Stephanie R. Morain, Matthew W. Semler, Jonathan D. Casey
Introduction
Pragmatic clinical trials (PCTs) are frequently embedded within settings in which patients receive their routine clinical care. The health care institutions that deliver routine clinical care must therefore decide how they prioritize PCTs in comparison to other institutional priorities. Yet no formal guidance yet exists to support health care institutions' assessment and prioritization of PCTs.
Methods
Drawing on a critical review and synthesis of published literature and experience in PCTs, we developed a framework by which health care institutions can assess the appropriateness and feasibility of a given PCT within their system.
Results
We propose healthcare institutions assess the appropriateness of a given PCT through three lines of inquiry: (1) importance and timeliness of the research question, (2) feasibility of the study within the respective institution, and (3) institutional willingness to act on the study results.
Conclusion
The proposed framework may support decision-making about whether to initiate a given PCT and also how to do so in a way that supports both research and operational priorities.
{"title":"A framework for health systems to assess and prioritize participation in pragmatic clinical trials","authors":"Stephanie R. Morain, Matthew W. Semler, Jonathan D. Casey","doi":"10.1002/lrh2.70016","DOIUrl":"https://doi.org/10.1002/lrh2.70016","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Pragmatic clinical trials (PCTs) are frequently embedded within settings in which patients receive their routine clinical care. The health care institutions that deliver routine clinical care must therefore decide how they prioritize PCTs in comparison to other institutional priorities. Yet no formal guidance yet exists to support health care institutions' assessment and prioritization of PCTs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Drawing on a critical review and synthesis of published literature and experience in PCTs, we developed a framework by which health care institutions can assess the appropriateness and feasibility of a given PCT within their system.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We propose healthcare institutions assess the appropriateness of a given PCT through three lines of inquiry: (1) importance and timeliness of the research question, (2) feasibility of the study within the respective institution, and (3) institutional willingness to act on the study results.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The proposed framework may support decision-making about whether to initiate a given PCT and also how to do so in a way that supports both research and operational priorities.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145375147","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}
Bethany M. Kwan, Patrick Hosokawa, Daniel Resnick-Ault, Sean S. Michael, W. Gannon Sungar, Matthew Mendes, Kelly Bookman
Introduction
The application of implementation science (IS) frameworks to evaluate quality process improvement initiatives in an academic emergency department (ED) setting offers a promising approach for learning health systems (LHS). This report describes the University of Colorado Department of Emergency Medicine's LHS partnership among clinical operations, data analytics, and IS experts to evaluate a novel shared note workflow.
Methods
The University of Colorado Health (UCH) ED, staffed with scribes, advanced practice providers, and physicians, implemented a shared note workflow in summer 2022. This workflow involved all parties in a clinical encounter using a single note template, unlike previous separate notes. An IS expert guided the evaluation using the implementation of change model and the theoretical domains framework, with surveys and Epic electronic health record (EHR) encounter data. Analysis included descriptive statistics and regression analysis, comparing note completion rates pre (summer 2021) and post (summer 2022 and 2023) shared notes.
Results
Among 146 survey respondents, knowledge [M (SD) = 6.0 (0.9)] and confidence [5.5 (1.3)] following the new workflow were high (1–7 scale). Acceptability [M (SD) = 4.42 (0.80)], appropriateness [4.48 (0.70)], and feasibility [4.5 (0.61)] were also high (1–5 scale). EHR data showed note completion during the shift increased to 48.3% in 2022 from 32.2% in 2021, a 50% improvement (OR (95% CI) = 2.34 (2.28–2.40), p < 0.0001). By summer 2023, note completion attenuated to 41.3%, still significantly higher than pre-shared note workflow (OR (95% CI) = 1.96 (1.90–2.01), p < 0.0001).
Conclusion
This IS-enhanced LHS evaluation of a shared note workflow in an academic ED demonstrated high satisfaction and a positive impact on note completion during shifts. Timely note completion is crucial for reducing clinician burnout and providing high-quality care. IS methods provided data-driven insights to justify the sustainment of the shared note workflow to organizational leaders.
