Elizabeth Oddone Paolucci, Natasha Gallant, Elena Lopatina, Kim McGrail, Tom Noseworthy, Kiran J. Pohar Manhas, Chelsea L. Russill, Simron Sidhu, Nitya Suryaprakash, Venezya H. Thorsteinson, Tracy Wasylak, Deborah A. Marshall
Introduction
Healthcare systems face persistent challenges, including workforce pressures and increasing patient complexity, and leaders are increasingly seeking approaches that support rapid learning and improvement. Learning Health Systems (LHSs) support continuous knowledge generation and application and depend on professionals with both traditional research competencies and skills in knowledge mobilization, implementation science, and collaborative leadership. The Health Systems Impact Fellowship (HSIF) National Cohort Training Program (NCTP) prepares doctoral students, postdoctoral fellows, and early-career researchers (ECRs) in Health Services and Policy Research (HSPR) through embedded research training.
Methods
We evaluated NCTP outcomes using the Kirkpatrick Evaluation Framework (KEF). Using a convergent mixed-methods survey design, we collected quantitative ratings and qualitative narrative feedback via online surveys mapped to KEF levels (reaction, learning, behavior, and results/impacts). Descriptive statistics summarized ratings across survey rounds, and directed content analysis (guided by the KEF) was used to analyze open-ended responses.
Results
Among 195 invited participants, response rates varied by training session. Overall satisfaction (agree/strongly agree) was 60.6% and varied across themes (52.6%–78.6%), reflecting diverse training needs within a multidisciplinary cohort. Respondents reported knowledge gains (over 70% for several items) and improved confidence (62%). Behavior- and impact-level evidence was less frequent but included early examples of skill application and perceived shifts in organizational practices. Qualitative feedback helped clarify what drove variation across themes. Interim findings were used to refine both training delivery and the evaluation approach.
Conclusion
Fellowship-based training programs such as the NCTP can support knowledge-to-action capabilities relevant to LHSs. Future evaluations should incorporate longer-term follow-up and additional data sources to better assess behavior change and organizational impact.
{"title":"Evaluating a National Training Program for Health Services and Policy Research: Building Capacity for Learning Health Systems","authors":"Elizabeth Oddone Paolucci, Natasha Gallant, Elena Lopatina, Kim McGrail, Tom Noseworthy, Kiran J. Pohar Manhas, Chelsea L. Russill, Simron Sidhu, Nitya Suryaprakash, Venezya H. Thorsteinson, Tracy Wasylak, Deborah A. Marshall","doi":"10.1002/lrh2.70074","DOIUrl":"https://doi.org/10.1002/lrh2.70074","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Healthcare systems face persistent challenges, including workforce pressures and increasing patient complexity, and leaders are increasingly seeking approaches that support rapid learning and improvement. Learning Health Systems (LHSs) support continuous knowledge generation and application and depend on professionals with both traditional research competencies and skills in knowledge mobilization, implementation science, and collaborative leadership. The Health Systems Impact Fellowship (HSIF) National Cohort Training Program (NCTP) prepares doctoral students, postdoctoral fellows, and early-career researchers (ECRs) in Health Services and Policy Research (HSPR) through embedded research training.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We evaluated NCTP outcomes using the Kirkpatrick Evaluation Framework (KEF). Using a convergent mixed-methods survey design, we collected quantitative ratings and qualitative narrative feedback via online surveys mapped to KEF levels (reaction, learning, behavior, and results/impacts). Descriptive statistics summarized ratings across survey rounds, and directed content analysis (guided by the KEF) was used to analyze open-ended responses.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Among 195 invited participants, response rates varied by training session. Overall satisfaction (agree/strongly agree) was 60.6% and varied across themes (52.6%–78.6%), reflecting diverse training needs within a multidisciplinary cohort. Respondents reported knowledge gains (over 70% for several items) and improved confidence (62%). Behavior- and impact-level evidence was less frequent but included early examples of skill application and perceived shifts in organizational practices. Qualitative feedback helped clarify what drove variation across themes. Interim findings were used to refine both training delivery and the evaluation approach.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Fellowship-based training programs such as the NCTP can support knowledge-to-action capabilities relevant to LHSs. Future evaluations should incorporate longer-term follow-up and additional data sources to better assess behavior change and organizational impact.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147585142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Melinda M. Davis, Jennifer Coury, Zsolt Nagykaldi, Kisha Coa, Brittany Badicke, Ana R. Quiñones, LeAnn C. Michaels, Alex H. Krist, Arlene S. Bierman, David A. Dorr
Introduction
Individuals at risk for or living with multiple chronic conditions (MCC) require coordinated care that is often navigated across primary, specialty care, and community environments. Person-centered care planning (PCCP) is an effective way to manage care for people with MCC needs but is not yet routine practice.
