the hospital. Hence HIMS integrates all the collaborators and service providers on a single platform. All these components perform major functions in the

hospital. For instance, ward management allows the administration to keep track of the rooms occupied, the number of patients in the hospital, etc. The blood bank data allows hospitals, especially in situations of emergency to coordinate and fulfill the blood requirement. Overall, the HMIS affirmatively guarantees the smooth functioning of the components and proper management of the data.

Fig. 1. Components of HMIS

COMMUNITIES AND SERVICES IN HMIS From figure 2, the various communities in the hospital

include the admin community, the patient care community, and the clinical community comprising doctors, specialists, and nurses. The figure shows task management in a hospital, and how the HMIS system links the various communities like the clinical and radiology community to essential components of the hospital like pharmacy, nursing, wards, etc. The HMIS system brings synchronization between various departments in the hospital. For instance, the admin community which through the HMIS will handle billing and patient services will be connected to other departments like consulting, patient admission, etc. which will allow for fast and smooth execution of processes and will also lead to patient satisfaction. The overall coordination and workflow management of inter-department and intra-department will happen seamlessly. As observed from the figure the various services that the HMIS provides include patient services like scheduling appointments, patient registration, etc. Other services include clinical services like a record of clinical units like the radiology unit, the pathology labs, etc. The HMIS also provides administration services like billing, staff management, and maintaining medical records and archives, and miscellaneous services like managing biomedical waste, and providing information about the hospital through information centers and kiosks. The HMIS also provides useful and essential services like patient education. The outcome of the process through the working of the HMIS system results in in-patient and out-patient management, medical issue management and managing lab reports and patient health records. The other key outcomes include managing the information in the hospital, waste

management, inventory and dashboard management, and others alike. Overall, the service provided in a particular HMIS product varies as per the requirements. For instance, sometimes the institutes and hospitals who deploy the HMIS only deploy it for administrative services like billing and managing patient records. Some other organization can make use of even more complex HMIS products which provides services right from administration to patient care also covering LABS and the clinical community, so basically, it all depends on the requirement and the type of services deployed and availed.

Fig. 2. Communities and Services in HMIS

EVOLUTION OF HMIS

Fig. 3. shows the subsequent rise in the field of HMIS, a software designed to collect, store, and analyze patient records, equipment data, management, and decision-making in hospitals. In the early stages of HMIS, the system was used simply to store, collect, and analyze data but the paperwork was also a part of the administration functionality in hospitals. HMIS was not as largely used and appreciated as it did in the coming years. Several hospitals started integrating their systems with HMIS around 2012, and most of the hospitals had made several advancements to make the software efficient and sustainable. The growth of HMIS till 2022 increased at a fast rate with time as the majority of the hospitals had adapted to the system-managing software due to its date quality assurance and rapid maturation of the system. The Histogram depicts the timely rise of the use of HMIS in various health facilities and organizations and the improvement of the quality of the software with an increase in its demand.

Fig. 3. Progress in HMIS

Case 1: The National Taiwan University Hospital (NTUH) has been implementing a Hospital Information System (HIS) since the 1980s and has been efficiently processing along with developments in medical technology

The patient-oriented benefit of this technology is the method of ward and bed assignment of each patient, based

CHALLENGES AND THEIR SUCCEEDING SOLUTIONS DURING THE EVOLUTION

In Case 3, the author of the paper [3] states the predominant reason behind acquiring the support of Information Technology (IT) and Artificial Intelligence (AI) for management experiences. The high reputation and grading of the hospital put a lot of pressure on the proper treatment of other children as well as the children who tested positive for covid, together. It had to ensure strict prevention of the occurrence of nosocomial diseases which compelled CHFU to introduce AI and IT for management purposes. The most evident success of their methodology was the execution of Internet Hospitals. It prevented the further occurrence of nosocomial diseases at a large scale while still being efficient and sustainable despite it being online. However, the scope of such enhanced and advanced

FUTURE PROSPECTS IN HMIS

Fig. 4. Fields developed by integration of AI in HMIS

Figure 4 shows the components applicable for the upgradation of HMIS using AI. Electronic Health Records (EHR) correspond to the paperwork of a hospitalized patient. With the help of AI, this system can be digitalized and automated for better access and to reduce additional inconveniences to patients. Certain important factors of a hospital's information management such as Virtual admin and Mechanized form-filling are the automated versions of the hospital administration department which provides the patients with an advanced help desk facility consisting of computerized reception assistants and form-filling procedures integrated with modernized technologies. Medical History Retrievement refers to the restoration and extraction of data that is several years old and is difficult to maintain as paperwork. It can be considered as a part of EHR; however, it can be branched out individually since the implementation of AI required by it has unique algorithms. Machine Learning or Deep Learning has a wide range of potential for growth in the healthcare field based on speech recognition, data analysis, computer vision, and decision and prediction-making using data that has been collected.

The overall intention of using AI for Hospital Information Management is patient-oriented and for the

benefit of healthcare providers. The increased use of AI will benefit paperless records, improved inventory, and better security of hospital staff as well as of the patient medical history information as shown by the result of a survey taken in the paper [10]. In the paper [10], 12 physicians were asked to examine the records of two types, one with AI optimization and one without. The results suggested that the records implemented with AI optimization do turn out to be time-saving and more convenient than the traditional methods.

This could result in a major success rate of hospital treatment quality, however, could also result in the unemployment of staff which will eventually, distress the hospital staff along with the city's employment rate. These methodologies do seem effective and revolutionary but involve just as large an amount of investment and risk factors, especially for developing countries. This limits the expansion of such technologies in several hospitals due to restricted funding and negligence in the acceptance of new methods because of psychological barriers.

Healthcare Organisations need to mandate the security of data collected by enhancing privacy and security. However, in todays world where cyber security violations are so often in existence, it is difficult to make a security system strong enough to defend from bypass activities. [13] This makes it difficult to build trust among the government, patients, and leading healthcare organizations.

This study involves a systematic and holistic review of the origin, components, implementation, and evolution of HMIS with time. The study also presents various case studies to demonstrate the evolution and progress of HMIS over time. The influence of IT and AI on HMIS can be proved to be revolutionary for patient and hospital staff satisfaction as well as in the quality of the services provided by health facilities and organizations. The challenges faced

in the integration of AI in HMIS along with the challenges faced in the development of HMIS have also been discussed.

H. Mazaherilagha, Hospital Information Systems: The status and approaches in selected countries of the Middle East, Electron Physician, Vol. 10, 2018.

pp. 574-582, June 2012.

M. Zhang, J. Long, A. Y. Nag, P. Rajpurkar, S. R. Sinha, Development and Validation of an Artificial Intelligence System to Optimize Clinician Review of Patient Records, Health Informatics, Vol. 4, no. 7, 2021.

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Home » Management Case Studies » Case Study: Hospital Management System (HMS)

Case Study: Hospital Management System (HMS)

XO Hospital Management System

XO Infotech Ltd. has developed a core package — Hospital Management System that addresses all major functional areas of Hospital. The development environment ensures that XO HMS has the portability and connectivity to run on virtually all standard hardware platforms, with stringent data security and easy recovery in case of a system failure. XO HMS provides the benefits of streamlined operations, enhanced administration and control, improved response to patient care, cost control, and increased profitability.

Some of the Subsystem Modules in XO HMS:

Reception : The reception module handles various enquiries about the patient’s admission and discharge details, bed census, and the patient’s movements within the hospital. The system can also handle fixed-cost package deals for patients as well as Doctor Consultation and Scheduling, Doctor Consultancy Fees and Time Allocation.

OPD, IPD Registration and Admission : This module helps in registering information about patients and handling both IPD and OPD patient’s query. A unique ID is generated for each patient after registration. This helps in implementing customer relationship management and also maintains medical history of the patient.

Administration : This module handles all the master entry details for the hospital requirement such as consultation detail such as doctor specialization, consultancy fee, and service charges.

Security : This module handles multi level security of XO HMS so that every admission and transaction can be traced with the help of user ID.

Pharmacy Store : This module deals with all medical items. This module helps in maintaining Item Master Maintenance, Receipt of Drugs/consumables, issue handling of material return, generating retail bills, stock maintenance. It also helps in fulfilling the requirements of both IPD and OPD Pharmacy.

Purchase : This module helps in raising purchase orders, maintaining purchase details and other purchase related details.

Phlebotomy : This specific module caters in maintaining test requisitions, sample collection status and various procedures for collection of sample for the tests prescribed.

Laboratory : This module enables the maintenance of investigation requests by the patient and generation of test results for the various available services, such as clinical pathology, X-ray and ultrasound tests. Requests can be made from various points, including wards, billing, sample collection and the laboratory receiving point. The laboratory module is integrated with the in-patient/ outpatient registration, wards and billing modules.

Emergency : The development of this module keeps in mind the criticality of this department. Every care has been taken to ensure that minimum of time is taken to register the patient, so as to reduce the tension of the already stressed out relatives. Neither any detailed contact information of the patient is required nor any information about the payment type is solicited.

OT Management : This module deals with operation theatre activities such as equipment used detail, resource ordering, drug order, gynecology detail recording, laboratory order and reports transfer requisition, patient monitoring, blood request, new born baby detail and details of delivery.

Minor Surgery : This module is same in features as in OT management though the function is different. This module deals with the surgeries minor in nature, which does not require complete anesthesia.