{"title":"A learning health systems approach to implementation and evaluation of an academic emergency department single shared note workflow","authors":"Bethany M. Kwan, Patrick Hosokawa, Daniel Resnick-Ault, Sean S. Michael, W. Gannon Sungar, Matthew Mendes, Kelly Bookman","doi":"10.1002/lrh2.70017","DOIUrl":"https://doi.org/10.1002/lrh2.70017","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>The application of implementation science (IS) frameworks to evaluate quality process improvement initiatives in an academic emergency department (ED) setting offers a promising approach for learning health systems (LHS). This report describes the University of Colorado Department of Emergency Medicine's LHS partnership among clinical operations, data analytics, and IS experts to evaluate a novel shared note workflow.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The University of Colorado Health (UCH) ED, staffed with scribes, advanced practice providers, and physicians, implemented a shared note workflow in summer 2022. This workflow involved all parties in a clinical encounter using a single note template, unlike previous separate notes. An IS expert guided the evaluation using the implementation of change model and the theoretical domains framework, with surveys and Epic electronic health record (EHR) encounter data. Analysis included descriptive statistics and regression analysis, comparing note completion rates pre (summer 2021) and post (summer 2022 and 2023) shared notes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Among 146 survey respondents, knowledge [<i>M</i> (SD) = 6.0 (0.9)] and confidence [5.5 (1.3)] following the new workflow were high (1–7 scale). Acceptability [<i>M</i> (SD) = 4.42 (0.80)], appropriateness [4.48 (0.70)], and feasibility [4.5 (0.61)] were also high (1–5 scale). EHR data showed note completion during the shift increased to 48.3% in 2022 from 32.2% in 2021, a 50% improvement (OR (95% CI) = 2.34 (2.28–2.40), <i>p</i> < 0.0001). By summer 2023, note completion attenuated to 41.3%, still significantly higher than pre-shared note workflow (OR (95% CI) = 1.96 (1.90–2.01), <i>p</i> < 0.0001).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This IS-enhanced LHS evaluation of a shared note workflow in an academic ED demonstrated high satisfaction and a positive impact on note completion during shifts. Timely note completion is crucial for reducing clinician burnout and providing high-quality care. IS methods provided data-driven insights to justify the sustainment of the shared note workflow to organizational leaders.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145375261","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}
C. Ann Vitous, Kaylee W. Burgan, John P. Donnelly, Nicholas W. Bowersox, Linda M. Kawentel
Introduction
Embedding research mechanisms within Learning Health Systems can improve healthcare performance by informing program development and expansion, supporting strategic planning, and demonstrating value. The role of the investigator is crucial in partnered funding mechanisms, and there is a growing need to better understand how investigators operate in this space. This evaluation assessed the impacts, challenges, and lessons learned by investigators funded through an embedded research program.
Methods
This was an outcome evaluation within the Department of Veterans Affairs healthcare system and followed an adapted version of the Centers for Disease Control and Prevention evaluation framework. Three data sources were used to generate information: a descriptive analysis of close-out reports, a comprehensive search of research databanks, and an online survey focused on high-priority areas. Participants were investigators whose projects took place during the FY2017–2021 funding period. During the funding period, 25 unique investigators completed projects.
Results
Investigators indicated that the embedded research program had significant, meaningful impacts on their professional development, including generating tangible products that advanced their professional goals and more subtle impacts, such as increasing knowledge and experience, increasing multidisciplinary collaboration, and creating opportunities for professional development. Investigators also reflected on challenges, including issues with the data, logistical concerns, and disruptions due to COVID-19. Investigators identified lessons learned for future collaboration efforts, including the need for feasible and appropriate goals, as well as dedicated and trained support staff.
