A longitudinal sensitivity-based curriculum focused on diversity, equity, and inclusion (DEI) and consisting of interactive seminars and roundtable discussions was constructed to equip participants with tools to enhance culturally effective care and to create an inclusive learning environment. The purpose of this study was to report our single-institutional experience with the development and implementation of this evidence-based curriculum.
Methods and Materials
Core DEI themes on which to center the sensitivity training curriculum were identified through an evidence-based review based on the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) statement. A MEDLINE search was undertaken to identify original peer-reviewed works using the items “equity, diversity, inclusion,” “underserved,” “disadvantaged,” “sensitivity training,” and “curriculum.”
Results
Based on the search results, a 12-month curriculum was established centered on the core themes that emerged. The 98 peer-reviewed publications chosen to develop this thematic framework could be broadly categorized as follows: health disparities (N = 33); workplace diversity (N = 24); implicit bias (N = 21); and structural racism (N = 20). Between November 2022 and January 2024, a total of 12 interactive sessions were scheduled. The mean number of attendees for each DEI session was 19 (range: 3 to 32), and the mean length of each session was 50 min (range: 20 to 81).
Conclusion
The developed curriculum helped promote awareness of historical inequities in healthcare and empowered learners to become better advocates for colleagues and patients alike. The implication for healthcare leaders are discussed.
{"title":"Development and implementation of a sensitivity training curriculum focused on equity in healthcare","authors":"Allen M. Chen","doi":"10.1002/lrh2.70007","DOIUrl":"https://doi.org/10.1002/lrh2.70007","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>A longitudinal sensitivity-based curriculum focused on diversity, equity, and inclusion (DEI) and consisting of interactive seminars and roundtable discussions was constructed to equip participants with tools to enhance culturally effective care and to create an inclusive learning environment. The purpose of this study was to report our single-institutional experience with the development and implementation of this evidence-based curriculum.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods and Materials</h3>\u0000 \u0000 <p>Core DEI themes on which to center the sensitivity training curriculum were identified through an evidence-based review based on the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) statement. A MEDLINE search was undertaken to identify original peer-reviewed works using the items “equity, diversity, inclusion,” “underserved,” “disadvantaged,” “sensitivity training,” and “curriculum.”</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Based on the search results, a 12-month curriculum was established centered on the core themes that emerged. The 98 peer-reviewed publications chosen to develop this thematic framework could be broadly categorized as follows: health disparities (<i>N</i> = 33); workplace diversity (<i>N</i> = 24); implicit bias (<i>N</i> = 21); and structural racism (<i>N</i> = 20). Between November 2022 and January 2024, a total of 12 interactive sessions were scheduled. The mean number of attendees for each DEI session was 19 (range: 3 to 32), and the mean length of each session was 50 min (range: 20 to 81).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The developed curriculum helped promote awareness of historical inequities in healthcare and empowered learners to become better advocates for colleagues and patients alike. The implication for healthcare leaders are discussed.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634988","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}
Emily Bacon, Laura J. Podewils, Brooke Bender, Joshua Blum, Christian Thurstone, Alia Al-Tayyib, John Mills, Alexandra R. Tillman, Judith Shlay
Introduction
Substance use disorders (SUDs) continue to place heavy burdens on patients, health systems, and communities. Rapidly evolving treatment for SUDs requires health systems that can efficiently and effectively assess data and integrate evidence to practice. This paper describes the implementation of a learning health systems model of SUD care at an urban, safety-net, integrated health system in Denver, Colorado.
Methods
The health system created the Center for Addiction Medicine (CAM) to coordinate comprehensive services for patients with SUD, including research, evaluation, and quality improvement efforts. CAM implemented a hub-and-spoke model of care and developed four strategic areas to identify, integrate, and grow SUD services. CAM also created a process for engaging community members with lived experience with SUDs and a CAM Academy to educate partners within the health system and across the region.
Results
CAM successfully implemented processes for gathering and assessing evidence, informing clinical practice, fostering community partnerships, and strategically expanding services and reach. Key factors driving success include strategic planning, leadership buy-in, data infrastructure, and community partnerships.