Blood Bank : The blood bank module provides information on the collection and storage of blood, results of blood tests, cross-matching identifications, and transfusion reactions. It also enables the maintenance of donor mailing lists and donation ledgers. It would also provide online stock of blood available in three blood banks (GTB, LNJP and DDU

Ward Management : The ward management module takes care of medical equipment, doctor visit, vitals recording, patient case sheet, diet ordering, blood requisition, transfer intimation and discharge intimation etc. It also deals with the maintenance of the wards, inter- and intra-ward transfers.

OPD and IPD Billing : The billing module facilitates cashier and billing operations for different categories of patients and automatic posting of charges for different services such as lab tests, medicines supplied, consulting fees, food and beverage charges, etc. It enables credit party billing through integration with the financial accounting module.

Intensive Care Unit (ICU) : This module caters to scheduling, maintaining ICU Record, drug orders, consultant details, specific blood requests etc.

Food and Beverages : This module facilitates collection of information regarding various diet routines of patients and identifies the resources required to satisfy diet orders. Depending on the diet orders and other requests from canteen, the kitchen order plan can be prepared to decide the menu for the day. Analysis of the consumption patterns helps in better and efficient management of the kitchen.

Discharge Summary : The module helps in generating patient’s discharge summary, which includes patient’s health at the time of discharge, medical history, various diagnosis and drug prescriptions, history of present illness and course in hospital.

Financial Accounting : This module deals with cash/bank, receipts/payments, journal vouchers, etc. Various books of accounts, such as cashbook, bankbook and ledgers, can be generated and maintained using this module. It can also generate trial balance, balance sheet, and profit and loss statements.

Marketing Module : This module ensures that the hospital gets maximum exposure to the general public and vice versa. This module keeps track of the enquiries made at the reception and follows the lead.

Doctor’s Module : This module helps the doctors to keep a track of the entire medical history of a particular patient. Details such as the medicines prescribed, general medical records, previous consultations are all available to the doctor.

HR Management : Various MIS Reports are generated on the above modules for the smooth functioning of the hospital management so that checks can be made on any irregularity done in the hospital.

Questions :

1. Using tools of System Analysis elaborate any one of the subsystems of HMS in detail.

2. Draw any Patient registration form and any one sample MIS report.

3. Prepare the Data dictionary for the Doctors Master file.

4. Elucidate the conceptual plan for implementation of the Hospital Management System.

5. You are hired as a System Analyst , advice on the following :

(a)     Networking requirements

(b)     Database model requirements

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Artificial Intelligence for Hospital Health Care: Application Cases and Answers to Challenges in European Hospitals

Matthias klumpp.

1 Fraunhofer Institute for Material Flow and Logistics (IML), Josef-von-Fraunhofer-Str. 2-4, 44227 Dortmund, Germany; [email protected] (M.H.); [email protected] (O.U.)

2 Department of Business Administration, Georg-August-University of Göttingen, Platz der Göttinger Sieben 3, 37073 Göttingen, Germany

Marcus Hintze

Milla immonen.

3 VTT Technical Research Centre of Finland Ltd., Kaitoväylä 1, 90571 Oulu, Finland; [email protected]

Francisco Ródenas-Rigla

4 Polibienestar Research Institute, University of Valencia, Carrer del Serpis 29, 46022 València, Spain; [email protected]

Francesco Pilati

5 Department of Industrial Engineering, University of Trento, Via Sommarive 9, 38123 Trento, Italy; [email protected]

Fernando Aparicio-Martínez

6 NUNSYS S.L., Calle Gustave Eiffel 3, 46980 Valencia, Spain; [email protected]

Dilay Çelebi

7 Department of Management Engineering, Istanbul Technical University, Macka, Beşiktaş, 34367 İstanbul, Turkey; rt.ude.uti@dibelec

Thomas Liebig

8 TU Dortmund, Artificial Intelligence Unit, Otto-Hahn-Straße 12, 44221 Dortmund, Germany; [email protected]

9 Materna Information & Communications SE, Artificial Intelligence Unit, Voßkuhle 37, 44141 Dortmund, Germany

Mats Jirstrand

10 Fraunhofer-Chalmers Centre & Fraunhofer Center for Machine Learning, Chalmers Science Park, 41288 Gothenburg, Sweden; es.sremlahc.ccf@jstam

Oliver Urbann

Marja hedman.

11 Heart Center, Kuopio University Hospital and Institute of Clinical Medicine, University of Eastern Finland, Ritva Jauhiainen-Bruun, 70029 Kuopio, Finland; [email protected]

Jukka A. Lipponen

12 Department of Applied Physics, University of Eastern Finland, Yliopistonranta 1, 70210 Kuopio, Finland; [email protected]

Silvio Bicciato

13 Interdepartmental Center for Stem Cells and Regenerative Medicine (CIDSTEM), Department of Life Sciences, University of Modena and Reggio Emilia, Via Gottardi 100, 41125 Modena, Italy; [email protected]

Anda-Petronela Radan

14 Department of Obstetrics and Gynecology, University Hospital of Bern, Murtenstraße 11, 3008 Bern, Switzerland; [email protected]

Bernardo Valdivieso

15 La Fe University Hospital Valencia, Avinguda de Fernando Abril Martorell 106, 46026 València, Spain; se.avg@reb_oseividlav

Wolfgang Thronicke

16 ATOS Information Technology GmbH, Fürstenallee 11, 33102 Paderborn, Germany; [email protected]

Dimitrios Gunopulos

17 Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Panepistimioupolis, Ilisia, 15784 Athens, Greece; moc.liamg@soluponugd

Ricard Delgado-Gonzalo

18 Centre Suisse d’Électronique et de Microtechnique CSEM, Jaquet Droz 1, 2002 Neuchâtel, Switzerland; [email protected]

Associated Data

Not applicable.

The development and implementation of artificial intelligence (AI) applications in health care contexts is a concurrent research and management question. Especially for hospitals, the expectations regarding improved efficiency and effectiveness by the introduction of novel AI applications are huge. However, experiences with real-life AI use cases are still scarce. As a first step towards structuring and comparing such experiences, this paper is presenting a comparative approach from nine European hospitals and eleven different use cases with possible application areas and benefits of hospital AI technologies. This is structured as a current review and opinion article from a diverse range of researchers and health care professionals. This contributes to important improvement options also for pandemic crises challenges, e.g., the current COVID-19 situation. The expected advantages as well as challenges regarding data protection, privacy, or human acceptance are reported. Altogether, the diversity of application cases is a core characteristic of AI applications in hospitals, and this requires a specific approach for successful implementation in the health care sector. This can include specialized solutions for hospitals regarding human–computer interaction, data management, and communication in AI implementation projects.

1. Introduction

Research into applications of artificial intelligence (AI) in health care and within hospitals is a crucial area of innovation [ 1 ]. Smart health care with the support of AI technologies, such as Machine Learning (ML), is needed due to specific challenges in the provision of medical support in European countries as well as in the rest of the world. It is not only the outbreak of the COVID-19 pandemic that reveals the current problems and challenges facing European hospitals. The success in the science of medicine in the last decades has had the effect of patients becoming older, frailer, and multi-morbid due to a longer lifetime expectation [ 2 ].

This is accompanied by the fact that medical care and diseases are becoming increasingly complex. Due to this medical complexity, medical personnel are becoming more and more specialized, which cannot in general be fully provided for by smaller hospitals in rural areas. Added to this is the demographic change already emerging in Europe, e.g., the population of over 80-year-olds in the EU27 will double from 6.1% in 2020 to 12.5% in 2060 [ 3 ]. Hence, more older people with their specific health problems will use the health care system. In contrast to this, the number of young well-trained medical personnel is currently decreasing and a shortage of skilled personnel, such as doctors and nurses, is already emerging in many European nations [ 4 ].

The challenges of the simultaneous increase of older and multi-morbid patients with complex diseases and the shortage of skilled personnel are also hampered by the increasing economic constraints on hospitals. An increase in chronic diseases due to aging populations and shortage of medical specialists results in resource scarcity and medical sustainability challenges. In order not to endanger the living and health standards of the European nations it will be necessary to develop applied AI-solutions to relieve the burden of increased workload as well as being instrumental to deliver efficient, effective, and high-quality health care.

Adaptability and agility at hospitals are major prerequisites in this context, and narrowing the application of AI to optimization solely does miss the point in many cases. By opening a wider range of actionable options, from personalized medical diagnosis and treatment to choices in care, sourcing, and logistics areas, AI applications will provide more important support avenues than efficiency enhancements only [ 5 , 6 ]. In addition, multiple benefits regarding the ongoing COVID-19 pandemic can also be expected and should be further explored, especially regarding data analysis and preventing unnecessary patient contact for health care personnel in hospitals as centres of the fight against the viral disease [ 7 ].

AI can also contribute to the fight against pandemics as COVID-19, helping hospitals focus resources on pandemic patient’s treatments in the current as well as possible future situations. In this sense, most AI applications are directed at contactless analysis, diagnosis, and treatment (e.g., self-treatment and prevention), reducing the number of personal contacts and hospital visits, therefore reducing the potential spread of COVID-19 and other viral pandemics. AI in particular offers great potential for improving medical care and supporting the medical staff. The state of the art and the challenges regarding AI applications in hospitals and the health care sector are described for specific application areas in Figure 1 .

Interrelation structure of AI application areas for AI in hospitals.