Methods
The PCCP for Persons with MCC Summit was the culminating event of 14-months of three working groups and a series of environmental scans of PCCP approaches and models. The Summit convened participants from multiple sectors to address these questions of interest: (1) strategies to address and mitigate barriers to PCCP as an integral component of routine practice; (2) the development of actionable Learning Health System-related steps for PCCP implementation; and (3) research priorities to advance PCCP for persons with MCC. Audio recordings, meeting transcripts and detailed notes were consolidated and reviewed to identify and synthesize key findings.
Results
The Summit was held March 2025 and attended by 84 in-person and 31 online participants. Participants emphasized three key outcomes: improving the patient experience, strengthening the clinician-patient relationship, and reducing care team burnout. Six interconnected themes relate to what needs to change to achieve PCCP: demand and awareness; quality measurement; team-based care; technology; payment reform; and training. Strategies for change focused on short and long-term goals, cross-sector coordination, context, and community-level interventions. Participants identified actionable implementation steps and six categories of research opportunities.
Conclusions
Widespread adoption of PCCP requires short- and long-term efforts that raise awareness and investment, engage multi-sector partners, and attend to context. Scaling PCCP as a widely used model of care for persons with or at risk of MCC will benefit from action from each partner in care. This transformational change could build upon evidence and Learning Health System strategies to advance research and inform organizational, payment and policy changes.
{"title":"Person-Centered Care Planning for Persons With Multiple Chronic Conditions: Summary of an Agency for Health Care Research and Quality Sponsored Summit","authors":"Melinda M. Davis, Jennifer Coury, Zsolt Nagykaldi, Kisha Coa, Brittany Badicke, Ana R. Quiñones, LeAnn C. Michaels, Alex H. Krist, Arlene S. Bierman, David A. Dorr","doi":"10.1002/lrh2.70073","DOIUrl":"https://doi.org/10.1002/lrh2.70073","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Individuals at risk for or living with multiple chronic conditions (MCC) require coordinated care that is often navigated across primary, specialty care, and community environments. Person-centered care planning (PCCP) is an effective way to manage care for people with MCC needs but is not yet routine practice.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The PCCP for Persons with MCC Summit was the culminating event of 14-months of three working groups and a series of environmental scans of PCCP approaches and models. The Summit convened participants from multiple sectors to address these questions of interest: (1) strategies to address and mitigate barriers to PCCP as an integral component of routine practice; (2) the development of actionable Learning Health System-related steps for PCCP implementation; and (3) research priorities to advance PCCP for persons with MCC. Audio recordings, meeting transcripts and detailed notes were consolidated and reviewed to identify and synthesize key findings.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The Summit was held March 2025 and attended by 84 in-person and 31 online participants. Participants emphasized three key outcomes: improving the patient experience, strengthening the clinician-patient relationship, and reducing care team burnout. Six interconnected themes relate to what needs to change to achieve PCCP: demand and awareness; quality measurement; team-based care; technology; payment reform; and training. Strategies for change focused on short and long-term goals, cross-sector coordination, context, and community-level interventions. Participants identified actionable implementation steps and six categories of research opportunities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Widespread adoption of PCCP requires short- and long-term efforts that raise awareness and investment, engage multi-sector partners, and attend to context. Scaling PCCP as a widely used model of care for persons with or at risk of MCC will benefit from action from each partner in care. This transformational change could build upon evidence and Learning Health System strategies to advance research and inform organizational, payment and policy changes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147565444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vivetha Thambinathan, Katie Lynch, Maricruz Moya, Maria G. Flores, Rama Mwenesi Musalia, Joshua Rubin, Jenna Lepczyk, Francesca A. Williamson
Introduction
The science of learning health systems (LHSs) has expanded to include equity aims. However, efforts toward this end are nascent. The purpose of this study was to critically examine how equity has been discussed in LHS literature.
Methods
We conducted a critical scoping review informed by the bounded justice concept. Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, Pubmed, Embase, Scopus, and CINAHL Complete (Ebsco) databases were searched to identify relevant literature from inception to June 2024. We used qualitative descriptive analysis for detailed extraction and to identify patterns across equity conceptions in the literature.
Results
The search strategy generated 420 articles. Forty articles met the selection criteria. Our analysis generated four interrelated themes: (1) equity as central, (2) equity as reducing risk, (3) equity as process, and (4) equity defined as population of interest. Most articles fell into the equity as central theme. Notably, 35 (87.5%) articles were published between 2020 and 2024. We also identified five emerging equity-centered LHS approaches.
Conclusions
LHS equity science is emerging, though it still privileges narrow, Westernized approaches. It is imperative that future work more deeply engages health justice traditions to inform LHS theory, research, and practice.