Conclusions
Integrated SUD care at a safety-net institution is a continued challenge. Persistent barriers include the SUD policy and regulatory landscape, addressing co-occurring social and health circumstances when treating SUDs, and accurate clinical documentation of SUDs. Applying a learning health system model can help health systems adapt to these challenges.
{"title":"A learning health system model for addressing substance use: Denver Health Center for Addiction Medicine","authors":"Emily Bacon, Laura J. Podewils, Brooke Bender, Joshua Blum, Christian Thurstone, Alia Al-Tayyib, John Mills, Alexandra R. Tillman, Judith Shlay","doi":"10.1002/lrh2.70003","DOIUrl":"https://doi.org/10.1002/lrh2.70003","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Substance use disorders (SUDs) continue to place heavy burdens on patients, health systems, and communities. Rapidly evolving treatment for SUDs requires health systems that can efficiently and effectively assess data and integrate evidence to practice. This paper describes the implementation of a learning health systems model of SUD care at an urban, safety-net, integrated health system in Denver, Colorado.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The health system created the Center for Addiction Medicine (CAM) to coordinate comprehensive services for patients with SUD, including research, evaluation, and quality improvement efforts. CAM implemented a hub-and-spoke model of care and developed four strategic areas to identify, integrate, and grow SUD services. CAM also created a process for engaging community members with lived experience with SUDs and a CAM Academy to educate partners within the health system and across the region.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>CAM successfully implemented processes for gathering and assessing evidence, informing clinical practice, fostering community partnerships, and strategically expanding services and reach. Key factors driving success include strategic planning, leadership buy-in, data infrastructure, and community partnerships.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Integrated SUD care at a safety-net institution is a continued challenge. Persistent barriers include the SUD policy and regulatory landscape, addressing co-occurring social and health circumstances when treating SUDs, and accurate clinical documentation of SUDs. Applying a learning health system model can help health systems adapt to these challenges.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635179","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}
Isomi Miake-Lye, Selene Mak, Tom Valente, Nicole Floyd, Paul Shekelle
Introduction
The Veterans Administration Evidence Synthesis Program (VA ESP) was established in 2007 to support evidence-based decisions about policies and programs in the VA healthcare system and has served as a critical component in the VA learning health system. Our objective was to document the growth of the ESP partner network and key policy and practice impacts of the program through a descriptive social network analysis and case series. We also aimed to provide insights into key features of some successful partnerships and the role evidence synthesis can play in supporting the improvement of the health system.
Methods
Using social network analysis and national ESP program data, we depicted the network at its initial, fully operational, and established phases of development to describe over a decade of ESP partnerships. We also queried program leadership and ESP partners about their collaboration experiences to generate a case series that demonstrates key examples of successful partnerships.
Results
Of the 82 partner offices for whom the ESP has generated a report, over half (66%, n = 54) collaborated with the ESP on multiple projects, having received two or more synthesis products over time. Case studies revealed the impacts of ESP evidence synthesis on policy and practice, how our partners used the evidence generated from these processes, and the future work spawned from these efforts. While infrastructure and formal mechanisms can support these connections, relationships and partnering are instrumental facets as well.
Conclusion
Partnerships between the ESP and decision-makers can facilitate access to evidence and leadership's ongoing use of this evidence. The ESP has led to meaningful, evidence-driven communication between research and operational partners and has supported stakeholders in informing policy and system change.