With regards to the introduction of AI applications in hospitals, two specific questions arise, with the answers to them as the central contributions of this paper: First, what are the requirements and hospital setups for AI applications? To this end, the authors carried out a survey of different European hospitals and identified relevant projects in this field. As a result, the main fields of application of AI for hospitals are found as care, diagnosis, and logistics. The hospitals surveyed saw the greatest medical and economical potential in these three areas through the use of AI. Building on this, the paper outlines altogether 11 use cases in 9 hospitals across Europe, informing how AI can contribute to agility and efficiency in hospitals, improving health care from the resource efficiency as well as the service quality and choice side, aligned with the core hospital workflow and value adding processes. The second question is: How can a basic structure for the different AI use cases be established to avoid the mistake of developing isolated solutions that are difficult to transfer across hospitals? The authors propose three basics support areas which help to ensure a holistic approach to AI application implementation and transfer within the paper.

The paper is structured as follows: The following section is outlining the applied use case methodology for the analysis presented. The next section is describing the specific use case descriptions and expectations of hospitals towards AI applications. The following section presents a discussion regarding possible benefits and challenges as well as concept items such as human–computer interaction and medical data space concepts to overcome the challenges posed by AI applications in the hospital context. The final section provides an outlook towards future developments and challenges for AI applications in hospitals.

2. Use Case Methodology

The first step to identify the current challenges and areas of interest of European hospitals was to create a survey. The survey was carried out to obtain a differentiated view of the needs of European hospitals. Specifics were requested, such as country, type, number of patients and beds, and the main health care areas. In addition, hospital decision-makers identified specific areas of application and presented the focus and expected output of the utility of AI. The following Table 1 outlines the specific setup of these hospital characteristics for the institutions included in the survey.

Included survey and case study hospitals in Europe.

1 Data from hospital sources. Definitions might differ due to national data regulations. 2 University Hospital of Bern: http://www.frauenheilkunde.insel.ch/de/ueber-die-klinik , accessed on 2 October 2020. 3 Kuopio University Hospital: https://www.psshp.fi/web/en/organisation/operations-and-tasks , accessed on 2 October 2020. 4 Südtiroler Sanitätsbetrieb: https://www.sabes.it/de/578.asp , accessed on 2 October 2020. 5 La Fe University Hospital: Hospital activity report, 2019. 6 Federico II University of Naples. 7 Orton Ltd. University Hospital. 8 Odense University Hospital: https://en.ouh.dk/about-ouh/key-figures , accessed on 2 October 2020. 9 Bayındır Hospital. 10 Universitätsklinikum Essen: https://www.uk-essen.de , accessed on 2 October 2020.

The framework situations for the outlined AI use cases are characterized by their specific hospital setup in a broad multitude of European hospitals. By means of surveys carried out in the hospitals participating in this analysis, different health care personnel have provided systematic answers to a structured questionnaire dealing with relevant aspects to the study. The hospitals where asked to detail current practical problems in different areas, how are they currently managing these problems, ways and mechanisms to improve in these areas by means of AI, and relevant KPIs determining qualitative and quantitative improvements related to the adoption of the AI application. As a result, after extracting the information from these surveys, use cases could be drafted for the different health institutions, based on real and actual needs and opportunities. Societies require an effective and efficient health care system and especially hospitals as nodes in a network of actors providing high-quality services, resources and serving patients. The following table summarizes the main expectations as stated by the health organizations in the survey (see Table 2 ).

From the expectations, a total of 11 use cases in different health areas has been envisioned. It turns out that three particular fields are of specific interest to the hospitals surveyed: diagnosis, care and logistics.

In the field of diagnosis, clinical decisions still mostly depend on the application of clinical practice guidelines, instead of being based on the use of automatic decision support tools that exploit the increasing availability of medical data from molecular assays, electronic health records, clinical and pathological images, and wearable connected sensors. Nowadays, clinicians face enormous challenges in reconciling heterogeneous clinical data and exploiting the information content to make optimal decisions when assessing a disease or its progression, and this situation has become more evident in the midst of the global COVID-19 pandemic. Thus, there is an urgent need to develop smart decision support systems, which assist clinicians in making rapid and precise diagnostic decisions through the combination of multiple data sources. AI-based methodologies for medical diagnosis and medical decision support have gained attention in the recent years as these systems hold promise to automate the diagnosis and triage processes, thus optimizing and accelerating the referral process especially in urgent and critical cases. Recently, state-of-the-art examples demonstrated that software based on AI can be used in clinical practice to improve decision-making and to achieve fast and accurate databased diagnosis of various pathologies. In particular, AI has been proven particularly helpful in areas where the diagnostic information is already digitized, such as: for detection of cancers based on molecular, genomic, and radiological data [ 8 ], making individual prognosis in psychiatry using neuroimaging [ 9 , 10 ] identifying strokes from computed tomography scans [ 11 ], assessing the risk of sudden cardiac death or other heart diseases based on electrocardiograms and cardiac magnetic resonance images [ 12 , 13 ], classifying skin lesions from skin images [ 14 ], finding indicators of diabetic retinopathy in eye images [ 15 ], and detect phenotypes that correlate with rare genetic diseases from patient facial photos [ 16 ]. The change in clinical practice through and by the means of technological innovation is today decisively enabling health care systems to face to the continuous economic, socio-demographic and epidemiological pressures [ 17 ]. However, technological innovation, although important and central, must be carefully examined and accompanied to ensure that it really corresponds to effective social innovation. As addressed by MedTech Europe, developing AI systems and algorithms for healthcare settings requires specific skillsets which are in short supply, and investment in education and training of professionals involved (e.g., data scientists, practitioners, software engineers, clinical engineers), is mandatory [ 18 ].

In the field of care, AI for health has shown great potential to improve healthcare efficiency, considering the relationship between health factors, including service and management, and ICT factors that include sensors, networks, data resources, platforms, applications and solutions [ 19 ]. For the hospital facilities, AI is one of the most powerful technologies from the perspectives of data, computing power and algorithms. Research in Health 4.0 has been conducted in an interdisciplinary way with a diversified set of applications and functionalities and in terms of its implementation, it has been more commonly found in hospitals’ information flows, especially the ones related to healthcare treatments [ 20 ]. In this context, it is also necessary to consider and to assess the prevailing opinions and expectations among stakeholders regarding ICT health solutions, such as the improvement of factors that affect quality of life, quality of health care, patient’s knowledge, monetary aspects, or data security and privacy [ 21 ]. Although the research trend in the field of chronic care is to keep a continuous monitoring of each patient (promoting continuity of health and social care), tools to identify chronic patients and analyze the use of health services (care pathways) that they perform do not exist yet, and in addition there are no AI models that facilitate the design of integrated care pathways. There is clear evidence of the relevance of organization and management of the technological issue in the health care, concept further reinforced on the light of recent COVID-19 pandemic. Assessment, supply, prioritization, appropriate usage, and exploitation are indeed not a trivial duty, and the final success of any health process is widely affected by technology management issues.

In the field of logistics, AI can be applied in the forms of optimizing ML algorithms for scheduling and transportation planning [ 22 , 23 , 24 ]. This has not been extended to AI-led prognosis applications at least with empirical testing. The currently existing industry standard draws on manual processes to plan and optimize resource use. Software applications are being widely used in hospitals for this problem area, such as ORBIS, Medico or M-KIS that rely on an old architecture and non-intelligent, manual interaction with users. Even specialized software modules such as myMedis support the whole process of OR management and related resource planning but still do not use AI-based technology and thus are not able to cope with rising complexity in resource planning optimization [ 25 , 26 , 27 ]. It has been reported that AI adoption by key stakeholders such as doctors remains low [ 28 ], and that existing applications do not cater enough to the specific needs of human stakeholders that are supposed to interact with the systems [ 29 ]. Accordingly, a focus on human–computer interaction (HCI) spanning pre-design, design and post-design phases as well as catering to user, system, task, and interaction characteristics [ 30 ] holds the potential to increase AI adoption and user satisfaction [ 31 ]. While expertise in HCI has been developed in the fields of computer science [ 32 , 33 ], it has not been systematically applied to the hospital context.

3. Use Cases Descriptions and Expectations

In the field of diagnosis, we propose to advance the methods that intelligently utilize heterogeneous data from various sources and novel AI-based methods for supporting medical diagnosis and decision making inside clinics. More specifically, we propose to increase the utilization of AI-based methods in four selected use cases: diagnosing coronary artery disease (CAD), assessing fetal state during labor, diagnosing epidermolysis bullosa (a rare genetic disease) and diagnosing arrhythmias automatically. All the use cases provide heterogeneous data, which at the same time is a challenge for the medical experts to handle and on the other hand provide a possibility for the rise of novel AI-based methods in supporting diagnosis and clinical decision-making. AI-based methods also enable detection of factors in medical diagnosis that are unnoticeable for humans. Collaboration between technical and medical experts is crucial to co-create such tools to be used in clinics that are highly acceptable, highly deployed, and provide real value for patients, doctors and societies.

