Pamela M. Dunlap, Kathleen M. Poploski, Catherine A. Anderson, Thiru M. Annaswamy, Melissa A. Clark, Peter C. Coyle, Natalie F. Douglas, Ann Marie Flores, Janet K. Freburger, Brian J. Hafner, Kenneth J. Harwood, Jeanne M. Hoffman, Adam R. Kinney, Linda Resnik, Kristin Ressel, Margarite J. Whitten, Christine M. McDonough
Introduction
In 2021, the Learning Health Systems Rehabilitation Research Network (LeaRRn) developed and administered a needs assessment survey, based on the Agency on Healthcare Research and Quality's (AHRQ's) original seven domains of learning health systems (LHS) researcher core competencies, to identify knowledge and interest in LHS research competencies among rehabilitation researchers. In 2022, the AHRQ added a new health and healthcare equity and justice (HE) domain to the existing seven domains for LHS researcher core competencies.
Methods
LeaRRn utilized methods similar to those employed in the development of their original needs assessment survey to generate and refine competency items for the HE domain. In this report, we describe the methods used to develop these HE competency items.
Results & Conclusions
Other training programs and LHS researchers may use the competency items developed for this needs assessment survey to identify training opportunities in the HE domain.
{"title":"Development of learning health system competency items related to health and healthcare equity and justice for rehabilitation researchers","authors":"Pamela M. Dunlap, Kathleen M. Poploski, Catherine A. Anderson, Thiru M. Annaswamy, Melissa A. Clark, Peter C. Coyle, Natalie F. Douglas, Ann Marie Flores, Janet K. Freburger, Brian J. Hafner, Kenneth J. Harwood, Jeanne M. Hoffman, Adam R. Kinney, Linda Resnik, Kristin Ressel, Margarite J. Whitten, Christine M. McDonough","doi":"10.1002/lrh2.10484","DOIUrl":"https://doi.org/10.1002/lrh2.10484","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>In 2021, the Learning Health Systems Rehabilitation Research Network (LeaRRn) developed and administered a needs assessment survey, based on the Agency on Healthcare Research and Quality's (AHRQ's) original seven domains of learning health systems (LHS) researcher core competencies, to identify knowledge and interest in LHS research competencies among rehabilitation researchers. In 2022, the AHRQ added a new health and healthcare equity and justice (HE) domain to the existing seven domains for LHS researcher core competencies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>LeaRRn utilized methods similar to those employed in the development of their original needs assessment survey to generate and refine competency items for the HE domain. In this report, we describe the methods used to develop these HE competency items.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results & Conclusions</h3>\u0000 \u0000 <p>Other training programs and LHS researchers may use the competency items developed for this needs assessment survey to identify training opportunities in the HE domain.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10484","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635670","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}
Brittany V. Barber, Douglas Sinclair, Christine Cassidy
<div> <section> <h3> Background</h3> <p>There is increasing demand for health systems to reduce greenhouse gas emissions and invest in climate-resilient health care. Coordinating organizational structures and processes for reducing health system emissions presents challenges. Learning health systems, defined as systems that seek to continuously generate and apply evidence, innovation, quality, and value in health care, can guide health systems with planning organizational structures and processes to advance environmentally sustainable healthcare. The purpose of this research is to gather in-depth insight from key health system leaders and healthcare professionals to identify challenges and recommendations for planning environmentally sustainable learning health systems.</p> </section> <section> <h3> Methods</h3> <p>Environmental scan methods were used, comprising jurisdictional literature review and informal discussions with key informants at one tertiary care center in Nova Scotia, Canada. Key informants were asked to describe challenges of coordinating environmentally sustainable health system structures and processes, and recommendations to advance planning for environmentally sustainable learning health systems. Deductive thematic analysis was used to categorize challenges and recommendations into seven characteristics of a learning health system framework.</p> </section> <section> <h3> Results</h3> <p>Informal discussions with 16 key informants provide detailed descriptions of 7 challenges and recommendations for planning and coordinating organizational structures and processes to advance environmentally sustainable learning health systems. Health system challenges include limited patient and community engagement, no systematic approach to measuring and monitoring emissions data, and limited knowledge of sustainability co-benefits and strategies for mobilizing sustainable organizational change. Recommendations include engaging patients and communities in co-creation of sustainable healthcare, monitoring of emissions data identifying high-impact areas for action, and well-coordinated leadership supporting sustainable policies, procedures, and decision-making in practice.</p> </section> <section> <h3> Conclusion</h3> <p>Learning health systems provide structure for establishing critical processes to adapt to routinely collected data through rapid cycle improvements, and operationalization of value-based health care that prioritizes health outcomes, reduction of costs, and mitigating environmental impacts.</p> </section>
