hypothesis generating case study

Hypothesis Generation and Interpretation

Design Principles and Patterns for Big Data Applications

© 2024
Hiroshi Ishikawa 0

Department of Systems Design, Tokyo Metropolitan University, Hino, Japan

You can also search for this author in PubMed Google Scholar

Provides an integrated perspective on why decisions are made and how the process is modeled
Presentation of design patterns enables use in a wide variety of big-data applications
Multiple practical use cases indicate the broad real-world significance of the methods presented

Part of the book series: Studies in Big Data (SBD, volume 139)

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Table of contents (8 chapters)

Front matter, basic concept.

Hiroshi Ishikawa

Science and Hypothesis

Machine learning and integrated approach, hypothesis generation by difference, methods for integrated hypothesis generation, interpretation, back matter.

Hypothesis Generation
Hypothesis Interpretation
Data Engineering
Data Science
Data Management
Machine Learning
Data Mining
Design Patterns
Design Principles

About this book

The novel methods and technologies proposed in Hypothesis Generation and Interpretation are supported by the incorporation of historical perspectives on science and an emphasis on the origin and development of the ideas behind their design principles and patterns.

Authors and Affiliations

About the author.

He has published actively in international, refereed journals and conferences, such as ACM Transactions on Database Systems , IEEE Transactions on Knowledge and Data Engineering , The VLDB Journal , IEEE International Conference on Data Engineering, and ACM SIGSPATIAL and Management of Emergent Digital EcoSystems (MEDES). He has authored and co-authored a dozen books, including Social Big Data Mining (CRC, 2015) and Object-Oriented Database System (Springer-Verlag, 1993).

Bibliographic Information

Book Title : Hypothesis Generation and Interpretation

Book Subtitle : Design Principles and Patterns for Big Data Applications

Authors : Hiroshi Ishikawa

Series Title : Studies in Big Data

DOI : https://doi.org/10.1007/978-3-031-43540-9

Publisher : Springer Cham

eBook Packages : Computer Science , Computer Science (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024

Hardcover ISBN : 978-3-031-43539-3 Published: 02 February 2024

Softcover ISBN : 978-3-031-43542-3 Due: 31 January 2024

eBook ISBN : 978-3-031-43540-9 Published: 01 January 2024

Series ISSN : 2197-6503

Series E-ISSN : 2197-6511

Edition Number : 1

Number of Pages : XII, 372

Number of Illustrations : 52 b/w illustrations, 125 illustrations in colour

Topics : Theory of Computation , Database Management , Data Mining and Knowledge Discovery , Machine Learning , Big Data , Complex Systems

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Main Article Content

Hypothesis generation is an early and critical step in any hypothesis-driven clinical research project. Because it is not yet a well-understood cognitive process, the need to improve the process goes unrecognized. Without an impactful hypothesis, the significance of any research project can be questionable, regardless of the rigor or diligence applied in other steps of the study, e.g., study design, data collection, and result analysis. In this perspective article, the authors provide a literature review on the following topics first: scientific thinking, reasoning, medical reasoning, literature-based discovery, and a field study to explore scientific thinking and discovery. Over the years, scientific thinking has shown excellent progress in cognitive science and its applied areas: education, medicine, and biomedical research. However, a review of the literature reveals the lack of original studies on hypothesis generation in clinical research. The authors then summarize their first human participant study exploring data-driven hypothesis generation by clinical researchers in a simulated setting. The results indicate that a secondary data analytical tool, VIADS—a visual interactive analytic tool for filtering, summarizing, and visualizing large health data sets coded with hierarchical terminologies, can shorten the time participants need, on average, to generate a hypothesis and also requires fewer cognitive events to generate each hypothesis. As a counterpoint, this exploration also indicates that the quality ratings of the hypotheses thus generated carry significantly lower ratings for feasibility when applying VIADS. Despite its small scale, the study confirmed the feasibility of conducting a human participant study directly to explore the hypothesis generation process in clinical research. This study provides supporting evidence to conduct a larger-scale study with a specifically designed tool to facilitate the hypothesis-generation process among inexperienced clinical researchers. A larger study could provide generalizable evidence, which in turn can potentially improve clinical research productivity and overall clinical research enterprise.

Article Details

The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives .

CIAO DATE: 8/00

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Results from a hypothesis generating case-control study: herpes family viruses and schizophrenia among military personnel

Affiliation.

1 Division of Preventive Medicine, Department of Epidemiology, Walter Reed Army Institute of Research, Silver Spring, MD 20901, USA. [email protected]
PMID: 18156638
PMCID: PMC2632504
DOI: 10.1093/schbul/sbm139

Background: Herpes family viruses can cause central nervous system inflammatory changes that can present with symptoms indistinguishable from schizophrenia and therefore are of interest in schizophrenia research. Most existing studies of herpes viruses have used small populations and postdiagnosis specimens. As part of a larger research program, we conducted a hypothesis-generating case-control study of selected herpes virus antibodies among individuals discharged from the US military with schizophrenia and pre- and postdiagnosis sera.

Methods: Cases (n = 180) were servicemembers hospitalized and discharged from military service with schizophrenia. Controls, 3:1 matched on several factors, were members not discharged. The military routinely collects and stores members' serum specimens. We used microplate enzyme immunoassay to measure immunoglobulin G (IgG) antibody levels to 6 herpes viruses in pre- and postdiagnosis specimens. Conditional logistic regression was used, and the measure of association was the hazard ratio (HR).