3.1. Use Case 1: Coronary Artery Disease Diagnosis

Among all routinely available diagnostic tests, coronary CT angiography (CCTA) has the highest sensitivity (95–99%) for detection of coronary artery disease (CAD), with a specificity of 64–83%, and it has recently set up as the first-hand diagnostic tool for stabile chest pain. However, after CCTA there are still several patients for whom the diagnosis and reason for symptoms remains unclear and further imaging studies (myocardial perfusion and/or invasive coronary angiography) are needed to decide the best way of the treatment. Training a ML algorithm to recognize those cases for whom further imaging is likely to provide essential information among the unclear cases with suspected CAD would improve the cost-efficiency and logistic of the diagnosis of chest pain patients. In other words, the aim would be to develop a tool for evaluating the risk of the patient to have prognostic CAD for customized clinical decision-making. The number of the patients with suspected CAD transmitted to hospital for diagnostic imaging is likely to grow in the future worldwide due to recently published clinical guidelines emphasizing the use of CCTA. For the study, a number of contemporary CCTA studies imaged and essential clinical data (age, sex, cardiovascular risk factors and medication) could be used to train a machine-learning algorithm such as Disease State Index (DSI), which is a method to quantify the probability to belonging to a certain disease population, originally developed to support clinicians in diagnosing Alzheimer’s Disease [ 34 ].

3.2. Use Case 2: AI Based Automatic Arrhythmia Analysis

Atrial fibrillation (AF) is the most common sustained arrhythmia and is associated with significant morbidity and adverse outcomes (stroke, heart failure, death). Overall, AF is associated with five-fold greater risk of stroke. Anticoagulation therapy has been demonstrated to reduce AF-related stroke risk significantly. Paroxysmal AF (PAF) is a self-terminating recurrent form of AF. The diagnosis of PAF is often tricky since PAF episodes can be short in duration, asymptomatic and the episode incidence can be low. It is estimated that the stroke causes total costs of EUR 45 billion/year across Europe. In European countries, 1.5 million peoples are diagnosed with stroke every year, 9 million are living with stroke and it is responsible for 9% (0.4 million) of all deaths in EU [ 2 ]. Cryptogenic stroke (CS) and transient Ischemic Attack (TIA) patients and cardiac surgery patients are the three most clinically significant patient groups where PAF is often underdiagnosed. In this use case, state of the art AI-based arrhythmia analysis algorithms are developed for PAF-screening in patients with TIA or cryptogenic stroke and detection of post-operative atrial fibrillation in cardiac surgery patients. AI-based automatic arrhythmia analysis implemented in wearable sensors enables longer monitoring time with improved patient usability and still requires minimal effort from healthcare professionals. Developing novel, AI-based non-invasive methods for PAF screening, using simple wearable ECG or PPG measurement would lead to increasing rate of PAF diagnosis in cardiac surgery, CS and TIA patients. These monitoring methods will be easily exploitable and inexpensive. The timely diagnosis of PAF has an important impact since anticoagulation may save the patient’s life or prevent stroke-related disabilities such as paralysis, aphasia and chronic pain. There is a high-cost saving potential, since one prevented stroke can save EUR 20,000 of direct medical costs and more than EUR 100,000 of indirect costs (disability-adjusted life years lost).

3.3. Use Case 3: Fetal State Assessment during Labour

Cardiotocography (CTG), also known as electronic fetal monitoring (EFM), is used for fetal assessment before and during labour and largely replaced the use of intermittent heart rate auscultation. Visual interpretation of CTG traces is characterized today by a great inter- and intra-observer variability with low specificity. EFM has been shown to lead to unnecessary medical interventions such as caesarean section and vaginal-operative deliveries, with the associated health consequences and economic costs. The low specificity for identifying fetal hypoxia can be partially interpreted in the context of observer variability. CTG recording is widely performed for fetal assessment during delivery and has become routine in most hospitals worldwide. A software program connected to the electrodes of the electronic fetal monitoring system (EFM) registers fetal and maternal data such as fetal heart rate and its variations, maternal heart rate, uterine contractions and fetal movements. Currently, the most specific available CTG interpretation system is the FIGO (Fédération Internationale de Gynécologie et d’Obstétrique) classification, which is most commonly used worldwide [ 35 ]. Fetal outcomes after delivery are being measured by assessing following two parameters: (1) arterial pH directly after birth (blood from the umbilical cord); (2) APGAR score assessment at 1, 5 and 10 min after delivery. This not only offers information about the fetal state, but also gives observer (obstetricians and midwives) direct feedback about previous CTG interpretation during delivery as well as prediction of fetal hypoxia/acidosis. An arterial pH under 7.15 is considered to be pathologic and is a direct indicator of fetal hypoxia. An APGAR score under 7, measured 5 min after delivery is also considered to be pathologic. APGAR as scoring system based on five fetal features—appearance, pulse, grimace, activity and respiration—providing information about the status of the new-born after delivery [ 36 ]. Considering the problematic of observer variability, four scenarios are possible when CTG interpretation is performed by obstetricians or midwives: (1) normal CTG, normal outcomes (pH/APGAR); (2) pathological CTG, normal outcomes (pH/APGAR); (3) normal CTG, pathological outcomes (pH/APGAR); (4) pathological CTG, pathological outcomes (pH/APGAR). By introducing AI interpretation, the purpose is to improve scenario 2 and 3, which will in most cases lead to avoidance of surgical interventions, since the main problem of CTG is specificity; or to performing interventions at moments where one would otherwise refrain from doing so (version 3). The AI system could provide feedback when fetal asphyxia is expected (pH < 7.15 or APGAR at 5 min < 7), as well as warnings, if applicable. The proposed AI (or ensemble of several AI instances) would help in removing the existing great inter- and intra-observer variability and would lead to a direct and positive impact on effectiveness and efficiency through: (1) decrease of unnecessary caesarean section and instrumental delivery; (2) increase of specificity for identifying fetal hypoxia; (3) decrease of unnecessary health costs derived from unnecessary surgical procedures.

3.4. Use Case 4: Diagnosis in Epidermolysis Bullosa, a Rare Genetic Disease

In Europe, a disease is considered rare when it affects less than 1 in 2000 people. There are more than 7000 rare diseases (RDs) worldwide, about 80% of them has a genetic origin and approximately 75% affect children. RDs are estimated to affect 350 million people globally [ 37 ]. In better-resourced countries, correct diagnosis of rare genetic diseases takes on average between 5.5 and 7.5 years. In Europe and United States, nearly half of the first diagnoses are only partially correct. The deployment of effective diagnostic procedures is hampered by the underestimation of the true disease frequency (owing to the lack of RDs’ awareness) and by an insufficient knowledge of the disease pathophysiology and natural history combined with the paucity of validated disease-specific biomarkers. Epidermolysis bullosa (EB) is a group of inherited, genetic diseases in which the skin (and the mucous membranes) is very fragile and forms severe, chronic blisters and lesions after even minor frictions or trauma. This rare genetic disorder affects all genders, ethnic and racial groups and determines either an early death or a long-term debilitating and life-threatening condition, since the severe blistering and associated scarring and deformities result in poor quality of life and reduce life expectancy. In the world there are about 500,000 persons affected by this disease and 36,000 in the European Union (EU). EB can be classified into four major subtypes, such as dystrophic EB (DEB), junctional EB (JEB), EB simplex (EBS), and Kindler Syndrome depending on the gene mutations and the level of skin cleavage [ 38 ]. Within the subtypes, EB has different severity levels and clinical manifestations. There is an urgent need to develop efficient methods for the early diagnosis of the EB subtype, the prediction of the disease progression and, consequently, the selection of individualized, precision therapeutic strategies. In this endeavour, “omics technologies”, as genomic analysis by means of next generation sequencing (NGS), have recently found applications in the diagnosis, molecular subtyping, and follow-up prediction of EB. Information retrieved from these technologies represents a substantial increase in the amount of data that can be used to support EB patients, provided that advanced computational methods are available for their integrative and combinatorial analysis. In this use case, state-of-the-art AI algorithms are developed and applied for supporting early diagnosis, sub-classification, and therapeutic stratification of EB, as an example of rare genetic disease. In particular, AI-based methods will be applied to the integrative analysis of biological (genomics, molecular, immunological, and images) and epidemiological (medical records) data with the aim to: (1) support disease and disease subtype diagnosis; (2) identify distinctive features (genomic lesions, proteins, and immunological states) associated to disease severity (biomarkers) for the prediction of disease progression; (3) detect molecular signatures for guiding patient stratification for novel means of treatment (precision therapeutics). ML algorithms can be trained to integrate phenotypic and clinical data for the prioritization of disease-related genes and mutations, for the prediction of the pathogenicity and disease clinical relevance of genetic variants, and for the identification of pathogenic variant combinations. Furthermore, AI-based methods could be used for disease comprehension and therapeutic target selection by unravelling the affected genetic and molecular players and pathways. AI and ML can be applied to detect anomalies in gene expression and to correlate transcriptional patterns with molecular mechanisms and clinical phenotypes, to learn low frequency patterns, and to deliver automated class attribution [ 37 ]. Results from these analyzes would facilitate the recommendation of optimal treatment approaches and the identification of reliable biomarkers of normal versus pathogenic states and of response to therapeutics interventions. AI methods focusing on removing the existing limitations in the correct diagnosis of EB subtypes and in the prediction of the clinical course of EB patients might achieve at least the same average accuracy as medical doctors following the latest consensus reclassification of inherited EB. The AI-based integrative analysis of biological and medical data will have a direct and positive impact on effectiveness and efficiency through: (1) decrease in the time needed for the diagnosis of the correct EB subtype and the stratification of the patient for the most effective therapeutic treatment; (2) increase in the number and efficacy of diagnostic and prognostic biomarker; (3) increase in the efficacy of selection criteria to identify patients who will benefit from ex vivo gene therapy; (4) decrease of unnecessary life-threatening conditions and health costs derived from delayed diagnosis and treatment administration.