{"title":"Learning Health Systems, Equity, and Bounded Justice: A Critical Scoping Review","authors":"Vivetha Thambinathan, Katie Lynch, Maricruz Moya, Maria G. Flores, Rama Mwenesi Musalia, Joshua Rubin, Jenna Lepczyk, Francesca A. Williamson","doi":"10.1002/lrh2.70072","DOIUrl":"10.1002/lrh2.70072","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>The science of learning health systems (LHSs) has expanded to include equity aims. However, efforts toward this end are nascent. The purpose of this study was to critically examine how equity has been discussed in LHS literature.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We conducted a critical scoping review informed by the bounded justice concept. Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, Pubmed, Embase, Scopus, and CINAHL Complete (Ebsco) databases were searched to identify relevant literature from inception to June 2024. We used qualitative descriptive analysis for detailed extraction and to identify patterns across equity conceptions in the literature.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The search strategy generated 420 articles. Forty articles met the selection criteria. Our analysis generated four interrelated themes: (1) <i>equity as central</i>, (2) <i>equity as reducing risk</i>, (3) <i>equity as process</i>, and (4) <i>equity defined as population of interest</i>. Most articles fell into the <i>equity as central</i> theme. Notably, 35 (87.5%) articles were published between 2020 and 2024. We also identified five emerging equity-centered LHS approaches.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>LHS equity science is emerging, though it still privileges narrow, Westernized approaches. It is imperative that future work more deeply engages health justice traditions to inform LHS theory, research, and practice.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12976803/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147445551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p>The Center for Learning Health System Sciences (CLHSS) at the University of Minnesota (UMN) hosted its third annual <i>Advances in Learning Health System Sciences</i> (AiLHSS) Conference on September 22-23 at McNamara Alumni Center in Minneapolis, MN. This year's conference theme, “Building Bridges in LHS”, highlights AiLHSS’ role in both advancing the field of learning health systems while also providing networking and collaboration opportunities.</p><p>Reflecting a growing interest in LHS work, this year's conference had a 58% increase in registration with 346 registrants, including public health professionals, researchers, clinicians, hospital administrators, and industry partners. Day 1 included three keynote speakers, a poster session with over 60 presenters, plenary sessions featuring local, regional, and national research, and breakout sessions. The Day 1 agenda purposely included time for networking, including extended poster session times and an evening reception. Day 2 featured half-day supplemental workshops.</p><p>Day 1 began with opening remarks from CLHSS Director Genevieve Melton-Meaux, MD, PhD, and Shashank Priya, PhD, Vice President for Research & Innovation at UMN. The morning included a keynote address by Amy Kilbourne, PhD, MPH (US Department of Veterans Affairs and the University of Michigan), who highlighted how implementation science (IS) can align research translation with public health goals. Following was a plenary session showcasing regional and national initiatives in advancing IS in health care. Topics included reducing racial disparities in cardiac rehabilitation, avoiding unnecessary emergency department admissions for seniors, and improving guideline adherence for children with cerebral palsy. An extended poster session closed the morning agenda, offering additional networking opportunities.</p><p>The midday agenda shifted to pragmatic trials. Over lunch, Areef Ishani, MD, MS (Minneapolis VA and the UMN Department of Medicine) delivered a keynote focused on using pragmatic trials to produce rigorous evidence. A plenary session followed featuring LHS partnerships with public health, including presentations on statewide public health tools, case studies of partnerships between public health and health care, and example comparisons of Electronic Health Record (EHR) data and local public health data to better understand community health trends. Following this were two concurrent breakout sessions: (1) <i>Digital Transformation in Healthcare: Where Strategy, AI, and Investment Meet</i>, which explored how emerging technologies and venture investment are connecting to drive innovation across health care systems, and (2) <i>Data Action: Stories from the MN EHR Consortium</i>, which highlighted how Minnesota's EHR data is being utilized to provide insights into community health.</p><p>The end of the first day's agenda centered on the role data plays in improving healthcare practice. Six presenters participated in fi
{"title":"2025 Advances in Learning Health System Sciences (AiLHSS) Conference: Abstracts","authors":"","doi":"10.1002/lrh2.70066","DOIUrl":"https://doi.org/10.1002/lrh2.70066","url":null,"abstract":"<p>The Center for Learning Health System Sciences (CLHSS) at the University of Minnesota (UMN) hosted its third annual <i>Advances in Learning Health System Sciences</i> (AiLHSS) Conference on September 22-23 at McNamara Alumni Center in Minneapolis, MN. This year's conference theme, “Building Bridges in LHS”, highlights AiLHSS’ role in both advancing the field of learning health systems while also providing networking and collaboration opportunities.</p><p>Reflecting a growing interest in LHS work, this year's conference had a 58% increase in registration with 346 registrants, including public health professionals, researchers, clinicians, hospital administrators, and industry partners. Day 1 included three keynote speakers, a poster session with over 60 presenters, plenary sessions featuring local, regional, and national research, and breakout sessions. The Day 1 agenda purposely included time for networking, including extended poster session times and an evening reception. Day 2 featured half-day supplemental workshops.