Conclusions
The insights from this evaluation provide understanding on the impacts, challenges, and lessons learned from the experiences of investigators participating in embedded research mechanisms. The impacts illuminated explicit ways in which investigators professionally benefited from this program, as well as challenges and lessons learned that offer insights into how future programs can be tailored. These findings offer ways to improve practices in implementing LHS in other large healthcare organizations.
{"title":"Funding model for embedded research: Impacts, challenges, and lessons learned for investigators","authors":"C. Ann Vitous, Kaylee W. Burgan, John P. Donnelly, Nicholas W. Bowersox, Linda M. Kawentel","doi":"10.1002/lrh2.70015","DOIUrl":"https://doi.org/10.1002/lrh2.70015","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Embedding research mechanisms within Learning Health Systems can improve healthcare performance by informing program development and expansion, supporting strategic planning, and demonstrating value. The role of the investigator is crucial in partnered funding mechanisms, and there is a growing need to better understand how investigators operate in this space. This evaluation assessed the impacts, challenges, and lessons learned by investigators funded through an embedded research program.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This was an outcome evaluation within the Department of Veterans Affairs healthcare system and followed an adapted version of the Centers for Disease Control and Prevention evaluation framework. Three data sources were used to generate information: a descriptive analysis of close-out reports, a comprehensive search of research databanks, and an online survey focused on high-priority areas. Participants were investigators whose projects took place during the FY2017–2021 funding period. During the funding period, 25 unique investigators completed projects.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Investigators indicated that the embedded research program had significant, meaningful impacts on their professional development, including generating tangible products that advanced their professional goals and more subtle impacts, such as increasing knowledge and experience, increasing multidisciplinary collaboration, and creating opportunities for professional development. Investigators also reflected on challenges, including issues with the data, logistical concerns, and disruptions due to COVID-19. Investigators identified lessons learned for future collaboration efforts, including the need for feasible and appropriate goals, as well as dedicated and trained support staff.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The insights from this evaluation provide understanding on the impacts, challenges, and lessons learned from the experiences of investigators participating in embedded research mechanisms. The impacts illuminated explicit ways in which investigators professionally benefited from this program, as well as challenges and lessons learned that offer insights into how future programs can be tailored. These findings offer ways to improve practices in implementing LHS in other large healthcare organizations.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145375312","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}
David Alexander Goodson, Brittany Garcia, Michael Hogarth, Shin-Ping Tu
Introduction
Physician burnout persists in the American healthcare system. In part, this burnout is believed to be driven by the Electronic Health Record (EHR) and its fraught role in the clinical work of physicians. Artificial intelligence (AI)-enabled healthcare technologies are often promoted on the basis of their promise to reduce burnout by introducing efficiencies into clinical work, particularly related to EHR utilization and documentation. Where documentation is perceived as the problem, AI scribes are offered as the solution.
Methods
This essay looks closely at existing studies of AI scribes in clinical context and draws upon experience and understanding of healthcare delivery and the EHR to anticipate how AI may related to provider burnout.
Results
We find that it is premature to assert that AI tools will reduce physician burnout. Considering the integration of AI scribes into Learning Health Systems healthcare delivery becomes a starting point for understanding the challenges faced in safely adopting AI tools more generally, with attention to the healthcare workforce and patients.
Conclusion
It is not a foregone conclusion that AI-enabled healthcare technologies, in their current state and application, will lead to improved healthcare delivery and reduced burnout. Instead, this is an open question that demands rigorous evaluation and high standards of evidence before we restructure the work of physicians and redefine the care of our patients.