In the field of care, AI will be applied in four other use cases: to improve the management and decision support process, specifically in the chronic care pathway and resources characterization, simulation of demand and prognosis, adverse events identification and prevention, chronic resources management support tool and monitoring of the recovery process. Novel innovative tools for simulation and prognosis would become available, projecting the demand in terms of health resources for a given characteristic population in a territory, considering temporary projections of frailty condition of population and patients. As for recovery monitoring, contactless determination of vital signs will suppose an advanced functional aspect by monitoring of all patients and not only critical cases. Patients will benefit from reduced restrictions due to cables and devices. In addition, there is a time saving for nursing staff, as they do not have to put the devices on the patient and disinfect them. Regarding prevention of adverse critical conditions, the proposed approach relies on the analysis of the entire temporal series of vital signs by means of deep neural networks and hybrid approaches.

3.5. Use Case 5: AI Chronic Management and Decision Support Engine

According to the data of the World Health Organization (WHO), respiratory diseases together with cardiovascular diseases are leading causes of death and disability in the world. Considering this premise, the use of case will focus on the analysis of data from chronic patients diagnosed with one of these four common pathologies: COPD, asthma, coronary heart disease (e.g., heart attack) and cerebrovascular disease (e.g., stroke). The objective would be to apply AI in the clinical context of chronic care to characterize the pathways and resources used, as well as anticipate the demand of resources in order to optimize the economic costs. ML could be then used to analyze data of patients related to clinical parameters (e.g., laboratory tests), use of resources (e.g., hospitalizations), sociodemographic data (e.g., age, gender), and quality of life, among others. The AI engine would be able to support two analysis processes: the chronic care pathway and resources characterization (stratify patients by degree of frailty and map pathways), and resources demand simulation and prognosis (according to each pathway/patient strata).

3.6. Use Case 6: Chronic Resources Management Support Tool

As stated by the surveyed hospitals, efficient and effective scheduling of the resources is a challenge for most hospitals. Possible resources to be scheduled are patients’ beds, material, medicament and assistance kit, medical equipment (e.g., diagnostic machines) or operating theatres. The goal would be to automatically schedule the usage of the considered resources as well as to measure and improve quantitative KPIs considered relevant for the most significant hospital metrics, e.g., cost, service level, delivery time, resource utilization, etc. To achieve this objective it is necessary to carry out the following activities: (1) translating hospital needs, often presented in a medical language, in technical concepts; (2) define the scheduling problem to be tackled by the intelligent algorithm and input data; (3) development an intelligent algorithm to automatically schedule the usage of resources and to measure quantitative KPIs over time; (4) test and validation of the intelligent algorithm using real datasets with the aim to fine-tune the procedures and selection rules implemented in the algorithm; (5) continuous learning of the intelligent algorithm by its utilization, performances and evolution of the surrounding environment.

3.7. Use Case 7: Adverse Events Identification and Prevention

Clinicians require support in the identification and prevention of adverse clinical conditions (ACC), as well as in identifying the main related care pathways. The technology could support the clinician in the automatic identification of ACC, such as a reaction to a new drug assumed by the patient after a change of her/his treatment plan. The AI tools could analyze data caught by vital signs monitoring systems, such as heart rate, pressure, body temperature and other data coming from the patient, such as information inferred by dialog systems based on natural language processing that would periodically interact with the patient to identify specific symptoms. Additionally, the tools would be able to support clinical staff in case a change within the care pathway is needed due. The objective would be to identify and forecast ACC for patients with non-communicable chronic diseases, particularly referring to cardiovascular diseases, by using AI. Models and tools for the automatic identification of ACC would be preliminarily realized adopting retrospective data and classic ML algorithms using current guidelines on the management of diseases of interest. Such models and tools, however, could be continuously improved, following a continuous learning approach. Successively, the prevention of ACC could be attempted by advanced classification systems, based on a combination of deep learning and reinforcement learning approaches that will analyze time series data concerning the patient condition evolution at different stages of the care pathway.

3.8. Use Case 8: Monitoring of the Recovery Process

Monitoring of the recovery process is a key hospital process. In order to achieve a high, continuous quality, vital parameters have to be monitored constantly. Vital parameters such as the heart rate or the respiration rate are key indicators for the current health status, urgent emergencies and the recovery process. Especially, persons with chronic diseases benefit from a continuous monitoring. In areas such as operation theatres or ICUs, there is a high coverage, whereas in normal wards or floors there is little to no coverage. The objective would be to remote determination of vital parameters such as heart rate and respiration rate for an improved recovery monitoring in a patient friendly method especially for chronic diseases. This could be realized by optical sensors with remote working mode and AI algorithms such as CNNs, BNNs or adaptive optical flow. To achieve the objective it is necessary to carry out the following activities: (1) identifying of optimal positioning of optical sensors within the hospital; (2) analysis of algorithms of remote vital parameter determination in clinical environments; (3) transfer and implementation of algorithms to the clinical setting; (4) evaluation of algorithms in clinical setting by means of reference systems, which would stayed synchronized; (5) interface protocol for transmission of vital parameters to central processing unit in the hospital. It should be guaranteed that only this meta data are transferred but not the raw data, thus protecting the privacy of the patients.

In the field of logistics, AI can be implemented for example in three different use cases as described below. The main focus is the optimization of resource use. It is expected that AI will help to better predict material consumption and needs in the whole process. Besides material consumption, transport planning is a further focus point in the field of logistics.

3.9. Use Case 9: Material Consumption Recognition and Prognosis

Currently, in the University Hospital in Essen as well as many other hospitals in Europe the documentation of used materials with hospital patients is a non-digital paper-pencil process consuming a lot of human work time. Therefore, digital improvements regarding automated capture system for material consumption are a prominent request in hospitals and addressed in this use case. Together with an industry partner an innovative care trolley is developed with a camera system and the complementary AI-based software using ML to recognize the consumed objects with patient processes automatically. User interaction can be implemented according to current state-of-the-art concepts. It will provide a data recognition and prognosis tool relating actual material consumption to patient cases and therefore enabling a bottom-up planning and prognosis for optimized procurement and logistics in hospitals.

3.10. Use Case 10: Optimization of Human-Robot Teams in Hospital Logistics Operations

Odense’s University Hospital (OUH) will benefit from a reactive AI-based resource management and scheduling system for material transport logistic operations. The main goal is to improve upon current task management systems with the inclusion of an AI-driven optimized scheduler that will be able to oversee all the available robots and to plan, schedule and assign tasks to the relevant hospital workforce, mainly logistic robots but also employees. The proposed task management software will have several functions and therefore will contain several different conceptual elements: (1) an automated task-generation system, based on Reinforcement Learning (RL) algorithm, that analyzes the relationship between room use and materials requirements to predict what will be needed where and when based on past experience; (2) a scheduling element that knows what transport resources are available to it, their status and where they are; and can create an optimal schedule out of transport requests generated from user input or the task generation above; (3) a reactive planning element that will rework the schedule regularly, e.g., either every hour or when new on-demand transport requests are received; (4) a transport optimizing element that analyzes the efficiency of the transport and adjusts scheduling parameters to produce maximal transport for minimal energy use and minimal task requests to humans; (5) a route generator element that creates efficient routes for the robots and sends these to robots with their new tasks, in accordance with the schedule, coupled with a route status analyzer which takes input from sensors on the robots and around the hospital to determine the location of any blockages; (6) A sensory data analyzer that can use incoming data from various infrastructure sources to inform the decision-making elements, e.g., use of elevator position to inform the route generator or use of smart cameras that can measure room occupancy for the task generator; (7) A representation of (a) task criticality, i.e., planned, urgent and critical in emergency situations, (b) the current status of the material flow, (c) the robots (name, capabilities, location, current task and status) and (d) item transport requests (also available in a form readable by humans); (8) and a supervision element that will be utilized to identify and criticize any suboptimal decisions made by the scheduler and provide feedback that will be used as input for a reinforcement learning sub-component. Task and material flow reports collected and shared by the hospital service and logistics departments of OUH, currently exceeding 555,000 entries describing various material flow logistic cases, i.e., transfer of medication, healthcare equipment and samples, will provide a variety of types of inputs and tasks. The system could automatically obtain information from various hospital software sources, e.g., human workforce positions provided by the proposed event-based messaging system by updating and adapting the current emergency messaging solution elevator status and sensors in the hospital.

3.11. Use Case 11: Co-Development and Evaluation

Bayındır Hospital Söğütözü in Ankara is one of the three high-capacity hospitals that belongs to Bayındır Healthcare Group. Bayındır Healthcare Group have three hospitals, one medical center and seven dental clinics. All healthcare facilities material management system can be centrally monitored and controlled. This provides an additional opportunity to study the impact of planned AI implementations over multi-location inventory systems. The hospital has specific experiences and requirements regarding healthcare logistics. It has an existing barcode scanning system for collecting healthcare and inventory information that aggregates centrally for the planning the availability of medical supplies and logistics management. However, the hospital may still benefit from a new picture recognition and AI-based system in terms of time savings, reductions in human error, and an increase the safety by reducing the contact between the healthcare staff and patients. Furthermore, material management and operation room scheduling are highly interrelated in practice. Using the OR schedules to trigger the purchase of perioperative materials is expected to further reduce inventory costs and increase operational efficiency compared to independent material management systems [ 39 ]. In a comparison to standalone applications of automated inventory tracking, predictive logistics, and cognitive automation, an additional understanding of the impact of integrated AI applications on healthcare logistics operations will bring several challenges, including data storage and management, data exchange, security and privacy, and integrated decision-making.