{"title":"How an evidence synthesis program's growth has supported research use and learning community partnership networks within a learning health system: A descriptive analysis and case series","authors":"Isomi Miake-Lye, Selene Mak, Tom Valente, Nicole Floyd, Paul Shekelle","doi":"10.1002/lrh2.70004","DOIUrl":"https://doi.org/10.1002/lrh2.70004","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>The Veterans Administration Evidence Synthesis Program (VA ESP) was established in 2007 to support evidence-based decisions about policies and programs in the VA healthcare system and has served as a critical component in the VA learning health system. Our objective was to document the growth of the ESP partner network and key policy and practice impacts of the program through a descriptive social network analysis and case series. We also aimed to provide insights into key features of some successful partnerships and the role evidence synthesis can play in supporting the improvement of the health system.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Using social network analysis and national ESP program data, we depicted the network at its initial, fully operational, and established phases of development to describe over a decade of ESP partnerships. We also queried program leadership and ESP partners about their collaboration experiences to generate a case series that demonstrates key examples of successful partnerships.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Of the 82 partner offices for whom the ESP has generated a report, over half (66%, <i>n</i> = 54) collaborated with the ESP on multiple projects, having received two or more synthesis products over time. Case studies revealed the impacts of ESP evidence synthesis on policy and practice, how our partners used the evidence generated from these processes, and the future work spawned from these efforts. While infrastructure and formal mechanisms can support these connections, relationships and partnering are instrumental facets as well.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Partnerships between the ESP and decision-makers can facilitate access to evidence and leadership's ongoing use of this evidence. The ESP has led to meaningful, evidence-driven communication between research and operational partners and has supported stakeholders in informing policy and system change.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635362","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}
Point-of-care trials, an approach to trial design that assesses medical product effectiveness while fully integrating research and care, represent a promising opportunity to generate practically relevant evidence efficiently for priority disease areas. However, this approach presents unique considerations for appropriate and ethical informed consent. As point-of-care trials evolve, it will be important to examine informed consent through the lens of their supporting technology, of the clinicians who are responsible for administering it, and of their broader regulatory environment. Steps should be taken to establish ethical standards for point-of-care trials that reflect and serve the best interests of patients while reducing administrative burden and complexity. Our commentary describes, overall and through the use of real-world examples, how this work is vital to ensuring a modern clinical trial enterprise that is patient-centered and equips patients to make fully informed decisions about their health care.
{"title":"Landscape of informed consent practices and challenges in point-of-care clinical trials","authors":"Caleigh Propes, Trevan Locke, Rachele Hendricks-Sturrup","doi":"10.1002/lrh2.10467","DOIUrl":"https://doi.org/10.1002/lrh2.10467","url":null,"abstract":"<p>Point-of-care trials, an approach to trial design that assesses medical product effectiveness while fully integrating research and care, represent a promising opportunity to generate practically relevant evidence efficiently for priority disease areas. However, this approach presents unique considerations for appropriate and ethical informed consent. As point-of-care trials evolve, it will be important to examine informed consent through the lens of their supporting technology, of the clinicians who are responsible for administering it, and of their broader regulatory environment. Steps should be taken to establish ethical standards for point-of-care trials that reflect and serve the best interests of patients while reducing administrative burden and complexity. Our commentary describes, overall and through the use of real-world examples, how this work is vital to ensuring a modern clinical trial enterprise that is patient-centered and equips patients to make fully informed decisions about their health care.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10467","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635448","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}
Louise A. Ellis, Georgia Fisher, Maree Saba, Genevieve Dammery, Kate Churruca, Samantha Spanos, Carolynn L. Smith, Bianca Forrester, Yvonne Zurynski, Jeffrey Braithwaite
Introduction
Since being introduced by the Institute of Medicine (IoM) in 2007, the learning health system (LHS) concept has gained traction as a promising solution for achieving systems-level healthcare transformation. This review of the LHS literature consolidates current understanding of LHS definitions, models, frameworks, and underlying theory, relative to their initial conceptualization by the IoM.
Methods
Three academic databases (PubMed, Embase, and Scopus) were searched for peer-reviewed literature between 2018 and 2023. Articles were included that explicitly focused on LHSs and described an LHS definition, model, or framework. Extracted article information included key article characteristics and article type; LHS definition(s) and their reference(s); components of LHS models or frameworks; and any reported theories underpinning LHS models or frameworks. Extracted data were examined using thematic and visual network analyses, and practical examples of how the domains of an LHS can be actualized in health settings were synthesized.
Results
The majority of the 226 included articles were nonempirical (47%) and originated from high-income countries (97%), with a significant portion from the United States (62%). A third of articles described an LHS implemented in a real-world setting (35%). A significant majority (82%) provided a definition of an LHS, with key concepts centering around “knowledge to practice,” “workplace culture,” and “informatics.” Over half of included articles described an LHS model or framework. From thematic deductive-inductive coding of 145 LHS models and frameworks, most identified aspects related to Science and Informatics (83%) and a Continuous Learning Culture (81%), with the most prevalent sub-domain being Supportive System Competencies (76%). Implementation science was the most used theory to underpin existing models and frameworks.