{"title":"Artificial intelligence and physician burnout: A productivity paradox","authors":"David Alexander Goodson, Brittany Garcia, Michael Hogarth, Shin-Ping Tu","doi":"10.1002/lrh2.70013","DOIUrl":"https://doi.org/10.1002/lrh2.70013","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Physician burnout persists in the American healthcare system. In part, this burnout is believed to be driven by the Electronic Health Record (EHR) and its fraught role in the clinical work of physicians. Artificial intelligence (AI)-enabled healthcare technologies are often promoted on the basis of their promise to reduce burnout by introducing efficiencies into clinical work, particularly related to EHR utilization and documentation. Where documentation is perceived as the problem, AI scribes are offered as the solution.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This essay looks closely at existing studies of AI scribes in clinical context and draws upon experience and understanding of healthcare delivery and the EHR to anticipate how AI may related to provider burnout.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We find that it is premature to assert that AI tools will reduce physician burnout. Considering the integration of AI scribes into Learning Health Systems healthcare delivery becomes a starting point for understanding the challenges faced in safely adopting AI tools more generally, with attention to the healthcare workforce and patients.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>It is not a foregone conclusion that AI-enabled healthcare technologies, in their current state and application, will lead to improved healthcare delivery and reduced burnout. Instead, this is an open question that demands rigorous evaluation and high standards of evidence before we restructure the work of physicians and redefine the care of our patients.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145375024","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}
Jaifred Christian F. Lopez, Sallie Allgood, Kate Sheahan, Brandolyn White, M. Amy Kirshner, Suzanne Shirley, Madison Coffey, Amanda Milo, Sarah L. Cutrona, Laura Damschroder, Gemmae M. Fix, Andrea L. Nevedal, Caitlin M. Reardon, Marilla A. Opra Widerquist, Maria Arasim, Allen L. Gifford, Kathryn DeLaughter, George L. Jackson
Background
The U.S. Veterans Health Administration (VHA) formed an Innovation Ecosystem that develops and disseminates innovative practices to enhance Veterans' health. Support of senior leadership and their perception of the innovation process is key to the Ecosystem's success. We aimed to elicit insights on (1) how national VHA program office leaders define innovation, and (2) important considerations in facilitating the adoption of innovations.
Methods
As part of a quality improvement initiative, we conducted 19 semi-structured interviews via teleconference. Interviews involved 4 administration offices, 7 clinical and population health program offices, and 8 policy and quality improvement offices; 12 of these offices reported experience working with the Innovation Ecosystem. Responses were audio recorded, transcribed, and analyzed using constructs from the Consolidated Framework for Implementation Research.
Results
Participants generally agreed that innovation within VHA is defined by evidence-based development and implementation of interventions that improve response to Veterans' needs. Considerations in facilitating innovations include: (1) implementation climate that promotes network-building, open communication, and well-executed planning processes; (2) implementation infrastructures that enable engagement with key players and augment existing resources; and (3) innovation evidence strength and responsiveness to patient needs. Individuals working in policy-related offices were more likely to identify complexity, leadership engagement, culture and available resources as factors in choosing innovations to adopt. Individuals who reported experience of working with the Ecosystem emphasized the importance of intra-organizational networks and a favorable implementation climate, while those without experience noted the importance of working with external change agents. CFIR ‘inner setting’ constructs were seen in responses across all categories; meanwhile, emerging constructs highlight how innovation should be balanced by the reality of operations.
Conclusions
Among national VHA program office leaders, innovation is pursued to improve Veterans' health. Resources, networks, culture, and processes are considered important factors among program office leaders to support and encourage innovation.