4. Discussion: Benefits and Challenges for AI in Hospitals

The specific benefits and data as well as AI application challenges are presented and discussed in this section, based on the outlined case studies and additionally directed towards the contribution against pandemic situations, such as COVID-19.

The use cases presented in Table 3 are distinguished by specific aspects often related to the area of interest, e.g., diagnosis, care, treatment, logistics or rehabilitation, or to the targeted goals, e.g., increase the efficiency of a certain health care process, improve its quality, or increase the service level. However, the detailed description of the aforementioned case studies suggests how all the involved hospitals are affected by common challenges and potential barriers to the adoption of AI to their healthcare processes on regular basis. In particular, it is possible to define three main issues which should be properly managed to ensure an efficient and effective adoption of AI tools and techniques in the healthcare delivery processes which distinguish European hospitals. The first aspect to be considered is the human acceptance and the real adoption of AI solutions in hospitals. The resistance to automated and partially obscure tools which offer assistance in several healthcare services is a major obstacle to overcome. Leveraging such tools in traditional diagnosis, care and treatment processes is useful but often distinguished by a low level of trust, in particular by doctors and medical personnel. Furthermore, the usage of such AI solutions should not increase the complexity or time required to complete certain medical process, therefore offering an adequate and well-designed interaction with human adopters. The second challenge to be tackled to foster the adoption of AI in European hospitals is the proper management of medical data. This information is distinguished by some features which make their storage and usage much more sensitive than other data typically collected in digital environments.

AI Use Cases, AI Methods and Outcomes.

However, as COVID-19 dramatically revealed, the value beyond medical data is huge. In particular, the opportunity to systematically collect data concerning the patient conditions, made diagnosis, performed treatments and defined care offer to the hospitals of the future the chance to significantly increase the efficacy and efficiency of the healthcare services delivered. The last area involved by AI structural adoption in European hospitals deals with technology selection and ethics. The former includes the complex and interrelated process of selecting a novel technology for its adoption in healthcare services, as represented by the solutions based on AI algorithms. The assessment of the most appropriate AI based technology to be adopted to ease diagnosis, treatment or care activities is a complex and distinguished by uncertain and multiple feasible outcomes with different and contrasting scenarios. The latter deals with the ethical aspects involved in the adoption of AI tools and techniques, from machine based medical decision to personalized treatments, from sharing of personal health data to acceptance of robot medical personnel. Finally, a latter aspect concerning the challenges of adopting AI in hospitals necessarily has to be mentioned, e.g., the appropriate involvement of adequate stakeholders. Indeed, this last issue is of fundamental importance to ensure the real usage of AI-based solutions in daily hospital activities by doctors, acceptance of renovated treatments and procedures by patients as well as commitment by local administrators to this modern form of health care assistance. Therefore, the process of stakeholder commitment is of paramount importance and should be adequately planned and implemented. Considering all the abovementioned challenges and potential obstacles, the following paragraphs propose possible solutions to overcome these difficulties, to ensure the adoption of AI solutions in European hospitals and maximizing the efficacy of the innovation provided. In particular, the proposed actions are grouped into three categories, human–computer interaction, medical data space, and guidebook and ethics. The linkage between these transversal activities with the application areas proposed in the manuscript is presented in the following Figure 2 .

Linkage between transversal activities and application areas for AI adoption in European hospitals.

Human–Computer-Interaction : Despite progress in the field of health care data analytics, resulting in more and more prototypes and technical advancement, actual adoption by key stakeholders such as doctors remains low [ 28 , 29 ]. This aspect will rise in relevance when the respective systems increase in intelligence and analytical capability. Accordingly, an increased focus on human–computer interaction spanning pre-design, design and post-design phases as well as catering to user, system, task and interaction characteristics [ 30 ] holds the potential to increase AI adoption and user satisfaction in clinical practice [ 31 ].

Medical Data Space : In addition, data connections in a Medical Data Space (MDS) with distributed AI applications will help to share resources and to support specially and severely affected regions and hospitals. In additions, overall data transparency and analysis will help to fight virus outbreaks earlier through faster detection and containment options due to AI analysis. The Medical Data Space (MDS) is a specialization of the International Data Space (IDS), which provides a trustworthy, secure and cross-domain data space allowing to build an economy of data between companies of all domains and sizes. IDS was the result of R&D activities in 2015 and is now actively promoted through the Industrial Data Space Association. It is in cooperation with the OPC foundation, the FIWARE foundation and the Industrial Value Chain Initiative and the Platform Industry 4.0. The IDS and thus the MDS define an architecture of data providers and consumers, which are linked through connectors forming the data space. The architecture is defined in the IDS document describing the layers of the architecture model which in turn describe the key components necessary to realize a data space [ 40 ]. The first prototype has been presented in 2018 at the Hannover fair. The MDS concept targets the connectivity of local data spaces in hospitals for analytics and the application of AI-based algorithms for research or hospital internal use. Therefore, special services are necessary to not only store and manage the transfer of medical data securely and maintaining the sovereignty of the data owner, but it must additionally conform to requirements on anonymity and protection of personal medical data sets. Here, the element of value-added services for the data space becomes relevant enabling pseudonymization and anonymization features in the process.

Medical data of patients is a highly sensitive and therefore regulated asset which requires handling in a secure and protected environment. The Medical Data Space (MDS) builds upon the international data space to deliver a secured, controlled data storage and processing environment to build an economy of data between providers and consumers retaining sovereignty and control. The MDS extends this to address the additional medical constraints. They key concept in MDS is the trusted connector which links both parties and enforces the security and privacy policies defined. In addition to access management the MDS architecture introduces data-processing services (data-apps) which can preprocess data before or after transfer. As AI-driven smart hospitals rely basically on data targets the connectivity of local data spaces in hospitals for analytics and the application of AI-based algorithms for research or hospital internal will be used. Therefore, special services are necessary to not only store and manage the transfer of medical data securely and maintaining the sovereignty of the data owner, but it must additionally conform to requirements on anonymity and protection of personal medical data sets. Here the element of value-added services (data-apps) for the data space becomes relevant enabling specifically pseudonymization and anonymization features in the process. In future works, we plan to demonstrate that medical data space technology can provide the foundation for the development and deployment of novel AI and data management data-apps. Specifically, a pilot program for the analysis and management of in-hospital cardiac patient intervention treatment with the goal of understanding and analyzing several key factors that impact the ability and capacity of a hospital to provide treatment. The location for this future installation will be the Evaggelismos Hospital in Athens.

Guidebook and Ethics : There is clear evidence of the relevance of organization and management of the technological issue in the health care, concept further reinforced on the light of recent COVID-19 pandemic [ 41 ]. Assessment, supply, prioritization, appropriate usage and exploitation are indeed not trivial duties, and the final success of any health process is widely affected by technology management issues. In the modern re-setting of health-care delivery via technology innovation, data driven management, health technology assessment, clinical practice guidelines as well as medical leadership are the main topics that have to be addressed [ 42 ]. Knowledge management and technology innovation with their continuously growing potentiality can indeed transversally represent the answer to the demand of efficacy and efficiency of the system. Furthermore, great expectations are placed in information and communication technologies (ICT) with their contribution in the development of eHealth and closely in AI with its paramount applications in the various sectors of medical practice and public health. The change in clinical practice through and by means of the injection of technological innovation is today decisive to make the health and care systems able to face to the continuous economic, socio-demographic and epidemiological pressures [ 17 ]. However, technological innovation, although important and central, must be carefully examined and accompanied to ensure that it really corresponds to effective social innovation [ 43 ]. Furthermore, as really recently underlined by a joint report of EIT Health and McKinsey [ 44 ]. AI has indeed many potentialities for the improvement in care outcomes, patient experience and access to healthcare services. AI is thought to increase productivity and the efficiency of care delivery and allow healthcare systems to provide more and better care to more people. Finally, it can support the faster delivery of care, mainly by accelerating diagnosis time, and help healthcare systems manage population health more proactively, dynamically allocating resources to where they can have the largest impact and need. As addressed by MedTech Europe, developing AI systems and algorithms for healthcare settings requires specific skillsets which are in short supply, and investment in education and training of professionals involved (e.g., data scientists, practitioners, software engineers, clinical engineers), is mandatory [ 18 ].

Ethical issues are a major hurdle to full-scale AI application use as many cases might bring about risks such as wrong diagnosis or deviant therapy, as well as dissent among personnel due to different opinions regarding correct AI analysis and advice. Therefore, not only HCI issues but also human-human interaction and collaboration issues and ethical questions to be solved and communicated among people first of all before AI can contribute according to the full potential in health care.