Results: Overall, we found a significant association between human herpes virus type 6 and schizophrenia, with an HR of 1.17 (95% confidence interval [CI] = 1.04, 1.32). Women and blacks had significant negative associations with herpes simplex virus type 2 and cytomegalovirus; among blacks, there was a significant positive association with herpes simplex virus type 1. Among men, there was a HHV-6 temporal effect with an HR of 1.41 (95% CI = 1.02, 1.96) for sera drawn 6-12 months before diagnosis.

Discussion: Findings from previous studies of herpes family viruses and schizophrenia have been inconsistent. Our study is based on a larger population than most previous studies and used serum specimens collected before onset of illness. This study adds to the body of knowledge and provides testable hypotheses for follow-on studies.

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Herpesvirus 2, Human / immunology
Herpesvirus 3, Human / immunology
Herpesvirus 4, Human / immunology
Herpesvirus 6, Human / immunology
Immunoglobulin G / blood
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Schizophrenia / ethnology
Schizophrenia / immunology*
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Outbreak Toolkit

Hypothesis generation, on this page, questionnaires and interviewing, exposure analysis.

Generating hypotheses is an important, but often challenging, step in an outbreak investigation . When generating hypotheses, it is best to keep an open mind and to cast a wide net. A good starting place would be to identify exposures that have been previously been associated with the pathogen under investigation. This can be done by:

searching an outbreak database such as Outbreak Summaries , the Marler-Clark database, and the CDC Foodborne Outbreak Online database (see Tools for links to these databases)
reviewing the published literature using a search engine such as PubMed or Google Scholar.

If the case definition for the illnesses under investigation includes laboratory information in the form of Whole Genome Sequencing (WGS) results, consider investigating where and when the sequence has been seen before. Provincial and federal public health laboratories maintain WGS databases that can contain valuable information for outbreak investigation purposes. PulseNet Canada can provide information about how common or rare the serotype or sequence is nationally, where and when it was last seen, and if it has been detected in any food samples in the past. PulseNet Canada will also be able to check the United States’ PulseNet WGS databases for matches. FoodNet Canada can provide information about whether the sequence has previously been seen in farm or retail samples from its sentinel sites.

While it is important to gather such historical information, the most effective way to generate a high-quality hypothesis is to identify common exposures amongst cases. This can be achieved by interviewing cases using a hypothesis generating questionnaire and analysing exposures.

Hypothesis generating questionnaires

Hypothesis generating questionnaires (or shotgun questionnaires) are intended to obtain detailed information on what a person’s exposures were in the days leading up to their illness. They are typically quite long and ask about many exposures such as travel history, contact with animals, restaurants, events attended, and a comprehensive food history. The time period of interest varies between pathogens, as the exposure period is equal to the maximum incubation period of the pathogen.

When designing a questionnaire, it is important to ensure that the questions are gathering the intended information. Questions should be concise, informal, and specific. Before interviewing cases, questionnaires should be tested to ensure clarity and identify any potential errors.

Case interviewing

Once the questionnaire is developed and piloted, it should be administered to cases in a consistent and unbiased manner. Case interviews can be conducted by one or multiple interviewers. A centralized approach allows a single interviewer to standardize interviews, detect patterns, and probe for items of interest. However, a multiple- interviewer approach is more time-efficient and allows for multiple perspectives when it comes time to identify the source.

Although case interviewing is an important outbreak investigation tool, it is not without its challenges. By the time the outbreak team is ready to conduct the interview, it could be weeks to months after the onset of symptoms. It is difficult for people to recall what they ate over a month ago. Sometimes cases might need to be interviewed multiple times as the hypothesis is developed and refined.

Learning together for better health using an evidence-based Learning Health System framework: a case study in stroke

Helena Teede 1 , 2 na1 ,
Dominique A. Cadilhac 3 , 4 na1 ,
Tara Purvis 3 ,
Monique F. Kilkenny 3 , 4 ,
Bruce C.V. Campbell 4 , 5 , 6 ,
Coralie English 7 ,
Alison Johnson 2 ,
Emily Callander 1 ,
Rohan S. Grimley 8 , 9 ,
Christopher Levi 10 ,
Sandy Middleton 11 , 12 ,
Kelvin Hill 13 &
Joanne Enticott ORCID: orcid.org/0000-0002-4480-5690 1

BMC Medicine volume 22 , Article number: 198 ( 2024 ) Cite this article

Metrics details

In the context of expanding digital health tools, the health system is ready for Learning Health System (LHS) models. These models, with proper governance and stakeholder engagement, enable the integration of digital infrastructure to provide feedback to all relevant parties including clinicians and consumers on performance against best practice standards, as well as fostering innovation and aligning healthcare with patient needs. The LHS literature primarily includes opinion or consensus-based frameworks and lacks validation or evidence of benefit. Our aim was to outline a rigorously codesigned, evidence-based LHS framework and present a national case study of an LHS-aligned national stroke program that has delivered clinical benefit.

Current core components of a LHS involve capturing evidence from communities and stakeholders (quadrant 1), integrating evidence from research findings (quadrant 2), leveraging evidence from data and practice (quadrant 3), and generating evidence from implementation (quadrant 4) for iterative system-level improvement. The Australian Stroke program was selected as the case study as it provides an exemplar of how an iterative LHS works in practice at a national level encompassing and integrating evidence from all four LHS quadrants. Using this case study, we demonstrate how to apply evidence-based processes to healthcare improvement and embed real-world research for optimising healthcare improvement. We emphasize the transition from research as an endpoint, to research as an enabler and a solution for impact in healthcare improvement.

Conclusions

The Australian Stroke program has nationally improved stroke care since 2007, showcasing the value of integrated LHS-aligned approaches for tangible impact on outcomes. This LHS case study is a practical example for other health conditions and settings to follow suit.