{"title":"Advancing environmentally sustainable learning health systems: Perspectives from a Canadian health center","authors":"Brittany V. Barber, Douglas Sinclair, Christine Cassidy","doi":"10.1002/lrh2.10470","DOIUrl":"https://doi.org/10.1002/lrh2.10470","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>There is increasing demand for health systems to reduce greenhouse gas emissions and invest in climate-resilient health care. Coordinating organizational structures and processes for reducing health system emissions presents challenges. Learning health systems, defined as systems that seek to continuously generate and apply evidence, innovation, quality, and value in health care, can guide health systems with planning organizational structures and processes to advance environmentally sustainable healthcare. The purpose of this research is to gather in-depth insight from key health system leaders and healthcare professionals to identify challenges and recommendations for planning environmentally sustainable learning health systems.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Environmental scan methods were used, comprising jurisdictional literature review and informal discussions with key informants at one tertiary care center in Nova Scotia, Canada. Key informants were asked to describe challenges of coordinating environmentally sustainable health system structures and processes, and recommendations to advance planning for environmentally sustainable learning health systems. Deductive thematic analysis was used to categorize challenges and recommendations into seven characteristics of a learning health system framework.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Informal discussions with 16 key informants provide detailed descriptions of 7 challenges and recommendations for planning and coordinating organizational structures and processes to advance environmentally sustainable learning health systems. Health system challenges include limited patient and community engagement, no systematic approach to measuring and monitoring emissions data, and limited knowledge of sustainability co-benefits and strategies for mobilizing sustainable organizational change. Recommendations include engaging patients and communities in co-creation of sustainable healthcare, monitoring of emissions data identifying high-impact areas for action, and well-coordinated leadership supporting sustainable policies, procedures, and decision-making in practice.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Learning health systems provide structure for establishing critical processes to adapt to routinely collected data through rapid cycle improvements, and operationalization of value-based health care that prioritizes health outcomes, reduction of costs, and mitigating environmental impacts.</p>\u0000 </section>\u0000 ","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10470","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634985","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}
Tom Belleman, Jeroen D. H. van Wijngaarden, Malou C. P. Kuppen, Saskia de Groot, Kim J. M. van der Velden, Dianne Bosch, Inge M. van Oort, Carin A. Uyl-de Groot, Welmoed K. van Deen
Introduction
Learning health systems (LHSs) are systems that seamlessly embed continuous quality improvement based on real-world data. To establish LHSs, several infrastructures need to be in place. Registries already have part(s) of this infrastructure and could therefore be leveraged to establish LHSs. This study aims to identify key factors facilitating the transition of registries into LHS to support continuous learning from real-world data.
Methods
Eleven interviews with 12 stakeholders, including medical specialists and nonmedical stakeholders, were conducted in the context of a prostate cancer registry. Findings were coded deductively based on seven previously identified facilitators for learning: complexity, relative advantage, compatibility, credibility, social impact, actionability, and resource match. These facilitators cover technical, social, and organizational aspects. An inductive phase followed to pinpoint factors for continuous learning and LHSs. Subsequently, two focus groups were conducted to ensure accurate interpretation of findings, and five expert panels to provide additional context.
Results
Complexity within healthcare systems emerged as a significant challenge, attributed to multiple stakeholders and the rapidly changing healthcare landscape. The advantage of LHSs is the timely availability of population-based data for real-time care adjustments. Compatibility of the system with stakeholders' needs was considered pivotal requiring a relatively flexible infrastructure. Credibility of data and results was supported by creating transparent processes in which stakeholders could review data from their own patient population. Social influences, including interpersonal trust and engaged leadership, fostered collaboration within LHSs. Actionability of the findings and resource match were vital for knowledge translation and sustainability.
Conclusion
Our findings provide practical recommendations to support registries in transitioning towards LHSs by leveraging and expanding their infrastructure for continuous learning. We identified technical, interpersonal, and organizational factors that facilitate continuous and rapid learning using real-world data, create transparent and collaborative infrastructures, and help to navigate the complexity of the healthcare system.