AI will play a significant role in future hospital health care systems. Applications such as ML will further advance the development of processes in several fields inside the hospital, of which we focus in medical diagnosis, logistics and care in this article. Important obstacles remain, such as regulations, integrations to the Electronic Health Record (EHR), standardization, medical devices certificates, training professionals, costs, updates—but this is manageable. It is important to stress that AI applications will not replace human clinicians but help them to concentrate on important human-related processes and to make correct diagnoses with less analysis and decision time. This hopefully provides them with time and focus to support patients from a specific human perspective. As a result of the developments in computational power and algorithmic advancements, combined with digitalization and improvements in data collection methods and storage technologies, the healthcare sector today is supported by AI, ML and robotics as never before in the history of medicine. Besides monitoring large-scale medical trends, these new technologies also allow measurement of individual risks based on predictions from big data analysis. AI has a key function in the healthcare management of the future. Research has already proven the game changing potential of AI in various fields of healthcare, such as those outlined in the use cases in this article. AI-based methods have been successfully developed to address several healthcare logistics problems such as appointment planning, patient and resources scheduling, resource utilization, and predicting demand for emergency departments, intensive care units, or ambulances [ 45 ]. In addition, there already exist a number of research studies which suggest that AI can perform at least as good as humans at basic healthcare functions, such as diagnosis. Today, malignant tumors are spotted more successfully by algorithms than humans [ 46 ]. As a consequence of rapid technological advancements, combined with ML’s enhanced ability to transform data into insight, many of the medical tasks previously limited to humans are expected to be taken on by algorithms [ 47 ]. However, there are several reasons why it will take a long time before AI might take over comprehensive fields of activity from humans in hospitals and healthcare: recent developments show that AI systems will not replace humans on a large scale, but rather will support them in their efforts of patient care. Progressing into future times, healthcare specialists can switch to tasks and job designs focusing on unique human skills such as empathy and care. One risk within this development might be the position of healthcare providers who are unable or refuse to work in collaboration with AI applications, endangering their contributions and jobs. The most important obstacle regarding AI applications in healthcare are not the capabilities or benefits of the technologies themselves, but their applicability in medical practice. Widespread use of AI systems requires approval by regulating institutions, integration with existing systems, sufficient standardization with similar products, training of healthcare professionals, and solutions regarding issues of data privacy and security. These challenges will eventually be solved, but it will take significant time and resources [ 46 ]. The COVID-19 crisis has revealed the challenges for healthcare systems—also for future pandemic situations. This increased attention to the potential of AI in healthcare as one means of pandemic management and prevention. Major challenges in responding to COVID-19, such as managing limited healthcare resources, developing personalized treatment plans, or predicting virus spread rates, can be addressed by recent developments in AI and ML. Wynants et al. [ 48 ] have already listed 31 prediction models in a review of early studies of COVID-19. The prospective post-COVID-19 era in preparation for future pandemic events will likely feature advanced healthcare solutions in combination with operation research modeling [ 49 ]—and AI will be a crucial part of it as outlined in this paper with 11 use case studies from European hospitals. The challenges connected to such AI applications such as data management (HCI) have to be addressed soon in order to prepare hospitals for future challenges, e.g., pandemic situations [ 50 ]. This is a core challenge for health care management science and the implication for hospital practice in order to apply the full potential of AI and ML to health care systems [ 51 ].

Author Contributions

Conceptualization, M.K., M.H. (Marcus Hintze), M.I.; methodology, M.H. (Marcus Hintze), M.I., F.R.-R., F.P., F.A.-M., D.Ç.; validation, F.A.-M., D.Ç., T.L., M.J., O.U., M.H. (Marcus Hintze), J.A.L., S.B., A.-P.R.; formal analysis, M.K., B.V., W.T., D.G.; investigation, M.I., F.R.-R., F.P., F.A.-M., D.Ç., T.L., M.J., O.U., B.V., D.G., R.D.-G.; writing—original draft preparation, M.I., F.R.-R., F.P., D.Ç., T.L., M.J., O.U., M.H. (Marcus Hintze & Marja Hedman), J.A.L., S.B., B.V., W.T., R.D.-G.; writing—review and editing, M.K., M.H. (Marcus Hintze), D.G.; visualization, M.H. (Marcus Hintze), F.R.-R., T.L., M.J., A.-P.R.; supervision, M.K., M.I., F.A.-M. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

HospiNEXT - Acceptance & Satisfaction

Client: a leading hospital in johor.

The hospital was already using another application for managing all their administrative and clinical related activities. They were not satisfied with the performance and wanted to evaluate HospiNEXT. We conducted a case study for the same through exploring the influential factors that affect the acceptance and satisfaction levels among different healthcare professionals in the Hospital.

DSS developed an exclusive approach to

We conducted objective quantitative survey methods to collect data directly from different types of Hospital Information Management system users. The survey covered 5 sections:

• Demographic user information
• Assessment on existing Hospital Information Management system
• Accessibility and availability of computers
• Impact of using Hospital Information Management system on patient care

We found that the most influential factors are :

• Availability of computers (laptops & computers on wheels) - To facilitate direct and immediate data entry and information retrieval processes
• Slow performance and responsiveness

It is observed that main areas of potential improvement are:

System performance – Improving the performance of the Hospital Information Management system is achieved by -

• Increasing the availability of computers at the point of care
• Making the Application more user-friendly
• Defining new methods for data entry
• Improve the MIS reporting

Organizational support – A very crucial factor that can be achieved by providing training and dedicated time (during working hours) for users to learn and practice on the Hospital Information Management system.

Users' feedback – Better and more reliable channels of communication and feedback are needed to consider users' complaints, suggestions and contribution.

We understood the system functionality thoroughly, the need for changes and improvement. We implemented HospiNEXT in their hospital and appointed a dedicated 2-member team to maintain their information in the application. After 1-month, it is observed that there is a lot of improvement in the system performance with increased information quality. Also our team conducted the user trainings (weekly) and immediate support was provided for all the helpdesk tickets arised. Now we are proud to have this hospital as our permanent client.

Case Study: Redesigning the Inpatient Case Management Team

The discharge planning arm of a patient’s hospital journey primarily lies with the patient’s case manager, a nurse who helps evaluate the patient’s discharge needs and collaborates on the best plan for the patient after discharge. The process for most patients is straightforward: they either go to another facility or gain some temporary support at home and connect with outpatient clinics.

At Boston Medical Center (BMC), however, the discharge planning process is complicated by its complex patient population, who may not have insurance, citizenship, or homes to discharge to. Delays in creating a safe discharge plan can result in unnecessary days in the hospital even after patients are medically cleared for discharge, resulting in unnecessary costs to our healthcare system, unavailable beds for those who need them, and frustration across staff.

Finding solutions to appropriately reduce length of stay and facilitate better throughput is top of mind for clinical operations — at BMC, our average morning occupancy was 98% on our Medical Surgical floors, for example, versus an industry target of just 80%. Further, every week, we identified at least 30–40 patients “stuck” in the hospital. Freeing these beds and back-filling them with new patients creates a significant financial opportunity associated with reducing length of stay.

The hospital’s Central Flow Unit (a team of physician, nursing, and administrative leaders that oversee and improve patient flow) partnered with our case management department to evaluate opportunities to support the team given the complexity of our patient population. We examined three core questions in consultation with other hospitals.

Should our case managers be responsible for discharge planning and utilization management, or split the tasks? Case management departments typically support both the discharge planning process and the utilization management function, which ensures appropriate payor reimbursement given the clinical profile of the patient. In different environments, case managers do both tasks or divide the responsibilities over two groups. Is a team-based, unit-based, or hybrid case management model most appropriate for our hospital’s needs? Case management can be structured to follow either a set number of beds (e.g., beds 1 – 20 on a unit) or a physician team (e.g., general medicine team). Both models have pros and cons that need to be assessed for each environment. Whereas some hospitals localize physician teams to specific units, this alignment was not the case at Boston Medical Center. What is the appropriate number of patients for a case manager to support? Based on the design of the unit vs. team-based alignment, and the inclusion or exclusion of utilization management, a hospital must determine how many patients a case manager can effectively support.

As a result, we designed a new discharge planning structure that has shown early success in reducing length of stay.

Context: What drove the redesign

We historically followed a unit-based structure where each case manager followed a set of beds — a maximum of 18 beds on a Medical Surgical unit — which facilitated strong relationships with patients’ families and other unit-based teams such as nursing. In this model, case managers were responsible for both the discharge planning and utilization management process.

Our physician teams were scattered across many units, increasing the number of case managers each physician team had to interact with, and likewise the number of physician teams each case manager had to interact with. In fact, a review of our data showed that our highest-volume teams were interacting with eight to nine case managers on average. This model created huge inefficiencies, and as they interacted with more case managers, patient length of stay also increased.

Balancing discharge planning and utilization management

After evaluating our systems, we chose a hybrid case management model — pairing the physician teams that were the most scattered with a team-based case manager, and assigning specific beds for the other case managers. This model requires more day-to-day management from leadership to distribute patients among case managers, but provides relief to the physician teams and case managers who were struggling with a high number of interactions.

Pre-COVID, we had settled on keeping the discharge planning and utilization management tasks associated with patients to one case manager. This was our incumbent model. It had synergies across the tasks, and it was also a team structure supported by half the institutions we consulted.

However, COVID-19 response necessitated that our case management department internally split the tasks. A central team managing denials and appeals took on the utilization management tasks for all patients in house, benefiting from the fact that we primarily cared for one diagnosis: COVID-19.

Anecdotally, our floor case managers appreciated the bandwidth to focus on discharge planning afforded by the split of responsibilities. We are now striving to maintain this split given the staff satisfaction.