Peer Review reports

Internationally, health systems are facing a crisis, driven by an ageing population, increasing complexity, multi-morbidity, rapidly advancing health technology and rising costs that threaten sustainability and mandate transformation and improvement [ 1 , 2 ]. Although research has generated solutions to healthcare challenges, and the advent of big data and digital health holds great promise, entrenched siloes and poor integration of knowledge generation, knowledge implementation and healthcare delivery between stakeholders, curtails momentum towards, and consistent attainment of, evidence-and value-based care [ 3 ]. This is compounded by the short supply of research and innovation leadership within the healthcare sector, and poorly integrated and often inaccessible health data systems, which have crippled the potential to deliver on digital-driven innovation [ 4 ]. Current approaches to healthcare improvement are also often isolated with limited sustainability, scale-up and impact [ 5 ].

Evidence suggests that integration and partnership across academic and healthcare delivery stakeholders are key to progress, including those with lived experience and their families (referred to here as consumers and community), diverse disciplines (both research and clinical), policy makers and funders. Utilization of evidence from research and evidence from practice including data from routine care, supported by implementation research, are key to sustainably embedding improvement and optimising health care and outcomes. A strategy to achieve this integration is through the Learning Health System (LHS) (Fig. 1 ) [ 2 , 6 , 7 , 8 ]. Although there are numerous publications on LHS approaches [ 9 , 10 , 11 , 12 ], many focus on research perspectives and data, most do not demonstrate tangible healthcare improvement or better health outcomes. [ 6 ]

Monash Learning Health System: The Learn Together for Better Health Framework developed by Monash Partners and Monash University (from Enticott et al. 2021 [ 7 ]). Four evidence quadrants: Q1 (orange) is evidence from stakeholders; Q2 (green) is evidence from research; Q3 (light blue) is evidence from data; and, Q4 (dark blue) is evidence from implementation and healthcare improvement

In developed nations, it has been estimated that 60% of care provided aligns with the evidence base, 30% is low value and 10% is potentially harmful [ 13 ]. In some areas, clinical advances have been rapid and research and evidence have paved the way for dramatic improvement in outcomes, mandating rapid implementation of evidence into healthcare (e.g. polio and COVID-19 vaccines). However, healthcare improvement is challenging and slow [ 5 ]. Health systems are highly complex in their design, networks and interacting components, and change is difficult to enact, sustain and scale up. [ 3 ] New effective strategies are needed to meet community needs and deliver evidence-based and value-based care, which reorients care from serving the provider, services and system, towards serving community needs, based on evidence and quality. It goes beyond cost to encompass patient and provider experience, quality care and outcomes, efficiency and sustainability [ 2 , 6 ].

The costs of stroke care are expected to rise rapidly in the next decades, unless improvements in stroke care to reduce the disabling effects of strokes can be successfully developed and implemented [ 14 ]. Here, we briefly describe the Monash LHS framework (Fig. 1 ) [ 2 , 6 , 7 ] and outline an exemplar case in order to demonstrate how to apply evidence-based processes to healthcare improvement and embed real-world research for optimising healthcare. The Australian LHS exemplar in stroke care has driven nationwide improvement in stroke care since 2007.

An evidence-based Learning Health System framework

In Australia, members of this author group (HT, AJ, JE) have rigorously co-developed an evidence-based LHS framework, known simply as the Monash LHS [ 7 ]. The Monash LHS was designed to support sustainable, iterative and continuous robust benefit of improved clinical outcomes. It was created with national engagement in order to be applicable to Australian settings. Through this rigorous approach, core LHS principles and components have been established (Fig. 1 ). Evidence shows that people/workforce, culture, standards, governance and resources were all key to an effective LHS [ 2 , 6 ]. Culture is vital including trust, transparency, partnership and co-design. Key processes include legally compliant data sharing, linkage and governance, resources, and infrastructure [ 4 ]. The Monash LHS integrates disparate and often siloed stakeholders, infrastructure and expertise to ‘Learn Together for Better Health’ [ 7 ] (Fig. 1 ). This integrates (i) evidence from community and stakeholders including priority areas and outcomes; (ii) evidence from research and guidelines; (iii) evidence from practice (from data) with advanced analytics and benchmarking; and (iv) evidence from implementation science and health economics. Importantly, it starts with the problem and priorities of key stakeholders including the community, health professionals and services and creates an iterative learning system to address these. The following case study was chosen as it is an exemplar of how a Monash LHS-aligned national stroke program has delivered clinical benefit.

Australian Stroke Learning Health System

Internationally, the application of LHS approaches in stroke has resulted in improved stroke care and outcomes [ 12 ]. For example, in Canada a sustained decrease in 30-day in-hospital mortality has been found commensurate with an increase in resources to establish the multifactorial stroke system intervention for stroke treatment and prevention [ 15 ]. Arguably, with rapid advances in evidence and in the context of an ageing population with high cost and care burden and substantive impacts on quality of life, stroke is an area with a need for rapid research translation into evidence-based and value-based healthcare improvement. However, a recent systematic review found that the existing literature had few comprehensive examples of LHS adoption [ 12 ]. Although healthcare improvement systems and approaches were described, less is known about patient-clinician and stakeholder engagement, governance and culture, or embedding of data informatics into everyday practice to inform and drive improvement [ 12 ]. For example, in a recent review of quality improvement collaborations, it was found that although clinical processes in stroke care are improved, their short-term nature means there is uncertainty about sustainability and impacts on patient outcomes [ 16 ]. Table 1 provides the main features of the Australian Stroke LHS based on the four core domains and eight elements of the Learning Together for Better Health Framework described in Fig. 1 . The features are further expanded on in the following sections.

Evidence from stakeholders (LHS quadrant 1, Fig. 1 )

Engagement, partners and priorities.