</p><p>Day 1 began with opening remarks from CLHSS Director Genevieve Melton-Meaux, MD, PhD, and Shashank Priya, PhD, Vice President for Research & Innovation at UMN. The morning included a keynote address by Amy Kilbourne, PhD, MPH (US Department of Veterans Affairs and the University of Michigan), who highlighted how implementation science (IS) can align research translation with public health goals. Following was a plenary session showcasing regional and national initiatives in advancing IS in health care. Topics included reducing racial disparities in cardiac rehabilitation, avoiding unnecessary emergency department admissions for seniors, and improving guideline adherence for children with cerebral palsy. An extended poster session closed the morning agenda, offering additional networking opportunities.</p><p>The midday agenda shifted to pragmatic trials. Over lunch, Areef Ishani, MD, MS (Minneapolis VA and the UMN Department of Medicine) delivered a keynote focused on using pragmatic trials to produce rigorous evidence. A plenary session followed featuring LHS partnerships with public health, including presentations on statewide public health tools, case studies of partnerships between public health and health care, and example comparisons of Electronic Health Record (EHR) data and local public health data to better understand community health trends. Following this were two concurrent breakout sessions: (1) <i>Digital Transformation in Healthcare: Where Strategy, AI, and Investment Meet</i>, which explored how emerging technologies and venture investment are connecting to drive innovation across health care systems, and (2) <i>Data Action: Stories from the MN EHR Consortium</i>, which highlighted how Minnesota's EHR data is being utilized to provide insights into community health.</p><p>The end of the first day's agenda centered on the role data plays in improving healthcare practice. Six presenters participated in fi","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Matthew F. Hudson, Virginia M. S. van Staden, Alicia M. Oostdyk, Julie C. Martin
Previous work affirms access and health care challenges persist for multiple racial groups. Race and ethnicity are socially and politically constructed terms conceived to describe and categorize people hierarchically (race) and describe people from a similar national or regional background also sharing common national, cultural, historical, and social experiences (ethnicity). Definitions of race and ethnicity may vary across historical, political, and geographic contexts. This variation challenges health care organizations to reliably measure health care outcomes longitudinally within and between racial and ethnic groups. Further, suboptimal concordance between race and ethnicity self-report vs. electronic health record suggests opportunities exist to improve race and ethnicity data solicitation and capture. Learning Health Systems (LHSs) may be particularly poised to address these challenges, as LHSs commit to equitable, transparent, and accountable engagement of individuals and families in care innovation. This discussion provides a rationale, model, and practical strategy to engage individuals and families in race data defining—an essential antecedent to health equity assessment and intervention development. The discussion encourages testing to ensure the proposed theory and steps beget comprehensive and precise race definitions. Enhanced precision in defining race may subsequently inform equity evaluation and interventions in LHSs.
{"title":"Knowledge Architecture for Race and Ethnic Group Defining in Learning Health Systems","authors":"Matthew F. Hudson, Virginia M. S. van Staden, Alicia M. Oostdyk, Julie C. Martin","doi":"10.1002/lrh2.70071","DOIUrl":"10.1002/lrh2.70071","url":null,"abstract":"<p>Previous work affirms access and health care challenges persist for multiple racial groups. Race and ethnicity are socially and politically constructed terms conceived to describe and categorize people hierarchically (race) and describe people from a similar national or regional background also sharing common national, cultural, historical, and social experiences (ethnicity). Definitions of race and ethnicity may vary across historical, political, and geographic contexts. This variation challenges health care organizations to reliably measure health care outcomes longitudinally within and between racial and ethnic groups. Further, suboptimal concordance between race and ethnicity self-report vs. electronic health record suggests opportunities exist to improve race and ethnicity data solicitation and capture. Learning Health Systems (LHSs) may be particularly poised to address these challenges, as LHSs commit to equitable, transparent, and accountable engagement of individuals and families in care innovation. This discussion provides a rationale, model, and practical strategy to engage individuals and families in race data defining—an essential antecedent to health equity assessment and intervention development. The discussion encourages testing to ensure the proposed theory and steps beget comprehensive and precise race definitions. Enhanced precision in defining race may subsequently inform equity evaluation and interventions in LHSs.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12945470/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147327661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Steven G. Johnson, Alanna M. Chamberlain, Paul Drawz, R. Adams Dudley, Stephen C. Waring, Tyler Winkelman, Peter Bodurtha, Kelly Bergmann, Inih Essien, Nayanjot K. Rai, Samuel T. Patnoe, Karen L. Margolis, MN EHR Consortium
Objective
The Minnesota Electronic Health Record Consortium (MNEHRC) was established during the early days of the COVID-19 pandemic to provide data for public health surveillance from the eleven largest health care systems in Minnesota.
Methods
This is a descriptive study of the Consortium, which is a federated network that implements best practices for governance and data infrastructure to support public health surveillance and clinical research. We conducted an analysis of the Consortium members, governance structure, infrastructure, and the characteristics of the patient population.