{"title":"How senior leaders support innovations in large learning health systems: Insights from United States Veterans Health Administration national program office leaders","authors":"Jaifred Christian F. Lopez, Sallie Allgood, Kate Sheahan, Brandolyn White, M. Amy Kirshner, Suzanne Shirley, Madison Coffey, Amanda Milo, Sarah L. Cutrona, Laura Damschroder, Gemmae M. Fix, Andrea L. Nevedal, Caitlin M. Reardon, Marilla A. Opra Widerquist, Maria Arasim, Allen L. Gifford, Kathryn DeLaughter, George L. Jackson","doi":"10.1002/lrh2.70012","DOIUrl":"https://doi.org/10.1002/lrh2.70012","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The U.S. Veterans Health Administration (VHA) formed an Innovation Ecosystem that develops and disseminates innovative practices to enhance Veterans' health. Support of senior leadership and their perception of the innovation process is key to the Ecosystem's success. We aimed to elicit insights on (1) how national VHA program office leaders define innovation, and (2) important considerations in facilitating the adoption of innovations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>As part of a quality improvement initiative, we conducted 19 semi-structured interviews via teleconference. Interviews involved 4 administration offices, 7 clinical and population health program offices, and 8 policy and quality improvement offices; 12 of these offices reported experience working with the Innovation Ecosystem. Responses were audio recorded, transcribed, and analyzed using constructs from the Consolidated Framework for Implementation Research.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Participants generally agreed that innovation within VHA is defined by evidence-based development and implementation of interventions that improve response to Veterans' needs. Considerations in facilitating innovations include: (1) implementation climate that promotes network-building, open communication, and well-executed planning processes; (2) implementation infrastructures that enable engagement with key players and augment existing resources; and (3) innovation evidence strength and responsiveness to patient needs. Individuals working in policy-related offices were more likely to identify complexity, leadership engagement, culture and available resources as factors in choosing innovations to adopt. Individuals who reported experience of working with the Ecosystem emphasized the importance of intra-organizational networks and a favorable implementation climate, while those without experience noted the importance of working with external change agents. CFIR ‘inner setting’ constructs were seen in responses across all categories; meanwhile, emerging constructs highlight how innovation should be balanced by the reality of operations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Among national VHA program office leaders, innovation is pursued to improve Veterans' health. Resources, networks, culture, and processes are considered important factors among program office leaders to support and encourage innovation.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635208","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}
Patricia Pearl O'Rourke, Joseph Ali, Judith Carrithers, David Magnus, Benjamin S. Wilfond, Sheana Bull, Laura M. Dember, Gail D'Onofrio, Julie Goldman, P. Michael Ho, Edward R. Melnick, Karen L. Staman, James A. Tulsky, Miguel A. Vazquez, Angelo Volandes, David Wendler
Introduction
Pragmatic clinical trials conducted in the context of routine care frequently satisfy the regulatory criteria for a waiver of research consent. When they do, investigators and Institutional Review Boards might assume that there is no reason to communicate any information regarding the study to participants. Yet, this approach ignores the possibility that there may be value in providing information to participants, even when the study does not pose significant risks and researchers are not obtaining their consent.
Methods
Members of the NIH Collaboratory Ethics and Regulatory Core working group used ethical analysis to determine whether there are reasons to provide information to research participants, other than notifying them of significant risks or obtaining their consent. Study team members then provided examples of trials which illustrate the feasibility and different options for providing information to participants in the context of trials conducted with a waiver of research consent.
Results
Communicating information to participants can promote one or more of six goals: respect for persons, participant understanding of the research, participant understanding of their contributions, participant ability to voice any concerns, participant engagement, and trust and trustworthiness. Providing information can also raise potential concerns about feasibility and cost, which need to be balanced against these reasons to inform participants. Depending on the study, a variety of methods can be used to communicate information; for example, letters, email, flyers, posters, as well as brief conversations with clinicians.
Conclusion
Even when researchers are not obtaining participants' consent, communicating information can promote one or more of six important goals. Providing information to participants should thus be the default for trials conducted under a waiver of research consent.