Conclusions
By dissecting LHS definitions, models, and frameworks, we present an integrated framework that can serve as a useful tool for LHS researchers, clinicians, and policymakers working to improve health system performance and outcomes.
{"title":"Charting progress in learning health systems: A systematic review of 5 years of definitions, models, and frameworks","authors":"Louise A. Ellis, Georgia Fisher, Maree Saba, Genevieve Dammery, Kate Churruca, Samantha Spanos, Carolynn L. Smith, Bianca Forrester, Yvonne Zurynski, Jeffrey Braithwaite","doi":"10.1002/lrh2.70006","DOIUrl":"https://doi.org/10.1002/lrh2.70006","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Since being introduced by the Institute of Medicine (IoM) in 2007, the learning health system (LHS) concept has gained traction as a promising solution for achieving systems-level healthcare transformation. This review of the LHS literature consolidates current understanding of LHS definitions, models, frameworks, and underlying theory, relative to their initial conceptualization by the IoM.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Three academic databases (PubMed, Embase, and Scopus) were searched for peer-reviewed literature between 2018 and 2023. Articles were included that explicitly focused on LHSs and described an LHS definition, model, or framework. Extracted article information included key article characteristics and article type; LHS definition(s) and their reference(s); components of LHS models or frameworks; and any reported theories underpinning LHS models or frameworks. Extracted data were examined using thematic and visual network analyses, and practical examples of how the domains of an LHS can be actualized in health settings were synthesized.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The majority of the 226 included articles were nonempirical (47%) and originated from high-income countries (97%), with a significant portion from the United States (62%). A third of articles described an LHS implemented in a real-world setting (35%). A significant majority (82%) provided a definition of an LHS, with key concepts centering around “knowledge to practice,” “workplace culture,” and “informatics.” Over half of included articles described an LHS model or framework. From thematic deductive-inductive coding of 145 LHS models and frameworks, most identified aspects related to Science and Informatics (83%) and a Continuous Learning Culture (81%), with the most prevalent sub-domain being Supportive System Competencies (76%). Implementation science was the most used theory to underpin existing models and frameworks.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>By dissecting LHS definitions, models, and frameworks, we present an integrated framework that can serve as a useful tool for LHS researchers, clinicians, and policymakers working to improve health system performance and outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635648","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}
Isabelle Meulenbroeks, Crisostomo Mercado, Rachel Urwin, Karla Seaman, Anna Kelly, Osman Qadri, Johanna Westbrook I.
Introduction
It is estimated that one in 30 patients experiences at least one preventable medication-related harm while receiving care. Cyclical medicine improvement interventions, where health systems continuously collect data, implement prescribing/dispensing interventions, review outcomes, and revise the intervention, have demonstrated health outcome improvements in a range of health care settings. This systematic review aimed to synthesize information on the characteristics and outcomes of cyclical medication management interventions.
Methods
Five databases were systematically searched for cyclical medication management interventions from 2000 to 2023. Studies were screened in a two-step process: title/abstract and full-text screening. All intervention, population, and outcome data were extracted. Intervention data were thematically categorized, and outcome data were categorized using Proctor's framework. The quality of data was assessed using the Mixed Methods Appraisal Tool (MMAT).
Results
Forty-five cyclical interventions from 46 publications were included. Most interventions studied cyclical medication management interventions in hospital settings (80%, n = 37) and utilized the plan-do-study-act framework to guide intervention design (64%, n = 29). Cyclical medication management interventions comprised multiple components (mean 2.4 components), with common components including practice standardization (n = 23), clinician feedback (n = 20), and clinician education (n = 18). One hundred and twenty-two outcome measures were extracted and categorized as implementation (n = 77), service (n = 41), and patient outcomes (n = 4). The quality of many publications was poor; 8 publications could not be scored or scored 0 on the MMAT, and the remaining publications scored on average (mean) 60% on the MMAT.
Conclusion
Cyclical medication management interventions show weak evidence that they can be implemented successfully and improve health system and service outcomes. Significant further research and health system structuring are required to address the quality issues surrounding cyclical medication management implementation and reporting.