Results
The Consortium health systems collect information from 105 hospitals, 773 clinics, 100 emergency departments and 29 040 providers. Information about the health systems and the demographic and clinical characteristics of its 5 471 367 patients is provided, which represents more than 90% of the patients in Minnesota. This manuscript also details the MNEHRC governance structure, working groups, data use agreements and technical infrastructure. The Consortium has produced several studies with state-wide impact. One study, Health Trends Across Communities in Minnesota, is described in detail to illustrate aspects of this collaboration.
Conclusion
MNEHRC has been a successful collaboration and vital resource for public health surveillance in the state of Minnesota. Initially, the Consortium focused on surveillance related to COVID-19 infections and vaccinations but has recently expanded into other public health and chronic disease research.
{"title":"Design and Implementation of a State-Wide Network for Near Real-Time Public Health Surveillance and Research: The Minnesota Electronic Health Record Consortium Experience","authors":"Steven G. Johnson, Alanna M. Chamberlain, Paul Drawz, R. Adams Dudley, Stephen C. Waring, Tyler Winkelman, Peter Bodurtha, Kelly Bergmann, Inih Essien, Nayanjot K. Rai, Samuel T. Patnoe, Karen L. Margolis, MN EHR Consortium","doi":"10.1002/lrh2.70070","DOIUrl":"https://doi.org/10.1002/lrh2.70070","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>The Minnesota Electronic Health Record Consortium (MNEHRC) was established during the early days of the COVID-19 pandemic to provide data for public health surveillance from the eleven largest health care systems in Minnesota.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This is a descriptive study of the Consortium, which is a federated network that implements best practices for governance and data infrastructure to support public health surveillance and clinical research. We conducted an analysis of the Consortium members, governance structure, infrastructure, and the characteristics of the patient population.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The Consortium health systems collect information from 105 hospitals, 773 clinics, 100 emergency departments and 29 040 providers. Information about the health systems and the demographic and clinical characteristics of its 5 471 367 patients is provided, which represents more than 90% of the patients in Minnesota. This manuscript also details the MNEHRC governance structure, working groups, data use agreements and technical infrastructure. The Consortium has produced several studies with state-wide impact. One study, Health Trends Across Communities in Minnesota, is described in detail to illustrate aspects of this collaboration.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>MNEHRC has been a successful collaboration and vital resource for public health surveillance in the state of Minnesota. Initially, the Consortium focused on surveillance related to COVID-19 infections and vaccinations but has recently expanded into other public health and chronic disease research.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147288463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jennie G. David-Rodgers, Christina E. Holbein, Hannah McKillop, Maria E. Lester, Victoria Levine, Ildiko Mehes, Heidi C. Riechel, Jane R. Weyer, ImproveCareNow Learning Health System, Autoimmune Liver Disease Network for Kids Learning Health System
Objective
Caregivers of children with chronic conditions have mental health needs, with no known resources for caregiver coping in pediatric Inflammatory Bowel Disease (IBD) and autoimmune liver disease (AILD), which can be co-morbid. Quality improvement (QI) has previously co-produced resources within Learning Health Networks (LHNs). This QI work sought to co-produce a caregiver coping resource for caregivers of children with IBD and/or AILD.
Methods
A multidisciplinary QI team of caregivers and psychosocial clinicians applied QI methodology to iteratively develop a caregiver coping resource. This work took place within two connected LHNs, ImproveCareNow (pediatric IBD) and Autoimmune Liver Disease Network for Kids (AILD).
Results
Over 1.5 years, a multidisciplinary QI team of eight caregivers and four psychosocial clinicians co-produced a caregiver coping resource. The formatted caregiver coping resource is 164 pages long with sections including caregiver coping (e.g., anxiety), caregiver support of child coping (e.g., discussing emotions), special considerations (e.g., surgery in IBD), and logistical topics (e.g., navigating insurance). The resource integrates caregiver and psychosocial clinician quotes and reputable resources. The resource is freely available and information about how to access the resource is included in the manuscript text.
Conclusion
While caregivers of children with IBD and/or AILD have known coping needs, caregiver-focused coping resources are urgently needed. A multidisciplinary team of caregivers and psychosocial clinicians within LHNs co-produced such a resource that is freely accessible. This QI work demonstrates the collaborative potential to build caregiver-focused resources for all families to benefit. Future work is needed to understand the clinical use of this resource as well as the impact of this resource for caregivers of children with IBD and/or AILD.