{"title":"Disentangling informing participants from obtaining their consent","authors":"Patricia Pearl O'Rourke, Joseph Ali, Judith Carrithers, David Magnus, Benjamin S. Wilfond, Sheana Bull, Laura M. Dember, Gail D'Onofrio, Julie Goldman, P. Michael Ho, Edward R. Melnick, Karen L. Staman, James A. Tulsky, Miguel A. Vazquez, Angelo Volandes, David Wendler","doi":"10.1002/lrh2.70014","DOIUrl":"https://doi.org/10.1002/lrh2.70014","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Pragmatic clinical trials conducted in the context of routine care frequently satisfy the regulatory criteria for a waiver of research consent. When they do, investigators and Institutional Review Boards might assume that there is no reason to communicate any information regarding the study to participants. Yet, this approach ignores the possibility that there may be value in providing information to participants, even when the study does not pose significant risks and researchers are not obtaining their consent.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Members of the NIH Collaboratory Ethics and Regulatory Core working group used ethical analysis to determine whether there are reasons to provide information to research participants, other than notifying them of significant risks or obtaining their consent. Study team members then provided examples of trials which illustrate the feasibility and different options for providing information to participants in the context of trials conducted with a waiver of research consent.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Communicating information to participants can promote one or more of six goals: respect for persons, participant understanding of the research, participant understanding of their contributions, participant ability to voice any concerns, participant engagement, and trust and trustworthiness. Providing information can also raise potential concerns about feasibility and cost, which need to be balanced against these reasons to inform participants. Depending on the study, a variety of methods can be used to communicate information; for example, letters, email, flyers, posters, as well as brief conversations with clinicians.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Even when researchers are not obtaining participants' consent, communicating information can promote one or more of six important goals. Providing information to participants should thus be the default for trials conducted under a waiver of research consent.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145375279","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}
Learning Health Systems (LHS) are an increasingly common element of health policy reform efforts in a number of jurisdictions. There is little disagreement around the LHS vision, and early adopters provide some development guidance. Despite the attractiveness of the LHS vision, progress on adoption by systems remains slow. In this commentary, we consider one potential reason, namely politics, or the ways in which government bodies, interest groups, and political ideas shape structures and policies. LHS can change the ways that health systems work and interact with payors and populations and thereby create political challenges. The need for upfront new investment to build capacity for LHS activities, the creation of new partnerships or collaborations, increased transparency, and the direct engagement of populations can all create political risks and subsequent barriers. With a broad population health focus that extends across typical political cycles, politics may create an even greater barrier. We suggest that building strong engagement, clear and transparent accountabilities, communities of practice and other vehicles to promote data sharing and transparency, and careful attention to risk management may all help reduce political challenges. Some sets of policies—like value-based care—can support these sorts of changes and accelerate the adoption of LHS.
{"title":"The surprising politics of learning health systems","authors":"Adalsteinn Brown, Robert J. Reid","doi":"10.1002/lrh2.70008","DOIUrl":"https://doi.org/10.1002/lrh2.70008","url":null,"abstract":"<p>Learning Health Systems (LHS) are an increasingly common element of health policy reform efforts in a number of jurisdictions. There is little disagreement around the LHS vision, and early adopters provide some development guidance. Despite the attractiveness of the LHS vision, progress on adoption by systems remains slow. In this commentary, we consider one potential reason, namely politics, or the ways in which government bodies, interest groups, and political ideas shape structures and policies. LHS can change the ways that health systems work and interact with payors and populations and thereby create political challenges. The need for upfront new investment to build capacity for LHS activities, the creation of new partnerships or collaborations, increased transparency, and the direct engagement of populations can all create political risks and subsequent barriers. With a broad population health focus that extends across typical political cycles, politics may create an even greater barrier. We suggest that building strong engagement, clear and transparent accountabilities, communities of practice and other vehicles to promote data sharing and transparency, and careful attention to risk management may all help reduce political challenges. Some sets of policies—like value-based care—can support these sorts of changes and accelerate the adoption of LHS.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635503","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}
Amanda L. Missel, Noor Khan, Michelle Williams, Emilee I. Coulter-Thompson, Sameer Hameed, James M. Pribble, Theresa Shields, Gwen Fosse, Stephen R. Dowker, Robert W. Neumar, Robert Swor, Jodyn Platt
Introduction
In the United States, only 8.2% of people treated for out-of-hospital cardiac arrest (OHCA) in 2023 survived with good neurological function. The interval from the onset of cardiac arrest to the start of CPR and defibrillation is strongly associated with survival and neurologic recovery. We present our process of conducting stakeholder engagement sessions to engage an OHCA Learning Community to develop an intervention to decrease time to first treatment (CPR and AED) and improve survival from OHCA in Michigan's Washtenaw and Livingston Counties.