{"title":"Cyclical medication management interventions in health care settings: A systematic review","authors":"Isabelle Meulenbroeks, Crisostomo Mercado, Rachel Urwin, Karla Seaman, Anna Kelly, Osman Qadri, Johanna Westbrook I.","doi":"10.1002/lrh2.70005","DOIUrl":"https://doi.org/10.1002/lrh2.70005","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>It is estimated that one in 30 patients experiences at least one preventable medication-related harm while receiving care. Cyclical medicine improvement interventions, where health systems continuously collect data, implement prescribing/dispensing interventions, review outcomes, and revise the intervention, have demonstrated health outcome improvements in a range of health care settings. This systematic review aimed to synthesize information on the characteristics and outcomes of cyclical medication management interventions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Five databases were systematically searched for cyclical medication management interventions from 2000 to 2023. Studies were screened in a two-step process: title/abstract and full-text screening. All intervention, population, and outcome data were extracted. Intervention data were thematically categorized, and outcome data were categorized using Proctor's framework. The quality of data was assessed using the Mixed Methods Appraisal Tool (MMAT).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Forty-five cyclical interventions from 46 publications were included. Most interventions studied cyclical medication management interventions in hospital settings (80%, <i>n</i> = 37) and utilized the plan-do-study-act framework to guide intervention design (64%, <i>n</i> = 29). Cyclical medication management interventions comprised multiple components (mean 2.4 components), with common components including practice standardization (<i>n</i> = 23), clinician feedback (<i>n</i> = 20), and clinician education (<i>n</i> = 18). One hundred and twenty-two outcome measures were extracted and categorized as implementation (<i>n</i> = 77), service (<i>n</i> = 41), and patient outcomes (<i>n</i> = 4). The quality of many publications was poor; 8 publications could not be scored or scored 0 on the MMAT, and the remaining publications scored on average (mean) 60% on the MMAT.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Cyclical medication management interventions show weak evidence that they can be implemented successfully and improve health system and service outcomes. Significant further research and health system structuring are required to address the quality issues surrounding cyclical medication management implementation and reporting.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635639","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 <i>Advances in Learning Health System Sciences</i> (<i>AiLHSS) Conference</i> is an annual event that addresses pressing and emerging Learning Health System (LHS) topics while fostering collaboration among researchers advancing the field presented by the University of Minnesota (UMN) Center for Learning Health System Sciences (CLHSS), a collaboration between the Medical School and School of Public Health at the UMN. The 2024 conference was held on September 9–10 at Coffman Memorial Union at UMN in Minneapolis, MN. This year's conference featured 219 attendees including clinicians, researchers, policymakers, and trainees, with local, national, and international representation from professional organizations, research institutes, healthcare organizations, and universities serving the needs of diverse populations such as children, the elderly, rural communities, and veterans. The agenda was composed of keynote presentations, scientific forums, workshops, and poster sessions all designed to foster collaboration and innovation in the field of LHS. Throughout the conference, attendees had the opportunity to engage in various networking sessions. This year's agenda highlighted the importance of collaboration and continuous learning in improving healthcare delivery, with a focus on practical tools, real-world examples, and the integration of patient and community perspectives.</p><p>On the first day, September 9, the conference began with coffee and networking, followed by welcome and opening remarks from Genevieve Melton-Meaux, MD, PhD (Director, CLHSS), and Jakub Tolar, MD, PhD (Dean, UMN Medical School). The morning sessions included a keynote by Sarah Greene, MPH, from the National Academy of Medicine discussing the complexities and opportunities in building and sustaining learning health systems. This was followed by a spotlight session and a panel on active data sharing collaboratives. The afternoon featured scientific forums on topics such as organ donation data, opioid substance use treatment, and cardiac imaging in stroke evaluation, as well as a workshop on partnering with patients to improve study design and outcomes. The day ended with a poster session which included networking opportunities.