Within the stroke field, there have been various support mechanisms to facilitate an LHS approach including partnership and broad stakeholder engagement that includes clinical networks and policy makers from different jurisdictions. Since 2008, the Australian Stroke Coalition has been co-led by the Stroke Foundation, a charitable consumer advocacy organisation, and Stroke Society of Australasia a professional society with membership covering academics and multidisciplinary clinician networks, that are collectively working to improve stroke care ( https://australianstrokecoalition.org.au/ ). Surveys, focus groups and workshops have been used for identifying priorities from stakeholders. Recent agreed priorities have been to improve stroke care and strengthen the voice for stroke care at a national ( https://strokefoundation.org.au/ ) and international level ( https://www.world-stroke.org/news-and-blog/news/world-stroke-organization-tackle-gaps-in-access-to-quality-stroke-care ), as well as reduce duplication amongst stakeholders. This activity is built on a foundation and culture of research and innovation embedded within the stroke ‘community of practice’. Consumers, as people with lived experience of stroke are important members of the Australian Stroke Coalition, as well as representatives from different clinical colleges. Consumers also provide critical input to a range of LHS activities via the Stroke Foundation Consumer Council, Stroke Living Guidelines committees, and the Australian Stroke Clinical Registry (AuSCR) Steering Committee (described below).

Evidence from research (LHS quadrant 2, Fig. 1 )

Advancement of the evidence for stroke interventions and synthesis into clinical guidelines.

To implement best practice, it is crucial to distil the large volume of scientific and trial literature into actionable recommendations for clinicians to use in practice [ 24 ]. The first Australian clinical guidelines for acute stroke were produced in 2003 following the increasing evidence emerging for prevention interventions (e.g. carotid endarterectomy, blood pressure lowering), acute medical treatments (intravenous thrombolysis, aspirin within 48 h of ischemic stroke), and optimised hospital management (care in dedicated stroke units by a specialised and coordinated multidisciplinary team) [ 25 ]. Importantly, a number of the innovations were developed, researched and proven effective by key opinion leaders embedded in the Australian stroke care community. In 2005, the clinical guidelines for Stroke Rehabilitation and Recovery [ 26 ] were produced, with subsequent merged guidelines periodically updated. However, the traditional process of periodic guideline updates is challenging for end users when new research can render recommendations redundant and this lack of currency erodes stakeholder trust [ 27 ]. In response to this challenge the Stroke Foundation and Cochrane Australia entered a pioneering project to produce the first electronic ‘living’ guidelines globally [ 20 ]. Major shifts in the evidence for reperfusion therapies (e.g. extended time-window intravenous thrombolysis and endovascular clot retrieval), among other advances, were able to be converted into new recommendations, approved by the Australian National Health and Medical Research Council within a few months of publication. Feedback on this process confirmed the increased use and trust in the guidelines by clinicians. The process informed other living guidelines programs, including the successful COVID-19 clinical guidelines [ 28 ].

However, best practice clinical guideline recommendations are necessary but insufficient for healthcare improvement and nesting these within an LHS with stakeholder partnership, enables implementation via a range of proven methods, including audit and feedback strategies [ 29 ].

Evidence from data and practice (LHS quadrant 3, Fig. 1 )

Data systems and benchmarking : revealing the disparities in care between health services. A national system for standardized stroke data collection was established as the National Stroke Audit program in 2007 by the Stroke Foundation [ 30 ] following various state-level programs (e.g. New South Wales Audit) [ 31 ] to identify evidence-practice gaps and prioritise improvement efforts to increase access to stroke units and other acute treatments [ 32 ]. The Audit program alternates each year between acute (commencing in 2007) and rehabilitation in-patient services (commencing in 2008). The Audit program provides a ‘deep dive’ on the majority of recommendations in the clinical guidelines whereby participating hospitals provide audits of up to 40 consecutive patient medical records and respond to a survey about organizational resources to manage stroke. In 2009, the AuSCR was established to provide information on patients managed in acute hospitals based on a small subset of quality processes of care linked to benchmarked reports of performance (Fig. 2 ) [ 33 ]. In this way, the continuous collection of high-priority processes of stroke care could be regularly collected and reviewed to guide improvement to care [ 34 ]. Plus clinical quality registry programs within Australia have shown a meaningful return on investment attributed to enhanced survival, improvements in quality of life and avoided costs of treatment or hospital stay [ 35 ].

Example performance report from the Australian Stroke Clinical Registry: average door-to-needle time in providing intravenous thrombolysis by different hospitals in 2021 [ 36 ]. Each bar in the figure represents a single hospital

The Australian Stroke Coalition endorsed the creation of an integrated technological solution for collecting data through a single portal for multiple programs in 2013. In 2015, the Stroke Foundation, AuSCR consortium, and other relevant groups cooperated to design an integrated data management platform (the Australian Stroke Data Tool) to reduce duplication of effort for hospital staff in the collection of overlapping variables in the same patients [ 19 ]. Importantly, a national data dictionary then provided the common data definitions to facilitate standardized data capture. Another important feature of AuSCR is the collection of patient-reported outcome surveys between 90 and 180 days after stroke, and annual linkage with national death records to ascertain survival status [ 33 ]. To support a LHS approach, hospitals that participate in AuSCR have access to a range of real-time performance reports. In efforts to minimize the burden of data collection in the AuSCR, interoperability approaches to import data directly from hospital or state-level managed stroke databases have been established (Fig. 3 ); however, the application has been variable and 41% of hospitals still manually enter all their data.