Methods
We conducted a CPR survey, a Community Engagement Studio, and three stakeholder engagement sessions with the OHCA Learning Community in Washtenaw and Livingston Counties in Michigan to achieve three goals: (1) increasing public awareness of OHCA, (2) engaging diverse and underserved communities, and (3) developing an intervention.
Results
As a result of these sessions, we identified improving in-home OHCA response, addressing disparities in underserved and minority communities, and increasing capacity among families and friends as the key targets for intervention.
Conclusion
Based on these sessions, we developed a HeartSafe Home intervention that aims to prepare household members to respond to a cardiac arrest at home.
{"title":"The experience of a learning community to develop a HeartSafe Home intervention to improve survival from out-of-hospital cardiac arrest","authors":"Amanda L. Missel, Noor Khan, Michelle Williams, Emilee I. Coulter-Thompson, Sameer Hameed, James M. Pribble, Theresa Shields, Gwen Fosse, Stephen R. Dowker, Robert W. Neumar, Robert Swor, Jodyn Platt","doi":"10.1002/lrh2.70010","DOIUrl":"https://doi.org/10.1002/lrh2.70010","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>In the United States, only 8.2% of people treated for out-of-hospital cardiac arrest (OHCA) in 2023 survived with good neurological function. The interval from the onset of cardiac arrest to the start of CPR and defibrillation is strongly associated with survival and neurologic recovery. We present our process of conducting stakeholder engagement sessions to engage an OHCA Learning Community to develop an intervention to decrease time to first treatment (CPR and AED) and improve survival from OHCA in Michigan's Washtenaw and Livingston Counties.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We conducted a CPR survey, a Community Engagement Studio, and three stakeholder engagement sessions with the OHCA Learning Community in Washtenaw and Livingston Counties in Michigan to achieve three goals: (1) increasing public awareness of OHCA, (2) engaging diverse and underserved communities, and (3) developing an intervention.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>As a result of these sessions, we identified improving in-home OHCA response, addressing disparities in underserved and minority communities, and increasing capacity among families and friends as the key targets for intervention.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Based on these sessions, we developed a HeartSafe Home intervention that aims to prepare household members to respond to a cardiac arrest at home.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145375214","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 concept of a learning health system (LHS) was established nearly 20 years ago as a unifying commitment to speed the generation and use of evidence primarily by leveraging rapid advances in data and technologies, resulting in optimized care for each patient. In the ensuing decades, vanguard adopters of the LHS who have sought to move the LHS from conceptual to operational have done so in ways that fit with and reflect their organizational structure, mission, and culture—as well as their personal values and experiences. They have also extended the focus from health care to include individual and population health more broadly. This commentary describes four distinctive models that have evolved as learning health system activities have matured. Viewing this diversity as a strength, the features, commonalities, and unique differences of these models are described.
{"title":"Four distinct models of learning health systems: Strength through diversity","authors":"Charles P. Friedman, Sarah M. Greene","doi":"10.1002/lrh2.70009","DOIUrl":"https://doi.org/10.1002/lrh2.70009","url":null,"abstract":"<p>The concept of a learning health system (LHS) was established nearly 20 years ago as a unifying commitment to speed the generation and use of evidence primarily by leveraging rapid advances in data and technologies, resulting in optimized care for each patient. In the ensuing decades, vanguard adopters of the LHS who have sought to move the LHS from conceptual to operational have done so in ways that fit with and reflect their organizational structure, mission, and culture—as well as their personal values and experiences. They have also extended the focus from health care to include individual and population health more broadly. This commentary describes four distinctive models that have evolved as learning health system activities have matured. Viewing this diversity as a strength, the features, commonalities, and unique differences of these models are described.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835793","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}
Mayuree Rao, Cindie Slightam, Alicia J. Cohen, Jamie S. Marsal, Camila Chaudhary, Karin Nelson, Matthew L. Maciejewski, Josephine C. Jacobs, Liberty Greene, Dan V. Blalock, Donna M. Zulman
Objective
To prioritize social risks (individual-level social and economic conditions) that may influence a person's health for inclusion in a national survey of Veterans Affairs (VA) healthcare system patients.