{"title":"Moving from a registry to a learning health system: A case study of a Dutch prostate cancer registry","authors":"Tom Belleman, Jeroen D. H. van Wijngaarden, Malou C. P. Kuppen, Saskia de Groot, Kim J. M. van der Velden, Dianne Bosch, Inge M. van Oort, Carin A. Uyl-de Groot, Welmoed K. van Deen","doi":"10.1002/lrh2.10476","DOIUrl":"https://doi.org/10.1002/lrh2.10476","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Learning health systems (LHSs) are systems that seamlessly embed continuous quality improvement based on real-world data. To establish LHSs, several infrastructures need to be in place. Registries already have part(s) of this infrastructure and could therefore be leveraged to establish LHSs. This study aims to identify key factors facilitating the transition of registries into LHS to support continuous learning from real-world data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Eleven interviews with 12 stakeholders, including medical specialists and nonmedical stakeholders, were conducted in the context of a prostate cancer registry. Findings were coded deductively based on seven previously identified facilitators for learning: complexity, relative advantage, compatibility, credibility, social impact, actionability, and resource match. These facilitators cover technical, social, and organizational aspects. An inductive phase followed to pinpoint factors for continuous learning and LHSs. Subsequently, two focus groups were conducted to ensure accurate interpretation of findings, and five expert panels to provide additional context.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Complexity within healthcare systems emerged as a significant challenge, attributed to multiple stakeholders and the rapidly changing healthcare landscape. The advantage of LHSs is the timely availability of population-based data for real-time care adjustments. Compatibility of the system with stakeholders' needs was considered pivotal requiring a relatively flexible infrastructure. Credibility of data and results was supported by creating transparent processes in which stakeholders could review data from their own patient population. Social influences, including interpersonal trust and engaged leadership, fostered collaboration within LHSs. Actionability of the findings and resource match were vital for knowledge translation and sustainability.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Our findings provide practical recommendations to support registries in transitioning towards LHSs by leveraging and expanding their infrastructure for continuous learning. We identified technical, interpersonal, and organizational factors that facilitate continuous and rapid learning using real-world data, create transparent and collaborative infrastructures, and help to navigate the complexity of the healthcare system.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10476","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635508","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}
Stanford Medicine is working to better coordinate care across the Stanford healthcare system, as well as improve patient and provider experiences in seeking and receiving care. This study aimed to explore the complexities of moving from a fragmented to an integrated academic healthcare system and to identify and explain factors (e.g., facilitators and barriers) of the implementation of three interventions meant to improve patient experience, reduce staff burden, and integrate health care systems across faculty and community settings.
Methods
We conducted qualitative semi-structured interviews via Zoom with faculty and community physicians. Interviews were audio-recorded, professionally transcribed, and analyzed using the Consolidated Framework for Implementation Research (CFIR) and open coding. Using consensus coding approaches, researchers met regularly to discuss themes and adaptations to CFIR.
Results
We analyzed transcripts from interviews with physicians (n = 26). Factors impacting integration included the following: (1) physicians supported the interventions, promoting mission alignment; (2) physicians were motivated for change, reporting the existing system was intolerable; (3) physicians reported different priorities between clinics: faculty versus community and primary care versus specialty; (4) physicians prioritized interpersonal versus system solutions; (5) specialists were wary of unintended consequences of integration, specifically inappropriate bookings or patients being redirected to other clinics. Broadly speaking, facilitator factors 1–2 focused on the openness to, and tension for, change; and barrier factors 3–5 promoted or sustained variation across specialties and faculty/community clinics.
Conclusions
Our results illustrate the challenges and opportunities of moving from a fragmented to an integrated healthcare system and emphasize the importance of building shared culture, collaboration, and coordinated actions across and within an integrated healthcare network.