{"title":"The Development and Co-Production of a Caregiver Coping Resource for Pediatric Inflammatory Bowel Disease and Autoimmune Liver Disease","authors":"Jennie G. David-Rodgers, Christina E. Holbein, Hannah McKillop, Maria E. Lester, Victoria Levine, Ildiko Mehes, Heidi C. Riechel, Jane R. Weyer, ImproveCareNow Learning Health System, Autoimmune Liver Disease Network for Kids Learning Health System","doi":"10.1002/lrh2.70061","DOIUrl":"10.1002/lrh2.70061","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>Caregivers of children with chronic conditions have mental health needs, with no known resources for caregiver coping in pediatric Inflammatory Bowel Disease (IBD) and autoimmune liver disease (AILD), which can be co-morbid. Quality improvement (QI) has previously co-produced resources within Learning Health Networks (LHNs). This QI work sought to co-produce a caregiver coping resource for caregivers of children with IBD and/or AILD.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A multidisciplinary QI team of caregivers and psychosocial clinicians applied QI methodology to iteratively develop a caregiver coping resource. This work took place within two connected LHNs, ImproveCareNow (pediatric IBD) and Autoimmune Liver Disease Network for Kids (AILD).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Over 1.5 years, a multidisciplinary QI team of eight caregivers and four psychosocial clinicians co-produced a caregiver coping resource. The formatted caregiver coping resource is 164 pages long with sections including caregiver coping (e.g., anxiety), caregiver support of child coping (e.g., discussing emotions), special considerations (e.g., surgery in IBD), and logistical topics (e.g., navigating insurance). The resource integrates caregiver and psychosocial clinician quotes and reputable resources. The resource is freely available and information about how to access the resource is included in the manuscript text.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>While caregivers of children with IBD and/or AILD have known coping needs, caregiver-focused coping resources are urgently needed. A multidisciplinary team of caregivers and psychosocial clinicians within LHNs co-produced such a resource that is freely accessible. This QI work demonstrates the collaborative potential to build caregiver-focused resources for all families to benefit. Future work is needed to understand the clinical use of this resource as well as the impact of this resource for caregivers of children with IBD and/or AILD.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12912881/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146228923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
John P. Donnelly, C. Ann Vitous, Kaylee W. Burgan, Gezan M. Yahya, Jessica L. Johnson, Jessica A. McDonald, Nicholas W. Bowersox, Linda M. Kawentel
Background
Understanding the priorities of patients and leaders can help organizations prepare for the future. This evaluation sought to generate actionable data on priorities in support of the Department of Veterans Affairs (VA) strategic plan, with priorities identified within the literature as well as among Veterans and VA leaders.
Methods
A rapid qualitative evaluation was conducted, including a document analysis based on published information, focus groups with Veterans associated with research centers, and semi-structured interviews with VA leaders. All data were analyzed using rapid qualitative methods, resulting in comprehensive templated descriptions of topics discussed and representative quotes. Summative content analysis was used to report frequencies and identify the most frequently mentioned priorities.
Results
A total of 170 documents (e.g., academic/research articles, government publications) were analyzed. Seventeen Veteran focus groups (92 participants) and 26 semi-structured interviews with VA leaders (representing 19 program offices) were conducted. There were differences in the most frequently mentioned priorities across sources, with none ranked in the top 5 for all. However, “Access and Continuity of Care,” “Health Benefits,” “Special Groups,” and “Workforce” were among the top 10 mentioned within all sources. Among these, “Workforce” was most mentioned within the document analysis, whereas “Access and Continuity of Care” was most mentioned in focus groups. Both “Health Benefits” and “Access and Continuity of Care” were most mentioned during leadership interviews.
Conclusions
Our findings suggest shared priorities to emphasize as VA looks to the future: improving care access, efficient and effective benefits processing, ensuring the needs of special Veteran groups are met, and sustaining an adequate workforce. However, further work should focus on understanding how Veterans and staff engage with the system to guide the investment of limited organizational resources. This work serves as an example of how rapid evaluation could be applied for strategic planning in other settings.
{"title":"Identifying Priorities for the Department of Veterans Affairs Strategic Plan: A Rapid Multi-Method Evaluation","authors":"John P. Donnelly, C. Ann Vitous, Kaylee W. Burgan, Gezan M. Yahya, Jessica L. Johnson, Jessica A. McDonald, Nicholas W. Bowersox, Linda M. Kawentel","doi":"10.1002/lrh2.70068","DOIUrl":"10.1002/lrh2.70068","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Understanding the priorities of patients and leaders can help organizations prepare for the future. This evaluation sought to generate actionable data on priorities in support of the Department of Veterans Affairs (VA) strategic plan, with priorities identified within the literature as well as among Veterans and VA leaders.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A rapid qualitative evaluation was conducted, including a document analysis based on published information, focus groups with Veterans associated with research centers, and semi-structured interviews with VA leaders. All data were analyzed using rapid qualitative methods, resulting in comprehensive templated descriptions of topics discussed and representative quotes. Summative content analysis was used to report frequencies and identify the most frequently mentioned priorities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>A total of 170 documents (e.g., academic/research articles, government publications) were analyzed. Seventeen Veteran focus groups (92 participants) and 26 semi-structured interviews with VA leaders (representing 19 program offices) were conducted. There were differences in the most frequently mentioned priorities across sources, with none ranked in the top 5 for all. However, “Access and Continuity of Care,” “Health Benefits,” “Special Groups,” and “Workforce” were among the top 10 mentioned within all sources. Among these, “Workforce” was most mentioned within the document analysis, whereas “Access and Continuity of Care” was most mentioned in focus groups. Both “Health Benefits” and “Access and Continuity of Care” were most mentioned during leadership interviews.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our findings suggest shared priorities to emphasize as VA looks to the future: improving care access, efficient and effective benefits processing, ensuring the needs of special Veteran groups are met, and sustaining an adequate workforce. However, further work should focus on understanding how Veterans and staff engage with the system to guide the investment of limited organizational resources. This work serves as an example of how rapid evaluation could be applied for strategic planning in other settings.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12900616/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146203217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rendelle Bolton, Steven B. Zeliadt, Justeen Hyde, Bella Etingen, Ekaterina Anderson, Anna Barker, Juliet Wu, Benjamin Kligler, Barbara G. Bokhour