Current status of automated data importing solutions in the Australian Stroke Clinical Registry, 2022, with ‘ n ’ representing the number of hospitals. AuSCR, Australian Stroke Clinical Registry; AuSDaT, Australian Stroke Data Tool; API, Application Programming Interface; ICD, International Classification of Diseases; RedCAP, Research Electronic Data Capture; eMR, electronic medical records

For acute stroke care, the Australian Commission on Quality and Safety in Health Care facilitated the co-design (clinicians, academics, consumers) and publication of the national Acute Stroke Clinical Care Standard in 2015 [ 17 ], and subsequent review [ 18 ]. The indicator set for the Acute Stroke Standard then informed the expansion of the minimum dataset for AuSCR so that hospitals could routinely track their performance. The national Audit program enabled hospitals not involved in the AuSCR to assess their performance every two years against the Acute Stroke Standard. Complementing these efforts, the Stroke Foundation, working with the sector, developed the Acute and Rehabilitation Stroke Services Frameworks to outline the principles, essential elements, models of care and staffing recommendations for stroke services ( https://informme.org.au/guidelines/national-stroke-services-frameworks ). The Frameworks are intended to guide where stroke services should be developed, and monitor their uptake with the organizational survey component of the Audit program.

Evidence from implementation and healthcare improvement (LHS quadrant 4, Fig. 1 )

Research to better utilize and augment data from registries through linkage [ 37 , 38 , 39 , 40 ] and to ensure presentation of hospital or service level data are understood by clinicians has ensured advancement in the field for the Australian Stroke LHS [ 41 ]. Importantly, greater insights into whole patient journeys, before and after a stroke, can now enable exploration of value-based care. The LHS and stroke data platform have enabled focused and time-limited projects to create a better understanding of the quality of care in acute or rehabilitation settings [ 22 , 42 , 43 ]. Within stroke, all the elements of an LHS culminate into the ready availability of benchmarked performance data and support for implementation of strategies to address gaps in care.

Implementation research to grow the evidence base for effective improvement interventions has also been a key pillar in the Australian context. These include multi-component implementation interventions to achieve behaviour change for particular aspects of stroke care, [ 22 , 23 , 44 , 45 ] and real-world approaches to augmenting access to hyperacute interventions in stroke through the use of technology and telehealth [ 46 , 47 , 48 , 49 ]. The evidence from these studies feeds into the living guidelines program and the data collection systems, such as the Audit program or AuSCR, which are then amended to ensure data aligns to recommended care. For example, the use of ‘hyperacute aspirin within the first 48 h of ischemic stroke’ was modified to be ‘hyperacute antiplatelet…’ to incorporate new evidence that other medications or combinations are appropriate to use. Additionally, new datasets have been developed to align with evidence such as the Fever, Sugar, and Swallow variables [ 42 ]. Evidence on improvements in access to best practice care from the acute Audit program [ 50 ] and AuSCR is emerging [ 36 ]. For example, between 2007 and 2017, the odds of receiving intravenous thrombolysis after ischemic stroke increased by 16% 9OR 1.06 95% CI 1.13–1.18) and being managed in a stroke unit by 18% (OR 1.18 95% CI 1.17–1.20). Over this period, the median length of hospital stay for all patients decreased from 6.3 days in 2007 to 5.0 days in 2017 [ 51 ]. When considering the number of additional patients who would receive treatment in 2017 in comparison to 2007 it was estimated that without this additional treatment, over 17,000 healthy years of life would be lost in 2017 (17,786 disability-adjusted life years) [ 51 ]. There is evidence on the cost-effectiveness of different system-focussed strategies to augment treatment access for acute ischemic stroke (e.g. Victorian Stroke Telemedicine program [ 52 ] and Melbourne Mobile Stroke Unit ambulance [ 53 ]). Reciprocally, evidence from the national Rehabilitation Audit, where the LHS approach has been less complete or embedded, has shown fewer areas of healthcare improvement over time [ 51 , 54 ].

Within the field of stroke in Australia, there is indirect evidence that the collective efforts that align to establishing the components of a LHS have had an impact. Overall, the age-standardised rate of stroke events has reduced by 27% between 2001 and 2020, from 169 to 124 events per 100,000 population. Substantial declines in mortality rates have been reported since 1980. Commensurate with national clinical guidelines being updated in 2007 and the first National Stroke Audit being undertaken in 2007, the mortality rates for men (37.4 deaths per 100,000) and women (36.1 deaths per 100,0000 has declined to 23.8 and 23.9 per 100,000, respectively in 2021 [ 55 ].

Underpinning the LHS with the integration of the four quadrants of evidence from stakeholders, research and guidelines, practice and implementation, and core LHS principles have been addressed. Leadership and governance have been important, and programs have been established to augment workforce training and capacity building in best practice professional development. Medical practitioners are able to undertake courses and mentoring through the Australasian Stroke Academy ( http://www.strokeacademy.com.au/ ) while nurses (and other health professionals) can access teaching modules in stroke care from the Acute Stroke Nurses Education Network ( https://asnen.org/ ). The Association of Neurovascular Clinicians offers distance-accessible education and certification to develop stroke expertise for interdisciplinary professionals, including advanced stroke co-ordinator certification ( www.anvc.org ). Consumer initiative interventions are also used in the design of the AuSCR Public Summary Annual reports (available at https://auscr.com.au/about/annual-reports/ ) and consumer-related resources related to the Living Guidelines ( https://enableme.org.au/resources ).