{"title":"Exploring implementation of interventions to facilitate integration in fragmented healthcare systems","authors":"Cassandra Bragdon, Rachel Siden, Marcy Winget, Sonia Rose Harris, Rebecca Carey, Justin Ko, Alpa Vyas, Cati Brown-Johnson","doi":"10.1002/lrh2.10483","DOIUrl":"https://doi.org/10.1002/lrh2.10483","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Stanford Medicine is working to better coordinate care across the Stanford healthcare system, as well as improve patient and provider experiences in seeking and receiving care. This study aimed to explore the complexities of moving from a fragmented to an integrated academic healthcare system and to identify and explain factors (e.g., facilitators and barriers) of the implementation of three interventions meant to improve patient experience, reduce staff burden, and integrate health care systems across faculty and community settings.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We conducted qualitative semi-structured interviews via Zoom with faculty and community physicians. Interviews were audio-recorded, professionally transcribed, and analyzed using the Consolidated Framework for Implementation Research (CFIR) and open coding. Using consensus coding approaches, researchers met regularly to discuss themes and adaptations to CFIR.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We analyzed transcripts from interviews with physicians (<i>n</i> = 26). Factors impacting integration included the following: (1) physicians supported the interventions, promoting mission alignment; (2) physicians were motivated for change, reporting the existing system was intolerable; (3) physicians reported different priorities between clinics: faculty versus community and primary care versus specialty; (4) physicians prioritized interpersonal versus system solutions; (5) specialists were wary of unintended consequences of integration, specifically inappropriate bookings or patients being redirected to other clinics. Broadly speaking, facilitator factors 1–2 focused on the openness to, and tension for, change; and barrier factors 3–5 promoted or sustained variation across specialties and faculty/community clinics.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our results illustrate the challenges and opportunities of moving from a fragmented to an integrated healthcare system and emphasize the importance of building shared culture, collaboration, and coordinated actions across and within an integrated healthcare network.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10483","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635019","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}
Windsor Westbrook Sherrill, Luke Hall, Lawrence Fredendall, Janet Hoffman Evatt
Introduction
A learning health system (LHS) necessitates collaboration to produce translational health research. This experience report examines the integration of Clemson University scholars into clinical departments of Prisma Health–Upstate in South Carolina, highlighting their experiences working alongside clinician mentors to inform and facilitate research translation. Particularly, this study aims to explore the interpersonal and structural factors influencing the success of an embedded scholar program, focusing on enablers and barriers to collaboration, knowledge integration, and mentorship within the LHS.
Methods
Nine embedded scholar and 12 mentor semi-structured interviews were conducted. This qualitative study initially used an inductive technique to analyze responses thematically. After thematic saturation was achieved, deductive analysis was utilized to further organize enablers and barriers across the following five categories: (1) Scholar Integration, (2) Scholar Autonomy, (3) Mentor Support, (4) Programmatic Outcomes, and (5) Institutional Dynamics.
Results
We found 10 major program-related enablers and barriers to successfully embedding scholars. These were clinical environment adaptation, mentor interaction, research management, balance of independence, role clarity, resource provision, research application and quality, scholar development, organizational support, and policy and procedure alignment. Findings reveal that effective mentorship, organizational alignment, and resource availability are critical enablers of program success, while misaligned expectations, limited institutional support, and insufficient scholar integration into clinical environments are barriers.
Conclusion
Evaluating specific components of embedded scholar programs can uncover best practices and innovation opportunities in the LHS. These provide a great opportunity to enhance the mentorship mechanisms between clinical mentors and embedded researchers. As research on embedded scholars in a LHS progresses, fostering structured mentoring relationships may serve as an impetus to bridge the gap between research and clinical practice. Further study is needed to operationalize these relationships effectively.
{"title":"Bridging research and practice in a learning health system: Developing and refining an embedded scholars program through insights from scholars and clinical mentors","authors":"Windsor Westbrook Sherrill, Luke Hall, Lawrence Fredendall, Janet Hoffman Evatt","doi":"10.1002/lrh2.10481","DOIUrl":"https://doi.org/10.1002/lrh2.10481","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>A learning health system (LHS) necessitates collaboration to produce translational health research. This experience report examines the integration of Clemson University scholars into clinical departments of Prisma Health–Upstate in South Carolina, highlighting their experiences working alongside clinician mentors to inform and facilitate research translation. Particularly, this study aims to explore the interpersonal and structural factors influencing the success of an embedded scholar program, focusing on enablers and barriers to collaboration, knowledge integration, and mentorship within the LHS.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Nine embedded scholar and 12 mentor semi-structured interviews were conducted. This qualitative study initially used an inductive technique to analyze responses thematically. After thematic saturation was achieved, deductive analysis was utilized to further organize enablers and barriers across the following five categories: (1) Scholar Integration, (2) Scholar Autonomy, (3) Mentor Support, (4) Programmatic Outcomes, and (5) Institutional Dynamics.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We found 10 major program-related enablers and barriers to successfully embedding scholars. These were clinical environment adaptation, mentor interaction, research management, balance of independence, role clarity, resource provision, research application and quality, scholar development, organizational support, and policy and procedure alignment. Findings reveal that effective mentorship, organizational alignment, and resource availability are critical enablers of program success, while misaligned expectations, limited institutional support, and insufficient scholar integration into clinical environments are barriers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Evaluating specific components of embedded scholar programs can uncover best practices and innovation opportunities in the LHS. These provide a great opportunity to enhance the mentorship mechanisms between clinical mentors and embedded researchers. As research on embedded scholars in a LHS progresses, fostering structured mentoring relationships may serve as an impetus to bridge the gap between research and clinical practice. Further study is needed to operationalize these relationships effectively.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10481","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635204","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}
The acute care system faced significant challenges in managing healthcare emergencies due to a lack of coordination between emergency services and logistical support. This disorganization undermined collaboration and response efficiency.