<div> <section> <h3> Objective</h3> <p>Embedded research partnerships can advance the implementation of evidence-based policies and practices, including those aligned with person-centered care. Care delivery model transformations, such as the VA's person-centered Whole Health System (WHS), can benefit from an ongoing cycle of program implementation and their evaluation to inform future evolution. This paper describes how embedded researchers partnered with policy makers leading VA's WHS transformation to support its development, implementation, and scaling to illustrate lessons learned for embedded research.</p> </section> <section> <h3> Data Sources and Study Setting</h3> <p>Fifty-eight embedded research projects were conducted from FY2013 to FY2024.</p> </section> <section> <h3> Study Design</h3> <p>We recorded each project's scope, methodology, results, products, and impact. Through a group reflection process, we identified common cross-project lessons that fostered this successful embedded research partnership. Finally, we mapped projects to phases of Kilbourne's Knowledge to Action Framework (Pre-Implementation, Implementation, and Sustainment) to demonstrate how embedded researchers defined evaluation questions, evaluated WHS transformation, and assessed outcomes to inform the implementation and sustainment of VA's WHS transformation.</p> </section> <section> <h3> Data Collection/Extraction Methods</h3> <p>Projects used multiple qualitative, survey, and large database methods.</p> </section> <section> <h3> Principal Findings</h3> <p>Across 58 projects, 380 discrete products were used by our operational partner to refine the WHS model, improve implementation support, scale effective practices, inform new policy, and sustain transformation. Three practices cut across these projects to contribute to our successful embedded research partnership: agility, collaboration, and continuous learning and improvement. Additionally, the purpose, questions, and methods of embedded research projects varied as operational partner activities moved across pre-implementation, implementation, and sustainment phases, iteratively impacting WHS transformation.</p> </section> <section> <h3> Conclusions</h3> <p>Embedded research can transform a healthcare system through the timely translation of data into practice, enabling evidence-based policy and practice decisions
{"title":"Embedded Research in a Learning Health System: How a Research-Operations Partnership Informed the Development, Implementation, and Scaling of VA's Whole Health System","authors":"Rendelle Bolton, Steven B. Zeliadt, Justeen Hyde, Bella Etingen, Ekaterina Anderson, Anna Barker, Juliet Wu, Benjamin Kligler, Barbara G. Bokhour","doi":"10.1002/lrh2.70062","DOIUrl":"10.1002/lrh2.70062","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>Embedded research partnerships can advance the implementation of evidence-based policies and practices, including those aligned with person-centered care. Care delivery model transformations, such as the VA's person-centered Whole Health System (WHS), can benefit from an ongoing cycle of program implementation and their evaluation to inform future evolution. This paper describes how embedded researchers partnered with policy makers leading VA's WHS transformation to support its development, implementation, and scaling to illustrate lessons learned for embedded research.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>Fifty-eight embedded research projects were conducted from FY2013 to FY2024.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>We recorded each project's scope, methodology, results, products, and impact. Through a group reflection process, we identified common cross-project lessons that fostered this successful embedded research partnership. Finally, we mapped projects to phases of Kilbourne's Knowledge to Action Framework (Pre-Implementation, Implementation, and Sustainment) to demonstrate how embedded researchers defined evaluation questions, evaluated WHS transformation, and assessed outcomes to inform the implementation and sustainment of VA's WHS transformation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>Projects used multiple qualitative, survey, and large database methods.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Across 58 projects, 380 discrete products were used by our operational partner to refine the WHS model, improve implementation support, scale effective practices, inform new policy, and sustain transformation. Three practices cut across these projects to contribute to our successful embedded research partnership: agility, collaboration, and continuous learning and improvement. Additionally, the purpose, questions, and methods of embedded research projects varied as operational partner activities moved across pre-implementation, implementation, and sustainment phases, iteratively impacting WHS transformation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Embedded research can transform a healthcare system through the timely translation of data into practice, enabling evidence-based policy and practice decisions","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12828349/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146054291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Persistent knowledge is essential for propagating the learning health system (LHS) cycle. Integral to the cycle are iterative transformations of data into knowledge. However, human efforts to undertake these transformations are increasingly challenged when dealing with larger data scales and complexities. Data sets within repositories and archives are often underutilized unless specifically requested for research programs. Specialized software algorithms (agents) can use existing knowledge for learning tasks, explore their environment, discover and create goals, and interact with humans.