The important success factors and lessons from stroke as a national exemplar LHS in Australia include leadership, culture, workforce and resources integrated with (1) established and broad partnerships across the academic-clinical sector divide and stakeholder engagement; (2) the living guidelines program; (3) national data infrastructure, including a national data dictionary that provides the common data framework to support standardized data capture; (4) various implementation strategies including benchmarking and feedback as well as engagement strategies targeting different levels of the health system; and (5) implementation and improvement research to advance stroke systems of care and reduce unwarranted variation in practice (Fig. 1 ). Priority opportunities now include the advancement of interoperability with electronic medical records as an area all clinical quality registry’s programs needs to be addressed, as well as providing more dynamic and interactive data dashboards tailored to the need of clinicians and health service executives.

There is a clear mandate to optimise healthcare improvement with big data offering major opportunities for change. However, we have lacked the approaches to capture evidence from the community and stakeholders, to integrate evidence from research, to capture and leverage data or evidence from practice and to generate and build on evidence from implementation using iterative system-level improvement. The LHS provides this opportunity and is shown to deliver impact. Here, we have outlined the process applied to generate an evidence-based LHS and provide a leading exemplar in stroke care. This highlights the value of moving from single-focus isolated approaches/initiatives to healthcare improvement and the benefit of integration to deliver demonstrable outcomes for our funders and key stakeholders — our community. This work provides insight into strategies that can both apply evidence-based processes to healthcare improvement as well as implementing evidence-based practices into care, moving beyond research as an endpoint, to research as an enabler, underpinning delivery of better healthcare.

Availability of data and materials

Not applicable

Abbreviations

Australian Stroke Clinical Registry

Confidence interval

Learning Health System

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Acknowledgements

The following authors hold National Health and Medical Research Council Research Fellowships: HT (#2009326), DAC (#1154273), SM (#1196352), MFK Future Leader Research Fellowship (National Heart Foundation #105737). The Funders of this work did not have any direct role in the design of the study, its execution, analyses, interpretation of the data, or decision to submit results for publication.

Author information

Helena Teede and Dominique A. Cadilhac contributed equally.

Authors and Affiliations

Monash Centre for Health Research and Implementation, 43-51 Kanooka Grove, Clayton, VIC, Australia

Helena Teede, Emily Callander & Joanne Enticott

Monash Partners Academic Health Science Centre, 43-51 Kanooka Grove, Clayton, VIC, Australia

Helena Teede & Alison Johnson

Stroke and Ageing Research, Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Level 2 Monash University Research, Victorian Heart Hospital, 631 Blackburn Rd, Clayton, VIC, Australia

Dominique A. Cadilhac, Tara Purvis & Monique F. Kilkenny

Stroke Theme, The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Heidelberg, VIC, Australia

Dominique A. Cadilhac, Monique F. Kilkenny & Bruce C.V. Campbell

Department of Neurology, Melbourne Brain Centre, Royal Melbourne Hospital, Parkville, VIC, Australia

Bruce C.V. Campbell

Department of Medicine, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Victoria, Australia

School of Health Sciences, Heart and Stroke Program, University of Newcastle, Hunter Medical Research Institute, University Drive, Callaghan, NSW, Australia

Coralie English

School of Medicine and Dentistry, Griffith University, Birtinya, QLD, Australia

Rohan S. Grimley

Clinical Excellence Division, Queensland Health, Brisbane, Australia

John Hunter Hospital, Hunter New England Local Health District and University of Newcastle, Sydney, NSW, Australia

Christopher Levi

School of Nursing, Midwifery and Paramedicine, Australian Catholic University, Sydney, NSW, Australia

Sandy Middleton

Nursing Research Institute, St Vincent’s Health Network Sydney and and Australian Catholic University, Sydney, NSW, Australia

Stroke Foundation, Level 7, 461 Bourke St, Melbourne, VIC, Australia

Kelvin Hill

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Contributions

HT: conception, design and initial draft, developed the theoretical formalism for learning health system framework, approved the submitted version. DAC: conception, design and initial draft, provided essential literature and case study examples, approved the submitted version. TP: revised the manuscript critically for important intellectual content, approved the submitted version. MFK: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. BC: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. CE: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. AJ: conception, design and initial draft, developed the theoretical formalism for learning health system framework, approved the submitted version. EC: revised the manuscript critically for important intellectual content, approved the submitted version. RSG: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. CL: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. SM: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. KH: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. JE: conception, design and initial draft, developed the theoretical formalism for learning health system framework, approved the submitted version. All authors read and approved the final manuscript.

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Teede, H., Cadilhac, D.A., Purvis, T. et al. Learning together for better health using an evidence-based Learning Health System framework: a case study in stroke. BMC Med 22 , 198 (2024). https://doi.org/10.1186/s12916-024-03416-w

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v.90(4); 2019 Aug

Hypothesis-generating and confirmatory studies, Bonferroni correction, and pre-specification of trial endpoints

A p-value presents the outcome of a statistically tested null hypothesis. It indicates how incompatible observed data are with a statistical model defined by a null hypothesis. This hypothesis can, for example, be that 2 parameters have identical values, or that they differ by a specified amount. A low p-value shows that it is unlikely (a high p-value that it is not unlikely) that the observed data are consistent with the null hypothesis. Many null hypotheses are tested in order to generate study hypotheses for further research, others to confirm an already established study hypothesis. The difference between generating and confirming a hypothesis is crucial for the interpretation of the results. Presenting an outcome from a hypothesis-generating study as if it had been produced in a confirmatory study is misleading and represents methodological ignorance or scientific misconduct.