Methods
Taiwan's Presidential Hackathon introduced an innovative approach to improving the trauma system by integrating digital pipeline science through public–private partnerships (PPPs). This initiative specifically addressed inefficiencies and complexities in the acute care ecosystem, brought to light by the catastrophic 2014 gas explosion in Kaohsiung City.
Results
The hackathon led to the development of a unified digital platform for emergency data management. This platform significantly enhanced communication, data sharing, and coordination across healthcare sectors, culminating in the implementation of a digital pre-hospital emergency care system across multiple administrative regions.
Conclusion
Our experience demonstrated the effectiveness of leveraging digital technologies, PPPs, and the hackathon model to revolutionize emergency healthcare management and response systems through cross-sector collaboration.
{"title":"Public–private partnership in pipelining science of acute care ecosystem: Insights from Taiwan's Presidential Hackathon","authors":"Chao-Wen Chen, Yung-Sung Yeh, Ta-Chien Chan, Yi-Syuan Wu","doi":"10.1002/lrh2.10474","DOIUrl":"https://doi.org/10.1002/lrh2.10474","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>The acute care system faced significant challenges in managing healthcare emergencies due to a lack of coordination between emergency services and logistical support. This disorganization undermined collaboration and response efficiency.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Taiwan's Presidential Hackathon introduced an innovative approach to improving the trauma system by integrating digital pipeline science through public–private partnerships (PPPs). This initiative specifically addressed inefficiencies and complexities in the acute care ecosystem, brought to light by the catastrophic 2014 gas explosion in Kaohsiung City.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The hackathon led to the development of a unified digital platform for emergency data management. This platform significantly enhanced communication, data sharing, and coordination across healthcare sectors, culminating in the implementation of a digital pre-hospital emergency care system across multiple administrative regions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Our experience demonstrated the effectiveness of leveraging digital technologies, PPPs, and the hackathon model to revolutionize emergency healthcare management and response systems through cross-sector collaboration.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10474","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635203","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}
Victor C. Rentes, Claire Kalpakjian, Anne Sales, Andrew Krumm
Background
The operationalization of learning health system (LHS) principles remains challenging, with minimal guidance currently available to support interprofessional teams on the ground. Consequently, LHS initiatives often fall short of their intended objectives, resulting in wasted resources, delays, and mounting frustration among key stakeholders.
Methods
To bridge this gap, we used design science and participatory action research to co-develop an operational roadmap for interprofessional LHS teams. Data sources for roadmap design included quantitative and qualitative feedback from interprofessional stakeholders (n = 20) from an academic health system and a pragmatic literature review. Using these data sources, we conducted three design iterations until a final version was reached.
Results
The resulting roadmap specifies processes to be performed during project-based LHS initiatives, and provides a self-assessment tool that enables team members to quantitatively evaluate progress. For generalizability and standardization across settings, we used clinically neutral terminology to describe all elements in the roadmap. We demonstrated content validity through multiple rounds of data collection and analyses with stakeholders. A simulated demonstration is provided to illustrate how the roadmap may be used for team assessments in practice.
Conclusions
Participants considered the roadmap to be an effective tool to assist project management and highly useful for evaluating teams' progress for planning and communication purposes. As a reference model, the roadmap may be re-utilized across multiple LHS initiatives in any given health system to standardize and streamline LHS development. This research was conducted within a single department in an academic health system, and future research is needed to assess the roadmap's generalizability in other settings. To facilitate development of similar or complementary instruments, the detailed design methodology used in this research may be replicated and/or tailored in other contexts.