Methods
This paper examines the potential role of software intelligence for autonomous transformations of data and knowledge. Agents can perform various goal-directed tasks. Multi-agent systems can be utilized for data collection, description, preparation, modeling, and knowledge-mining tasks. Knowledge representation, ontologies, semantic web standards, knowledge bases, and graphs can lead to a higher level of directed learning. Agents can develop reasoning abilities and self-generate goals by leveraging semantic relationships between various datasets.
Results
A conceptual framework for an intelligent biomedical platform (IBP) is proposed. The IBP comprises four layers: infrastructure (IS), user interface (UI), coordination system (CS), and data and knowledge (DK). It also integrates a network of multi-agent systems for clinical decision-making and knowledge-mining tasks. Intelligence in the platform results from the interaction of the IS, UI, CS, and DK agents. These agents can implement multiple inferential steps using the data and knowledge within accessible repositories. Large language models can be integrated with various knowledge resources and domain-specific databases, thereby improving the accuracy of results.
Conclusion
An IBP supported by a multi-agent system can enhance the autonomous transformation of data and knowledge. Including software intelligence within current repositories and archives enhances data reuse and the generation of new knowledge. With the addition of software reasoning capabilities in biomedical platforms, the LHS cycle can be efficiently propagated to aid in newer biomedical discoveries.
持续性知识对于学习型卫生系统（LHS）周期的传播至关重要。这个循环的组成部分是数据到知识的迭代转换。然而，当处理更大的数据规模和复杂性时，人类进行这些转换的努力日益受到挑战。除非研究项目特别要求，否则存储库和档案中的数据集往往未得到充分利用。专门的软件算法（代理）可以使用现有的知识来完成学习任务，探索环境，发现和创建目标，并与人类进行交互。方法本文探讨了软件智能在数据和知识自主转换中的潜在作用。代理可以执行各种目标导向的任务。多智能体系统可以用于数据收集、描述、准备、建模和知识挖掘任务。知识表示、本体、语义web标准、知识库和图形可以导致更高层次的定向学习。智能体可以通过利用各种数据集之间的语义关系来发展推理能力和自我生成目标。结果提出了智能生物医学平台的概念框架。IBP由IS （infrastructure）、UI （user interface）、CS （coordination system）和DK （data and knowledge）四层组成。它还集成了一个多代理系统网络，用于临床决策和知识挖掘任务。平台中的智能来自于IS、UI、CS和DK代理之间的交互。这些代理可以使用可访问存储库中的数据和知识实现多个推理步骤。大型语言模型可以与各种知识资源和特定领域的数据库集成，从而提高结果的准确性。结论多智能体系统支持的IBP可以增强数据和知识的自主转换。在当前存储库和存档中包含软件智能可以增强数据重用和新知识的生成。随着生物医学平台中软件推理能力的增加，LHS周期可以有效地传播，以帮助更新的生物医学发现。
{"title":"A Perspective on Software Intelligence for Autonomous Transformations in Biomedical Data and Knowledge","authors":"Vivek Navale","doi":"10.1002/lrh2.70063","DOIUrl":"https://doi.org/10.1002/lrh2.70063","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Persistent knowledge is essential for propagating the learning health system (LHS) cycle. Integral to the cycle are iterative transformations of data into knowledge. However, human efforts to undertake these transformations are increasingly challenged when dealing with larger data scales and complexities. Data sets within repositories and archives are often underutilized unless specifically requested for research programs. Specialized software algorithms (agents) can use existing knowledge for learning tasks, explore their environment, discover and create goals, and interact with humans.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This paper examines the potential role of software intelligence for autonomous transformations of data and knowledge. Agents can perform various goal-directed tasks. Multi-agent systems can be utilized for data collection, description, preparation, modeling, and knowledge-mining tasks. Knowledge representation, ontologies, semantic web standards, knowledge bases, and graphs can lead to a higher level of directed learning. Agents can develop reasoning abilities and self-generate goals by leveraging semantic relationships between various datasets.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>A conceptual framework for an intelligent biomedical platform (IBP) is proposed. The IBP comprises four layers: infrastructure (IS), user interface (UI), coordination system (CS), and data and knowledge (DK). It also integrates a network of multi-agent systems for clinical decision-making and knowledge-mining tasks. Intelligence in the platform results from the interaction of the IS, UI, CS, and DK agents. These agents can implement multiple inferential steps using the data and knowledge within accessible repositories. Large language models can be integrated with various knowledge resources and domain-specific databases, thereby improving the accuracy of results.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>An IBP supported by a multi-agent system can enhance the autonomous transformation of data and knowledge. Including software intelligence within current repositories and archives enhances data reuse and the generation of new knowledge. With the addition of software reasoning capabilities in biomedical platforms, the LHS cycle can be efficiently propagated to aid in newer biomedical discoveries.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146002549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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