Appropriate case mangement case load

Our final question concerned how many patients per case manager was appropriate to facilitate efficient and high-quality throughput. Our inquiry into the caseloads per case manager at other institutions quickly highlighted that our case managers were caring for significantly more patients — an average of 17 patients at BMC while the average at other hospitals was 12, accounting for differences in structures.

In the end, with the shift to team-based case management we reduced the case load for the case managers assigned to these teams, accounting for the fact that they will be spread out, an additional challenge in its own right. We maintained the case load for the other case managers, appreciating that they now could focus on discharge planning exclusively, bringing their case load on par with other institutions who have split functions.

Conclusions from case management redesign

Our decisions related to the structure of case management were anchored in additional recommendations meant to strengthen the processes and data systems within the department to make the environment that our case managers work in effective for them. Underscoring these efforts is collaboration across disciplines and strong, on-the-ground leadership ensuring these changes work for the front-line teams. We are assessing the impact of these changes with both qualitative and quantitative metrics and are excited for the potential these changes have for our patients and our staff — initial findings of 0.7 day reduction in LOS for teams in this new program compared to a baseline of five months indicate we are on the right path.

Boston Medical Center is a 514-bed hospital and houses the busiest emergency department in New England. In January 2019, we created the Central Flow Unit (CFU) which oversees patient flow and is co-led by physician, nursing and administrative leaders. The CFU, in collaboration with many stakeholders, has worked to improve patient flow and problems many hospitals encounter. Along the way, we have turned to the literature and our peers across the country to learn more about the challenges and progress against them. In the spirit of collaborating across institutions, we are sharing an inside look at some of our biggest successes in improving inpatient operations and care. Don’t forget to check out our other results in The Hospital Playbook series.

Neha Gaur is the senior director of inpatient operations and cancer care at Boston Medical Center. She co-leads the Central Flow Unit with nursing and physician partners. She graduated from the University of Pennsylvania and the Wharton School of Business with a master's degree in biotechnology, as well as bachelor of science and bachelor of arts degrees.

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How to Design ER Diagram for a Hospital Management System

Healthcare management is a crucial function that comes as the backbone of hospital management . An ER (Entity-Relationship) Diagram therefore functions as a foundation for the organization and visualization of the various entities , attributes , and relationships within a system.

In this article, the steps of building an ER Diagram for a Hospital Management System (HMS) will be examined closely.

ER Diagram for a Hospital Management System

Hospital Management System (HMS) is a comprehensive software tool devised to fast several hospital operations through a single and efficient system. It includes patient management, doctor management , room management , nurse management , test report management , personal data management , billing , and secretary management .

Each module deals with particular functionalities, for example, patient registration, appointment scheduling, the allocation of rooms, the management of medical records, billing and invoicing, and many more.

The system is developed using an ER Diagram( Entity-Relationship Diagram ) which represents entities , attributes , and relationships between them thus ensuring a well-defined database schema organized and structured.

Hospital Management System Features

To design an Entity-Relationship Diagram ( ER Diagram ) for a Hospital Management System ( HMS ) with the entities provided, let’s focus on the following requirements:

• Patient Management: The platform should also allow for the patients registration and the management of their information with details such as demographics , medical history , and current condition in mind.
• Doctor Management: The system should have the functionality of a doctor directory in which details such as doctor’s specialties , contacts , and availability can be set.
• Room Management: The system should be able to assist the department in assigning and inspecting rooms in the hospital , particularly those that are available or not currently occupied.
• Nurse Management: The software should contain necessary functions like the management of nurse data like information about their departments , shifts , and contact details. system
• Test Report Management: The system provides a mechanism for the generation, storage , and retrieval of test reports on patients , which gives detailed information on the tests , results , and dates .
• Record Management: The system should keep track of hospital activities in their records. Each record should have a unique identification number for identification.
• Billing: The system will be handling billing details of services rendered to patients and this will cover all functions such as bill generation, tracking payments and management of insurance details.
• Receptionist Management: The system will have functions that will be able to handle a receptionist role in the hospital. Receptionists should be able to retrieve relevant patient data and do booking of appointments schedules .

Entities and Attributes of the Hospital Management System

A thing in the real world with an independent existence. It is may be an object with physical existence (ex: house, person) or with a conceptual existence (ex: course, job). The are represented by rectangle.

Let’s Defining Entities for Hospital Management System are:

• P-ID : Unique identifier for each Patient
• Name : Name of the Patient.
• DOB : Date of borthf of Patient.
• Gender : Gender of Patient.
• Mob-No :  Contact number of the Patient.
• Age: Age of Patient.

2. Employee

• E-ID: Unique identifier for each Employee.
• Name: Name of the Employee.
• Salary: Salary of Employee
• Sex: Gender of Employee.
• Mob-No: Contact number of the Employee.
• Address: Address of Employee.
• State : State of Employee
• City : city of Employee
• Pin – no : Pin no of Employee
• E-ID (Foreign Key referencing Employee):
• Department : Department of doctor.
• Qualification : Qualification of Doctor
• E-ID: E-ID is a foreign key linking a table to the Employee table through the Employee ID.
• R-ID: It is an room id every room has different room number or ID.
• Type: It define the quality of room such as deluxe, private general etc.
• Capacity: It defines the number of people can stay in room.
• Availability: It define the duration or Availability of room.

6. Receptionist

• E-ID (Foreign Key referencing Employee) : E-ID is a foreign key in a table that references the Employee table, typically used to establish a relationship between the two tables based on the Employee ID.

7. Test Report

• R-ID (Primary Key): Unique identifier for each Room.
• P-ID (Foreign Key referencing Patient): P-ID is a foreign key in a table that references the Patient table, typically used to establish a relationship between the two tables based on the Patient ID.
• Test Type: It define the what kinf of test.
• Result: It shows the test result.
• B-ID: Unique identifier for each Bill.
• Amount: The Amount which Patient has to pay to the Hospital.
• Record-no: Every record book has some number for each Patient .
• App-no: Every app book has some number for each Patient .

Establishing Relationships

Entities have some relationships with each other. Relationships define how entities are associated with each other.

Let’s Establishing Relationships between them are:

• Patient consults Doctor.
• Employee have roles as a nurse, doctor and receptionist within the hospital.
• Patient pays bills for medical services.
• Nurse governs rooms.
• Patient assigned rooms during their stay at hospital.
• Receptionist maintains hospital records.
• Patient has test report.

Relationships Between These Entities

1. patient – doctor relationship.

• A patient can have a relationship with one or more doctors for consultations or treatments.
• A doctor can have multiple patients.
• This is a Many-to-Many (Patient-to-Doctor) as multiple Patient can visit multiple Doctor.

2. Nurse – Rooms Relationship

• A nurse can be assigned to one or more rooms during their shift.
• A room can have multiple nurses assigned to it over different shifts.
• This is a many-to-many relationship between nurses and rooms , each nurse can be assigned to multiple rooms, and each room can have multiple nurses assigned to it.

3. Receptionist – Records Relationship

• A receptionist manages records which could include patient records, appointment schedules, or other administrative documents.
• A record can be managed by one or more receptionists.
• This is a many-to-many relationship between receptionists and records , each receptionist can manage multiple records, and each record can be managed by multiple receptionists.

4. Patient – Bills Relationship

• One patient can have multiple bills.
• One bill is associated with only one patient.
• This is a one-to-many relationship between patients and bills , each patient can have multiple bills, but each bill belongs to only one patient.

5. Patient – Test Report Relationship

• One patient can have multiple test reports.
• One test report is associated with only one patient.
• This is a one-to-many relationship between patients and test reports, each patient can have multiple test reports, but each test report belongs to only one patient.

6. Rooms – Patient Relationship

• One room can accommodate multiple patients over time.
• One patient occupies one room at a time.
• This is a one-to-many relationship between rooms and patients, each room can accommodate multiple patients, but each patient occupies only one room at a time.

Representation of ER Diagram

Tips and Tricks to Improve Database Design

• Normalize the database: Normalize the database to avoid the redundancy and the dependency .
• Use appropriate data types: Choose proper data types for attributes to ensure optimal storage and assure data integrity .
• Index key fields: Indexing primary and foreign key fields can provide better performance with queries.
• Implement constraints: Apply constraints like NOT NULL , UNIQUE , and FOREIGN KEY to ensure data integrity.
• Consider scalability: Design the database with scalability in mind so as to be able to accommodate future growth and adjust to changes in requirements.
• Optimize queries: Write effective SQL queries and factor them out for better performance.
• Document the design: Make sure to document database design in details for better understanding and maintenance in the future.
• Security measures: Implement security measures such as user authentication and authorization to prevent unauthorized access into sensitive data.

The process of designing an entity-relationship diagram for a hospital management system is one that call for a thorough analysis of requirements, identification of entities, definition of attributes, and defining relationships. The foundation of an operational and efficient system lies in the carefully designed ER Diagram as it accounts for both current and future system requirements to cater to the wide range of functional needs of healthcare management.

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Case Study-1

Hospital management system.

XYZ hospital is a multi speciality hospital that includes a number of departments, rooms, doctors, nurses, compounders, and other staff working in the hospital. Patients having different kinds of ailments come to the hospital and get checkup done from the concerned doctors. If required they are admitted in the hospital and discharged after treatment.

The aim of this case study is to design and develop a database for the hospital to maintain the records of various departments, rooms, and doctors in the hospital. It also maintains records of the regular patients, patients admitted in the hospital, the check up of patients done by the doctors, the patients that have been operated, and patients discharged ...

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