{"title":"Operationalizing a learning health system: A self-assessment tool for interprofessional teams","authors":"Victor C. Rentes, Claire Kalpakjian, Anne Sales, Andrew Krumm","doi":"10.1002/lrh2.10482","DOIUrl":"https://doi.org/10.1002/lrh2.10482","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The operationalization of learning health system (LHS) principles remains challenging, with minimal guidance currently available to support interprofessional teams on the ground. Consequently, LHS initiatives often fall short of their intended objectives, resulting in wasted resources, delays, and mounting frustration among key stakeholders.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>To bridge this gap, we used design science and participatory action research to co-develop an operational roadmap for interprofessional LHS teams. Data sources for roadmap design included quantitative and qualitative feedback from interprofessional stakeholders (<i>n</i> = 20) from an academic health system and a pragmatic literature review. Using these data sources, we conducted three design iterations until a final version was reached.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The resulting roadmap specifies processes to be performed during project-based LHS initiatives, and provides a self-assessment tool that enables team members to quantitatively evaluate progress. For generalizability and standardization across settings, we used clinically neutral terminology to describe all elements in the roadmap. We demonstrated content validity through multiple rounds of data collection and analyses with stakeholders. A simulated demonstration is provided to illustrate how the roadmap may be used for team assessments in practice.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Participants considered the roadmap to be an effective tool to assist project management and highly useful for evaluating teams' progress for planning and communication purposes. As a reference model, the roadmap may be re-utilized across multiple LHS initiatives in any given health system to standardize and streamline LHS development. This research was conducted within a single department in an academic health system, and future research is needed to assess the roadmap's generalizability in other settings. To facilitate development of similar or complementary instruments, the detailed design methodology used in this research may be replicated and/or tailored in other contexts.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10482","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634999","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}
Lauren Hajjar, Olawale Olaleye, Julius Yang, Susan McGirr, Erin E. Sullivan
Introduction
Most change interventions to address quality of care and lower costs focus on technical aspects of the work through process improvements, which have not consistently delivered the anticipated impact for healthcare organizations. This study aims to (1) understand how relational interventions including shared huddles and cross-role shadowing opportunities, impact team dynamics and functioning and (2) describe the challenges and opportunities associated with implementing relational interventions at an Academic Medical Center in a large metropolitan city in the United States.
Methods
This paper is a mixed method, pre–post-intervention study in which data were collected using a validated survey, observations, interviews, and one focus group. Relational coordination survey data were analyzed within and across eight interdependent workgroups on three inpatient medical units at baseline and 16 months post-intervention. Qualitative data were coded and analyzed for themes.
Results
While there were some improvements in overall relational coordination between baseline and post-intervention measures, the findings were not statistically significant. Qualitative data reveal four themes, highlighting the strengths and barriers to the intervention: (1) incomplete fidelity to the relational coordination framework, (2) leadership, (3) meeting structure and participation, and (4) stakeholder engagement.
Conclusions
Within the healthcare context, this study contributes to our learning about implementing and measuring relational interventions. We offer insights for future research and practice on change initiative overload and operational constraints, socializing relational interventions, and balancing core and non-core roles in the intervention strategy.
{"title":"Relational coordination and team-based care: Change initiative overload and other challenges in a learning health system","authors":"Lauren Hajjar, Olawale Olaleye, Julius Yang, Susan McGirr, Erin E. Sullivan","doi":"10.1002/lrh2.10455","DOIUrl":"https://doi.org/10.1002/lrh2.10455","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Most change interventions to address quality of care and lower costs focus on technical aspects of the work through process improvements, which have not consistently delivered the anticipated impact for healthcare organizations. This study aims to (1) understand how relational interventions including shared huddles and cross-role shadowing opportunities, impact team dynamics and functioning and (2) describe the challenges and opportunities associated with implementing relational interventions at an Academic Medical Center in a large metropolitan city in the United States.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This paper is a mixed method, pre–post-intervention study in which data were collected using a validated survey, observations, interviews, and one focus group. Relational coordination survey data were analyzed within and across eight interdependent workgroups on three inpatient medical units at baseline and 16 months post-intervention. Qualitative data were coded and analyzed for themes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>While there were some improvements in overall relational coordination between baseline and post-intervention measures, the findings were not statistically significant. Qualitative data reveal four themes, highlighting the strengths and barriers to the intervention: (1) incomplete fidelity to the relational coordination framework, (2) leadership, (3) meeting structure and participation, and (4) stakeholder engagement.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Within the healthcare context, this study contributes to our learning about implementing and measuring relational interventions. We offer insights for future research and practice on change initiative overload and operational constraints, socializing relational interventions, and balancing core and non-core roles in the intervention strategy.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10455","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634998","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}
Dushka Crane, Mary Applegate, Gilbert Liu, Allison Lorenz, Shari Bolen, Christopher R. Jordan, Melissa McCoy, Jon Barley, Yan Yuan, Katie Jenkins, Melissa Nance, Amber Waweru, Jayne Kubiak, Caitlin Lorincz, Doug Spence
Introduction
In 2022, the Ohio Department of Medicaid (ODM) launched a Managed Care Population Health and Quality Strategy to improve healthcare quality and equity for Medicaid Managed Care enrollees. Aligned with national quality objectives, the strategy focuses on personalized care, service coordination for complex needs, reducing health disparities, and includes performance incentives for Managed Care Organizations (MCOs) and innovative provider payment models. While Ohio has made progress in quality improvement, challenges remain in addressing statewide health indicators and disparities and helping healthcare providers adapt to performance-based models. This report outlines a new approach that builds on Ohio's partnership with six colleges of medicine (CoMs) to support provider organizations and engage stakeholders in quality improvement (QI).