</p><p>The second day, September 10, started with coffee and networking, followed by opening remarks from Timothy Beebe, PhD (Deputy Director, CLHSS), and a keynote by Sara Singer, PhD, MBA, from Stanford University on the importance of learning in health systems. The morning sessions included evidence synthesis case studies and a spotlight session on implementation science reporting and adherence challenges. The conference concluded with a keynote by Angela D. Thomas, DrPH, MPH, MBA, from MedStar Health discussing the realities of being a learning health system.</p><p>This article features a curated collection of abstracts from spotlight sessions, oral presentations, and the highest-scored posters, all of which were recognized for their innovative
学习卫生系统科学进展（AiLHSS）会议是一年一度的会议，旨在解决紧迫和新兴的学习卫生系统（LHS）主题，同时促进研究人员之间的合作，推进明尼苏达大学（UMN）学习卫生系统科学中心（CLHSS）提出的领域，这是明尼苏达大学医学院和公共卫生学院之间的合作。2024年会议于9月9日至10日在明尼苏达州明尼阿波利斯市UMN的科夫曼纪念联盟举行。今年的会议共有219名与会者，包括临床医生、研究人员、政策制定者和学员，以及来自当地、国家和国际的专业组织、研究机构、卫生保健组织和大学的代表，他们服务于儿童、老年人、农村社区和退伍军人等不同人群的需求。会议议程由主题演讲、科学论坛、研讨会和海报会议组成，旨在促进LHS领域的合作和创新。在整个会议期间，与会者有机会参与各种网络会议。今年的议程强调了协作和持续学习在改善医疗保健服务方面的重要性，重点是实用工具、现实世界的例子以及患者和社区观点的整合。第一天，9月9日，会议以咖啡和网络开始，随后是Genevieve Melton-Meaux医学博士（CLHSS主任）和Jakub Tolar医学博士（UMN医学院院长）的欢迎和开幕词。上午的会议包括来自美国国家医学院的公共卫生硕士Sarah Greene的主题演讲，讨论了建立和维持学习型卫生系统的复杂性和机遇。随后是一场聚焦会议和一个关于活跃数据共享协作的小组讨论。下午的科学论坛主题包括器官捐赠数据、阿片类药物使用治疗和卒中评估中的心脏成像，以及与患者合作改善研究设计和结果的研讨会。这一天以海报会议结束，其中包括交流机会。第二天，即9月10日，以咖啡和社交开始，随后由Timothy Beebe博士（CLHSS副主任）致开幕致辞，并由斯坦福大学MBA博士Sara Singer就卫生系统中学习的重要性发表主旨演讲。上午的会议包括证据综合案例研究和关于实施科学报告和遵守挑战的重点会议。会议结束时，MedStar Health的博士、公共卫生硕士、工商管理硕士Angela D. Thomas发表了主题演讲，讨论了学习型卫生系统的现实。本文精选了焦点会议、口头报告和得分最高的海报的摘要，所有这些都因其对LHS研究和实践的创新贡献而得到认可。这些演讲强调了基于证据的干预措施的共同创造、现实世界数据的利用和系统思考，以改善患者的护理和结果。通过关注LHS的核心原则，如迭代学习、利益相关者参与和数据驱动决策，他们为解决各种医疗和公共卫生挑战提供了实用模型。
{"title":"2024 Advances in Learning Health System Sciences (AiLHSS) Conference: Abstracts","authors":"","doi":"10.1002/lrh2.70000","DOIUrl":"https://doi.org/10.1002/lrh2.70000","url":null,"abstract":"<p>The <i>Advances in Learning Health System Sciences</i> (<i>AiLHSS) Conference</i> is an annual event that addresses pressing and emerging Learning Health System (LHS) topics while fostering collaboration among researchers advancing the field presented by the University of Minnesota (UMN) Center for Learning Health System Sciences (CLHSS), a collaboration between the Medical School and School of Public Health at the UMN. The 2024 conference was held on September 9–10 at Coffman Memorial Union at UMN in Minneapolis, MN. This year's conference featured 219 attendees including clinicians, researchers, policymakers, and trainees, with local, national, and international representation from professional organizations, research institutes, healthcare organizations, and universities serving the needs of diverse populations such as children, the elderly, rural communities, and veterans. The agenda was composed of keynote presentations, scientific forums, workshops, and poster sessions all designed to foster collaboration and innovation in the field of LHS. Throughout the conference, attendees had the opportunity to engage in various networking sessions. This year's agenda highlighted the importance of collaboration and continuous learning in improving healthcare delivery, with a focus on practical tools, real-world examples, and the integration of patient and community perspectives.</p><p>On the first day, September 9, the conference began with coffee and networking, followed by welcome and opening remarks from Genevieve Melton-Meaux, MD, PhD (Director, CLHSS), and Jakub Tolar, MD, PhD (Dean, UMN Medical School). The morning sessions included a keynote by Sarah Greene, MPH, from the National Academy of Medicine discussing the complexities and opportunities in building and sustaining learning health systems. This was followed by a spotlight session and a panel on active data sharing collaboratives. The afternoon featured scientific forums on topics such as organ donation data, opioid substance use treatment, and cardiac imaging in stroke evaluation, as well as a workshop on partnering with patients to improve study design and outcomes. The day ended with a poster session which included networking opportunities.</p><p>The second day, September 10, started with coffee and networking, followed by opening remarks from Timothy Beebe, PhD (Deputy Director, CLHSS), and a keynote by Sara Singer, PhD, MBA, from Stanford University on the importance of learning in health systems. The morning sessions included evidence synthesis case studies and a spotlight session on implementation science reporting and adherence challenges. The conference concluded with a keynote by Angela D. Thomas, DrPH, MPH, MBA, from MedStar Health discussing the realities of being a learning health system.</p><p>This article features a curated collection of abstracts from spotlight sessions, oral presentations, and the highest-scored posters, all of which were recognized for their innovative","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836471","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}