Data Sources and Study Setting
Quantitative ratings of candidate survey measures were obtained from a national Advisory Group of researchers, clinicians, Veterans, and VA operations leaders; qualitative input was collected from the Advisory Group and Veterans.
Study Design
We solicited input on social risk prioritization across four phases: (1) candidate social risks were identified through a literature review and existing screening tools, (2) Advisory Group members (n = 15) individually and anonymously rated social risks on four criteria (impact on health outcomes, impact on patient experience, actionability, and overall prioritization), (3) the Advisory Group discussed collective ratings and provided qualitative feedback about candidate social risks, and 4) Veterans (n = 29) provided qualitative feedback about the draft survey during four Veteran Engagement Group meetings and in survey pretesting with individuals (n = 5).
Data Collection/Extraction Methods
Selection of social risks for survey inclusion was based on an a priori definition of a social risk and relevance to Veterans (phase 1), quantitative and qualitative input from the Advisory Group (phases 2 and 3), and qualitative Veteran input (phase 4).
Principal Findings
An initial list of 37 social risks was pared down to 18 for inclusion in a national survey: financial strain, health care/medicine access and affordability, food insecurity, homelessness/housing insecurity, transportation barriers, digital access/literacy, utilities insecurity, social support, caregiver responsibilities, discrimination experiences, interpersonal violence, education, employment, health literacy, legal problems or exposure to the justice system, race/ethnicity, gender identity, and sexual orientation.
Conclusions
Our partner-informed approach combining quantitative and qualitative input offers a road map for other learning health systems seeking to prioritize social r
{"title":"A partner-informed approach to prioritizing social risks for research in a learning health system","authors":"Mayuree Rao, Cindie Slightam, Alicia J. Cohen, Jamie S. Marsal, Camila Chaudhary, Karin Nelson, Matthew L. Maciejewski, Josephine C. Jacobs, Liberty Greene, Dan V. Blalock, Donna M. Zulman","doi":"10.1002/lrh2.70011","DOIUrl":"https://doi.org/10.1002/lrh2.70011","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To prioritize social risks (individual-level social and economic conditions) that may influence a person's health for inclusion in a national survey of Veterans Affairs (VA) healthcare system patients.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>Quantitative ratings of candidate survey measures were obtained from a national Advisory Group of researchers, clinicians, Veterans, and VA operations leaders; qualitative input was collected from the Advisory Group and Veterans.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>We solicited input on social risk prioritization across four phases: (1) candidate social risks were identified through a literature review and existing screening tools, (2) Advisory Group members (<i>n</i> = 15) individually and anonymously rated social risks on four criteria (impact on health outcomes, impact on patient experience, actionability, and overall prioritization), (3) the Advisory Group discussed collective ratings and provided qualitative feedback about candidate social risks, and 4) Veterans (<i>n</i> = 29) provided qualitative feedback about the draft survey during four Veteran Engagement Group meetings and in survey pretesting with individuals (<i>n</i> = 5).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>Selection of social risks for survey inclusion was based on an a priori definition of a social risk and relevance to Veterans (phase 1), quantitative and qualitative input from the Advisory Group (phases 2 and 3), and qualitative Veteran input (phase 4).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>An initial list of 37 social risks was pared down to 18 for inclusion in a national survey: financial strain, health care/medicine access and affordability, food insecurity, homelessness/housing insecurity, transportation barriers, digital access/literacy, utilities insecurity, social support, caregiver responsibilities, discrimination experiences, interpersonal violence, education, employment, health literacy, legal problems or exposure to the justice system, race/ethnicity, gender identity, and sexual orientation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our partner-informed approach combining quantitative and qualitative input offers a road map for other learning health systems seeking to prioritize social r","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145375142","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号