Methods
ODM established Regional QI Hubs within Ohio's CoMs to advance population health initiatives using the Model for Improvement developed by the Associate in Process Improvement. These academically based hubs collaborate with local healthcare clinics, community partners, and payers on QI projects to enhance care, reduce disparities, and strengthen health systems. By engaging stakeholders in designing and testing change ideas using Plan-Do-Study-Act cycles and electronic health record data feedback, QI Hubs further the goals of the learning health system.
Results
Key lessons highlight the benefits of engaging academic institutions to build internal QI capacity and promote health equity. The model required substantial capacity building and commitment on behalf of academic institutions and strengthening of regional partnerships. Collaboration between MCOs and health clinics is focused on standardizing processes to access services and implement best practices. Patient, family, and community engagement efforts aim to improve patient experience and address drivers of health equity. Each partner leverages resources and benefits from the collaboration.
Conclusions
Ohio's academically based Regional QI Hub Model offers a promising approach to advancing population health. Policymakers are encouraged to consider integrating academic expertise into state quality strategies.
{"title":"Academically based regional quality improvement hubs: Advancing Medicaid's quality strategy in the state of Ohio through state-academic partnerships","authors":"Dushka Crane, Mary Applegate, Gilbert Liu, Allison Lorenz, Shari Bolen, Christopher R. Jordan, Melissa McCoy, Jon Barley, Yan Yuan, Katie Jenkins, Melissa Nance, Amber Waweru, Jayne Kubiak, Caitlin Lorincz, Doug Spence","doi":"10.1002/lrh2.10480","DOIUrl":"https://doi.org/10.1002/lrh2.10480","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>In 2022, the Ohio Department of Medicaid (ODM) launched a Managed Care Population Health and Quality Strategy to improve healthcare quality and equity for Medicaid Managed Care enrollees. Aligned with national quality objectives, the strategy focuses on personalized care, service coordination for complex needs, reducing health disparities, and includes performance incentives for Managed Care Organizations (MCOs) and innovative provider payment models. While Ohio has made progress in quality improvement, challenges remain in addressing statewide health indicators and disparities and helping healthcare providers adapt to performance-based models. This report outlines a new approach that builds on Ohio's partnership with six colleges of medicine (CoMs) to support provider organizations and engage stakeholders in quality improvement (QI).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>ODM established Regional QI Hubs within Ohio's CoMs to advance population health initiatives using the Model for Improvement developed by the Associate in Process Improvement. These academically based hubs collaborate with local healthcare clinics, community partners, and payers on QI projects to enhance care, reduce disparities, and strengthen health systems. By engaging stakeholders in designing and testing change ideas using Plan-Do-Study-Act cycles and electronic health record data feedback, QI Hubs further the goals of the learning health system.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Key lessons highlight the benefits of engaging academic institutions to build internal QI capacity and promote health equity. The model required substantial capacity building and commitment on behalf of academic institutions and strengthening of regional partnerships. Collaboration between MCOs and health clinics is focused on standardizing processes to access services and implement best practices. Patient, family, and community engagement efforts aim to improve patient experience and address drivers of health equity. Each partner leverages resources and benefits from the collaboration.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Ohio's academically based Regional QI Hub Model offers a promising approach to advancing population health. Policymakers are encouraged to consider integrating academic expertise into state quality strategies.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10480","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635188","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}
Saketh Boddapati, University of Michigan College of Literature, Science, and the Arts
[email protected]
Yongqun “Oliver” He, University of Michigan Medical School
[email protected]
Healthcare providers learn continuously as a core part of their work. However, as the rate of knowledge production in biomedicine increases, better support for providers' continuous learning is needed. Tools for learning from clinical data are widely available in the form of clinical quality dashboards and feedback reports. However, these tools seem to be frequently unused.