This study aims to analyze hospitals' adoption and integration of electronic health record (EHR) metadata into their management processes.
Design
The study compares the rates of EHR metadata utilization across various hospitals over time. Hospitals' self-reported use of EHR metadata is drawn from the AHA-IT Supplements from 2018 to 2020. An analysis of metadata utilization by EHR vendors is also provided.
Method
The study uses Bass diffusion modeling to estimate EHR adoption parameters by fitting adoption rate data from 2018 to 2020, using Excel Solver to minimize prediction errors. The estimated internal and external influence coefficients reveal which factor primarily drives adoption, while the diffusion model enables future projection of tipping point and adoption level.
Results
Analysis of EHR metadata utilization rates from 2018 to 2020 find a significant trend towards the integration of this data into hospital management practices. Among health systems responding to the items of interest, 69% of them are already using EHR metadata, and it is projected that nearly all will do so by 2035. Further, metadata use varied significantly depending on the vendor.
Discussion
The study underscores that hospital managers' intrinsic motivations, rather than external demands, are driving EHR metadata. As innovations with greater intrinsic appeal spread more rapidly and have greater staying power, EHR metadata use will continue to grow. These trends are indicative of the growing importance of EHR metadata in management decision-making, clinical quality improvement, and optimizing workforce efficiency.
Conclusions
EHR metadata holds great promise as a managerial and health service research source. The tools' utilities would be enhanced if EHR vendors created uniform metrics.
{"title":"Trends in electronic health record metadata use for management purposes","authors":"Nuo Xu, Ishwar Badwaik, Gunwoo Lee, Eric W. Ford","doi":"10.1002/lrh2.70001","DOIUrl":"https://doi.org/10.1002/lrh2.70001","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This study aims to analyze hospitals' adoption and integration of electronic health record (EHR) metadata into their management processes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Design</h3>\u0000 \u0000 <p>The study compares the rates of EHR metadata utilization across various hospitals over time. Hospitals' self-reported use of EHR metadata is drawn from the AHA-IT Supplements from 2018 to 2020. An analysis of metadata utilization by EHR vendors is also provided.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>The study uses Bass diffusion modeling to estimate EHR adoption parameters by fitting adoption rate data from 2018 to 2020, using Excel Solver to minimize prediction errors. The estimated internal and external influence coefficients reveal which factor primarily drives adoption, while the diffusion model enables future projection of tipping point and adoption level.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Analysis of EHR metadata utilization rates from 2018 to 2020 find a significant trend towards the integration of this data into hospital management practices. Among health systems responding to the items of interest, 69% of them are already using EHR metadata, and it is projected that nearly all will do so by 2035. Further, metadata use varied significantly depending on the vendor.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>The study underscores that hospital managers' intrinsic motivations, rather than external demands, are driving EHR metadata. As innovations with greater intrinsic appeal spread more rapidly and have greater staying power, EHR metadata use will continue to grow. These trends are indicative of the growing importance of EHR metadata in management decision-making, clinical quality improvement, and optimizing workforce efficiency.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>EHR metadata holds great promise as a managerial and health service research source. The tools' utilities would be enhanced if EHR vendors created uniform metrics.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635281","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}
Diane Holder, Donna Keyser, Jane Kogan, Matthew O. Hurford, John Lovelace, James Schuster
Introduction
To accelerate transformation toward value-based, equitable care, health systems need a viable approach for engaging and aligning multiple stakeholders to promote innovation. Building and sustaining learning communities offers one possible solution.