Hypothesis-generating studies differ methodologically from confirmatory studies. A generated hypothesis must be confirmed in a new study. An experiment is usually required for confirmation as an observational study cannot provide unequivocal results. For example, selection and confounding bias can be prevented by randomization and blinding in a clinical trial, but not in an observational study. Confirmatory studies, but not hypothesis-generating studies, also require control of the inflation in the false-positive error risk that is caused by testing multiple null hypotheses. The phenomenon is known as a multiplicity or mass-significance effect. A method for correcting the significance level for the multiplicity effect has been devised by the Italian mathematician Carlo Emilio Bonferroni. The correction (Bender and Lange 2001 ) is often misused in hypothesis-generating studies, often ignored when designing confirmatory studies (which results in underpowered studies), and often inadequately used in laboratory studies, for example when an investigator corrects the significance level for comparing 3 experimental groups by lowering it to 0.05/3 = 0. 017 and believes that this solves the problem of testing 50 null hypotheses, which would have required a corrected significance level of 0.05/50 = 0.001.

In a confirmatory study, it is mandatory to show that the tested hypothesis has been pre-specified. A study protocol or statistical analysis plan should therefore be enclosed with the study report when submitted to a scientific journal for publication. Since 2005 the ICMJE (International Committee of Medical Journal Editors) and the WHO also require registration of clinical trials and their endpoints in a publicly accessible register before enrollment of the first participant. Changing endpoints in a randomized trial after its initiation can in some cases be acceptable, but this is never a trivial problem (Evans 2007 ) and must always be described to the reader. Many authors do not understand the importance of pre-specification and desist from registering their trial, use vague or ambiguous endpoint definitions, redefine the primary endpoint during the analysis, switch primary and secondary outcomes, or present completely new endpoints without mentioning this to the reader. Such publications are simply not credible, but are nevertheless surprisingly common (Ramagopalan et al. 2014 ) even in high impact factor journals (Goldacre et al. 2019 ). A serious editorial evaluation of manuscripts presenting confirmatory results should always include a verification of the endpoint’s pre-specification.

Hypothesis-generating studies are much more common than confirmatory, because the latter are logistically more complex, more laborious, more time-consuming, more expensive, and require more methodological expertise. However, the result of a hypothesis-generating study is just a hypothesis. A hypothesis cannot be generated and confirmed in the same study, and it cannot be confirmed with a new hypothesis-generating study. Confirmatory studies are essential for scientific progress.

Bender R, Lange S. Adjusting for multiple testing: when and how? J Clin Epidemiol 2001; 54 : 343–9. [ PubMed ] [ Google Scholar ]
Evans S. When and how can endpoints be changed after initiation of a randomized clinical trial? PLoS Clin Trials 2007; 2 : e18. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Goldacre B, Drysdale H, Milosevic I, Slade E, Hartley P, Marston C, Powell-Smith A, Heneghan C, Mahtani K R. COMPare: a prospective cohort study correcting and monitoring 58 misreported trials in real time . Trials 2019; 20 : 118. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Ramagopalan S, Skingsley A P, Handunnetthi L, Klingel M, Magnus D, Pakpoor J, Goldacre B. Prevalence of primary outcome changes in clinical trials registered on ClinicalTrials.gov: a cross-sectional study . F1000Research 2014, 3 : 77. [ PMC free article ] [ PubMed ] [ Google Scholar ]