Making clinical data useful as feedback for learning appears to be a key challenge for health systems. Feedback can include coaching, evaluation, and appreciation, but systems developed for performance improvement do not adequately recognize these purposes in the context of provider learning. Moreover, providers have different information needs, motivational orientations, and workplace cultures, all of which affect the usefulness of data as feedback.
To increase the usefulness of data as feedback, we developed a Precision Feedback Knowledge Base (PFKB) for a precision feedback system. PFKB contains knowledge about how feedback influences motivation, to enable the precision feedback system to compute a motivational potential score for possible feedback messages. PFKB has four primary knowledge components: (1) causal pathway models, (2) message templates, (3) performance measures, and (4) annotations of motivating information in clinical data. We also developed vignettes about 7 diverse provider personas to illustrate how the precision feedback system uses PFKB in the context of anesthesia care. This ongoing research includes a pilot study that has demonstrated the technical feasibility of the precision feedback system, in preparation for a trial of precision feedback in an anesthesia quality improvement consortium.
Bruce Bray, University of Utah, on behalf of the HL7 Learning Health Systems Work Group
[email protected]
Data is the lifeblood of computable biomedical knowledge (CBK) and must adhere to standards to achieve the interoperability needed to generate virtuous learning cycles within a learning health system (LHS). The HL7 Learning Health System Work Group (HL7 LHS WG) conducted a scoping review to compile an initial list of standards that can support the LHS across “quadrants” of a virtuous learning cycle: (1) knowledge to action, (2) action to data, (3) data to evidence, and (4) evidence to knowledge. We found that few standards explicitly refer to an overarching framework that aligns interoperability and data standards across the phases of the LHS. We will describe our initial work to identify relevant gaps and overlaps in standards in this environment. Future work should address standards coordination and pilot testing within an LHS framework. The
{"title":"2024 MCBK North American chapter meeting—Lightning talk and demonstration abstracts","authors":"","doi":"10.1002/lrh2.10479","DOIUrl":"https://doi.org/10.1002/lrh2.10479","url":null,"abstract":"<p><b>POSTERS</b></p><p><b>DEMONSTRATIONS</b></p><p>Saketh Boddapati, University of Michigan College of Literature, Science, and the Arts</p><p><span>[email protected]</span></p><p>Yongqun “Oliver” He, University of Michigan Medical School</p><p><span>[email protected]</span></p><p>Healthcare providers learn continuously as a core part of their work. However, as the rate of knowledge production in biomedicine increases, better support for providers' continuous learning is needed. Tools for learning from clinical data are widely available in the form of clinical quality dashboards and feedback reports. However, these tools seem to be frequently unused.</p><p>Making clinical data useful as feedback for learning appears to be a key challenge for health systems. Feedback can include coaching, evaluation, and appreciation, but systems developed for performance improvement do not adequately recognize these purposes in the context of provider learning. Moreover, providers have different information needs, motivational orientations, and workplace cultures, all of which affect the usefulness of data as feedback.</p><p>To increase the usefulness of data as feedback, we developed a Precision Feedback Knowledge Base (PFKB) for a precision feedback system. PFKB contains knowledge about how feedback influences motivation, to enable the precision feedback system to compute a motivational potential score for possible feedback messages. PFKB has four primary knowledge components: (1) causal pathway models, (2) message templates, (3) performance measures, and (4) annotations of motivating information in clinical data. We also developed vignettes about 7 diverse provider personas to illustrate how the precision feedback system uses PFKB in the context of anesthesia care. This ongoing research includes a pilot study that has demonstrated the technical feasibility of the precision feedback system, in preparation for a trial of precision feedback in an anesthesia quality improvement consortium.</p><p>Bruce Bray, University of Utah, on behalf of the HL7 Learning Health Systems Work Group</p><p><span>[email protected]</span></p><p>Data is the lifeblood of computable biomedical knowledge (CBK) and must adhere to standards to achieve the interoperability needed to generate virtuous learning cycles within a learning health system (LHS). The HL7 Learning Health System Work Group (HL7 LHS WG) conducted a scoping review to compile an initial list of standards that can support the LHS across “quadrants” of a virtuous learning cycle: (1) knowledge to action, (2) action to data, (3) data to evidence, and (4) evidence to knowledge. We found that few standards explicitly refer to an overarching framework that aligns interoperability and data standards across the phases of the LHS. We will describe our initial work to identify relevant gaps and overlaps in standards in this environment. Future work should address standards coordination and pilot testing within an LHS framework. The","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10479","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143111476","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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