Methods
We describe how one learning community has leveraged the collective strengths, assets, and expertise of multiple stakeholders to improve care value for subpopulations who experience low-quality, high-cost, and/or inequitable health outcomes.
Results
By providing critical infrastructure and support, UPMC's Learning Community was able to (1) accelerate adoption of risk-based payment models that promote shared accountability among providers, payers, and families/caregivers for the total costs of care of children and adolescents with medically complex conditions, (2) drive widespread practice change for improving physical and mental wellness for individuals with serious mental illness, and (3) increase access to evidence-based treatment and improve outcomes for individuals with opioid and substance use disorders.
Conclusions
Learning communities can serve as important catalyzers for the payment, practice, and service delivery innovation necessary to achieve a high-value, equitable health system.
{"title":"Aligning forces to accelerate healthcare transformation: Insights from the UPMC learning community","authors":"Diane Holder, Donna Keyser, Jane Kogan, Matthew O. Hurford, John Lovelace, James Schuster","doi":"10.1002/lrh2.10477","DOIUrl":"https://doi.org/10.1002/lrh2.10477","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>To accelerate transformation toward value-based, equitable care, health systems need a viable approach for engaging and aligning multiple stakeholders to promote innovation. Building and sustaining learning communities offers one possible solution.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We describe how one learning community has leveraged the collective strengths, assets, and expertise of multiple stakeholders to improve care value for subpopulations who experience low-quality, high-cost, and/or inequitable health outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>By providing critical infrastructure and support, UPMC's Learning Community was able to (1) accelerate adoption of risk-based payment models that promote shared accountability among providers, payers, and families/caregivers for the total costs of care of children and adolescents with medically complex conditions, (2) drive widespread practice change for improving physical and mental wellness for individuals with serious mental illness, and (3) increase access to evidence-based treatment and improve outcomes for individuals with opioid and substance use disorders.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Learning communities can serve as important catalyzers for the payment, practice, and service delivery innovation necessary to achieve a high-value, equitable health system.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10477","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635299","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}
Patrick Lewicki, Alim Swarray-Deen, Cheryl A. Moyer
Introduction
Learning health system (LHS) development has been described in the highest resource settings, which could suggest that resources are a precondition to LHS formation.
Method
We reviewed literature surrounding LHSs in low-resource contexts and used this to inform an argument that LHS activity is critically important in these circumstances.
Results
We focus on three key points. First, when resources are scarce, they should not be squandered. Second, local knowledge artifacts have advantages. Finally, LHS emphasis on lasting sociotechnical infrastructure addresses sustainability concerns.
Conclusion
We believe LHS formation and activity is more important in low-resource contexts than in their higher resource counterparts. Less path dependence in many low-resource contexts forecasts that LHSs may see their greatest success there.
{"title":"The learning health system imperative in low-resource contexts","authors":"Patrick Lewicki, Alim Swarray-Deen, Cheryl A. Moyer","doi":"10.1002/lrh2.70002","DOIUrl":"https://doi.org/10.1002/lrh2.70002","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Learning health system (LHS) development has been described in the highest resource settings, which could suggest that resources are a precondition to LHS formation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>We reviewed literature surrounding LHSs in low-resource contexts and used this to inform an argument that LHS activity is critically important in these circumstances.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We focus on three key points. First, when resources are scarce, they should not be squandered. Second, local knowledge artifacts have advantages. Finally, LHS emphasis on lasting sociotechnical infrastructure addresses sustainability concerns.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>We believe LHS formation and activity is more important in low-resource contexts than in their higher resource counterparts. Less path dependence in many low-resource contexts forecasts that LHSs may see their greatest success there.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635014","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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