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However, hypothesis-generating case studies contribute to the process of theory construction rather than to theory itself (Ravenswood, 2011). For instance, it is particularly useful in explaining cases that do not fit into existing brief notes on Case Study (Collier, 1999, as cited in Levy, 2008 , p. 5).
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Formulating Hypotheses for Different Study Designs. Generating a testable working hypothesis is the first step towards conducting original research. Such research may prove or disprove the proposed hypothesis. Case reports, case series, online surveys and other observational studies, clinical trials, and narrative reviews help to generate ...
Case Studies: Types, Designs, and Logics of Inference
the theoretical purposes of case studies and the case selection strategies or research de. signs used to advance those objectives. I construct a typology of case studies based on their purposes: idiographic (inductive and theory-guided), hypothesis-generating, hypothesis-testing, and plausibility probe case studies.
Case Study Methodology of Qualitative Research: Key Attributes and
A case study is one of the most commonly used methodologies of social research. This article attempts to look into the various dimensions of a case study research strategy, the different epistemological strands which determine the particular case study type and approach adopted in the field, discusses the factors which can enhance the effectiveness of a case study research, and the debate ...
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Case studies can be distinguished according to their purpose. Levy suggests a typology with four types: idiographic, hypothesis generating, hypothesis testing, and plausibility probes. Idiographic case studies aim to describe, explain, or interpret a singular historical epi - sode with no intention of generalizing beyond the case.
Case Study Method: A Step-by-Step Guide for Business Researchers
Although case studies have been discussed extensively in the literature, little has been written about the specific steps one may use to conduct case study research effectively (Gagnon, 2010; Hancock & Algozzine, 2016).Baskarada (2014) also emphasized the need to have a succinct guideline that can be practically followed as it is actually tough to execute a case study well in practice.
Case Studies: Types, Designs, and Logics of Inference
I distinguish between the theoretical purposes of case studies and the case selection strategies or research designs used to advance those objectives. I construct a typology of case studies based on their purposes: idiographic (inductive and theory-guided), hypothesis-generating, hypothesis-testing, and plausibility probe case studies.
Local Politics and Decision-Making: A Hypothesis-Generating Case Study
theoretical types is the hypothesis-generating case study. Of studies of this type, Lijphart says: 'They start out with a more or less vague notion of possible hypotheses, and attempt to formulate definite hypotheses to be tested subsequently among a large number of cases.'2 The study upon which this paper is based satisfies entirely the first
Local Politics and Decision-Making: a Hypothesis-Generating Case Study
In a recent article on the comparative method in the study of politics, 1 Arend Lijphart discusses the place of the case study and suggests that there are six types of case study, four of which may be characterized as theoretical, two as atheoretical. The first of the theoretical types is the hypothesis-generating case study.
Hypothesis Generation and Interpretation
Practical case studies are used to demonstrate how hypothesis-generation and -interpretation technologies work. These are based on "social infrastructure" applications like in-bound tourism, disaster management, lunar and planetary exploration, and treatment of infectious diseases.
Data-Driven Hypothesis Generation in Clinical Research: What We Learned
Hypothesis generation is an early and critical step in any hypothesis-driven clinical research project. Because it is not yet a well-understood cognitive process, the need to improve the process goes unrecognized. Without an impactful hypothesis, the significance of any research project can be questionable, regardless of the rigor or diligence applied in other steps of the study, e.g., study ...
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1. An Overview. Broadly, preclinical research can be classified into two distinct categories depending on the aim and purpose of the experiment, namely, "hypothesis generating" (exploratory) and "hypothesis testing" (confirmatory) research (Fig. 1).Hypothesis generating studies are often scientifically-informed, curiosity and intuition-driven explorations which may generate testable ...
Hypothesis-generating research and predictive medicine
The hypothesis-generating mode of research has been primarily practiced in basic science but has recently been extended to clinical-translational work as well. Just as in basic science, this approach to research can facilitate insights into human health and disease mechanisms and provide the crucially needed data set of the full spectrum of ...
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Case Study: Hypothesis Generation with NYC Taxi Trip Duration Prediction . What is Hypothesis Generation? Hypothesis generation is an educated "guess" of various factors that are impacting the business problem that needs to be solved using machine learning. In framing a hypothesis, the data scientist must not know the outcome of the ...
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A case study is an in-depth, ... Hypothesis-generating (or heuristic) case studies aim to inductively identify new variables, hypotheses, causal mechanisms, and causal paths. Theory testing case studies aim to assess the validity and scope conditions of existing theories.
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The hypothesis-generating case study. A case study begun for any purpose can become a hypothesis-generating case study as well. One of the most valuable contributions of any method would be the generation of a new hypothesis that turned out to be valid or fruitful of fresh lines of investigation.
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FUTURE DIRECTIONS. In assembling this long list of techniques for creative hypothesis generating I have tried to be inclusive, allowing some of the heuristics to overlap, e.g. several heuristics use variants of interaction effects or of deviant-case analysis as a creative device.
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The problem with transposing the concepts of "hypothesis generation" and "hypothesis testing" is that hypothesis generation is relatively easy. It's basically coming up with testable ...
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5. Phrase your hypothesis in three ways. To identify the variables, you can write a simple prediction in if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable. If a first-year student starts attending more lectures, then their exam scores will improve.
PDF Case Studies: Types, Designs, and Logics of Inference
and emphasize that one of the main tasks of case study analysis is to generate as many testable implications of one's hypotheses as possible in a given case (King et al., 1994).7 ... hypothesis generating case studies; hypothesis testing cases, which combine Lijphart's theory-conﬁrming and theory-informing categories; ...
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A free case study generator is a tool or system designed to automatically create detailed case studies. It typically uses predefined templates and may incorporate artificial intelligence (AI) to generate comprehensive analyses of specific situations, events, or individuals. This tool streamlines the process of crafting informative case studies ...
Results from a hypothesis generating case-control study ...
As part of a larger research program, we conducted a hypothesis-generating case-control study of selected herpes virus antibodies among individuals discharged from the US military with schizophrenia and pre- and postdiagnosis sera. Methods: Cases (n = 180) were servicemembers hospitalized and discharged from military service with schizophrenia ...
Shigella Surveillance
The Shigella Hypothesis Generating Questionnaire, modeled after the National Hypothesis Generating Questionnaire, is a tool created for public health professionals to interview people who are experiencing diarrheal illness and are suspected or confirmed to have Shigella infection. The questionnaire investigates exposures related to shigellosis ...
Hypothesis generation
Case study, Module 2: Interpreting hypothesis generation results; Hypothesis generation through case exposures in multiple restaurant clusters example: Barton Behravesh C, et al. 2012. Multistate outbreak of Salmonella serotype Typhimurium infections associated with consumption of restaurant tomatoes, USA, 2006: hypothesis generation through ...
The case study approach
A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table.
Learning together for better health using an evidence-based Learning
Internationally, health systems are facing a crisis, driven by an ageing population, increasing complexity, multi-morbidity, rapidly advancing health technology and rising costs that threaten sustainability and mandate transformation and improvement [1, 2].Although research has generated solutions to healthcare challenges, and the advent of big data and digital health holds great promise ...
JRFM
This paper proposes a method for conducting quantitative inductive research on survey data when the variable of interest follows an ordinal distribution. A methodology based on novel and traditional penalising models is described. The main aim of this study is to pedagogically present the method utilising the new penalising methods in a new application. A case was employed to outline the ...
Multi-omics bioactivity profile-based chemical grouping and read-across
First, we assessed the structural similarities of the dyes, generating a grouping hypothesis with DY3 and two Sudan dyes within one group. Daphnia magna were exposed acutely to equi-effective doses of all seven dyes (each at 3 doses and 3 time points), transcriptomics and metabolomics data were generated from 760 samples.
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The purpose of this study is to highlight the relationship between historical monuments, viewed as factors generating tourist motivation and tourism. This was made possible by conducting a spatial analysis (at the level of territorial administrative units and localities) of the defining criteria for historical monuments and tourism in Bihor ...
Hypothesis-generating and confirmatory studies, Bonferroni correction
Hypothesis-generating studies differ methodologically from confirmatory studies. A generated hypothesis must be confirmed in a new study. An experiment is usually required for confirmation as an observational study cannot provide unequivocal results. For example, selection and confounding bias can be prevented by randomization and blinding in a ...