Learning Health Systems最新文献

{"title":"VA's EHR transition and health professions trainee programs: Findings and impacts of a multistakeholder learning community","authors":"Julian Brunner,&nbsp;Ellen A. Ahlness,&nbsp;Ekaterina Anderson,&nbsp;Brianne K. Molloy-Paolillo,&nbsp;Alexandre Braga,&nbsp;Sarah L. Cutrona,&nbsp;Christian D. Helfrich,&nbsp;Deborah Levy,&nbsp;Erin Matteau,&nbsp;Edward Walton,&nbsp;George Sayre,&nbsp;Seppo T. Rinne","doi":"10.1002/lrh2.10460","DOIUrl":"https://doi.org/10.1002/lrh2.10460","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>The Department of Veterans Affairs (VA) is undergoing an unprecedented electronic health record (EHR) transition, switching from its homegrown EHR to a commercial system. The transition affects nearly every clinical employee but is particularly disruptive to health professions trainees (HPTs)—an often-overlooked population in EHR transitions. To better understand and address trainee challenges with the EHR transition, we formed a multistakeholder learning community. In this study, we describe the findings of this learning community and the practices and policies developed in response.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In the qualitative study designed and executed by our learning community, we conducted 51 interviews with HPTs, program leaders, and preceptors before and multiple times after an EHR transition site's go-live (February 16, 2022 to April 7, 2023). We merged interview transcripts with 125 survey free-text responses from a survey conducted with preceptors 2 months post-go-live and conducted thematic analysis to identify key themes. To complement qualitative findings, we also include a quantitative survey finding, and, where applicable, we note policy and practice responses spurred by our learning community.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Interviews yielded six key themes: (1) High satisfaction with HPT programs, despite negative impacts of the EHR transition; (2) early delays, then substantial improvements, in HPTs' EHR access; (3) persistent challenges with HPTs' EHR training and support, mitigated by local and national efforts; (4) the challenge of learning to use a rapidly evolving EHR during clinical training; (5) reduced visit volume as a continuing barrier to education; and (6) an impression that HPTs' relative lack of exposure to the prior EHR facilitated their proficiency with the new EHR.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Findings highlighted challenges for HPT programs related to the EHR transition, which spurred important changes including the creation of a national VA council to represent the needs of HPTs in the EHR transition, and improvements to HPTs' EHR training and access.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10460","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836444","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}

{"title":"Thanks to our peer reviewers","authors":"","doi":"10.1002/lrh2.10464","DOIUrl":"10.1002/lrh2.10464","url":null,"abstract":"<p>The publication of Issue 4 marks the completion of Volume 8 of <i>Learning Health Systems</i>. An international, trans-disciplinary, open access publication, the journal has advanced research and scholarship on learning health systems in partnership with our reviewers. With indexing in multiple major sources and an Impact Factor of 2.6, we have achieved a publication milestone that signals a sustainable, positive trajectory. Articles from the journal were downloaded over 123, 126 times in 2023.</p><p>Each year, the journal publishes a Special Issue; we have now published eight <i>Special Issues</i>: “Patient Empowerment and the Learning Health System” (v.1); “Ethical, Legal, and Social Implications of Learning Health Systems” (v.2); “Learning Health Systems: Connecting Research to Practice Worldwide” (v.3); “Human Phenomics and the Learning Health System” (v.4); “Collaborative Learning Health Systems: Science and Practice” (v.5); and “Education To Meet the Multidisciplinary Workforce Needs of Learning Health Systems” (v.6); “Transforming Health Through Computable Biomedical Knowledge (CBK)” (v.7); and “Envisioning Public Health As a Learning Health System” (v.8). Our talented guest editors have been instrumental in helping these <i>Special Issues</i> come to fruition.</p><p>In addition, we published a Supplement (“Focus on Research by AcademyHealth members”) in June 2024. The Supplement was a collaboration with the Department of Learning Health Sciences (University of Michigan), Academy Health, (LHS Interest Group), and John Wiley &amp; Sons.</p><p>We are keenly aware that these achievements would not have happened without the dedicated efforts and insightful comments of all those individuals who accepted invitations to review submitted articles. With busy schedules and full commitments, these individuals found the time and energy to contribute their expertise to our authors to help ensure that their papers met (and often exceeded) the journal's high standards for publication.</p><p>Please accept our sincere gratitude for your outstanding efforts!</p><p><i>Charles P. Friedman</i>, Editor in Chief</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11493543/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510012","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}

{"title":"Assessing the harmonization of structured electronic health record data to reference terminologies and data completeness through data provenance","authors":"Keith Marsolo,&nbsp;Lesley Curtis,&nbsp;Laura Qualls,&nbsp;Jennifer Xu,&nbsp;Yinghong Zhang,&nbsp;Thomas Phillips,&nbsp;C. Larry Hill,&nbsp;Gretchen Sanders,&nbsp;Judith C. Maro,&nbsp;Daniel Kiernan,&nbsp;Christine Draper,&nbsp;Kevin Coughlin,&nbsp;Sarah K. Dutcher,&nbsp;José J. Hernández-Muñoz,&nbsp;Monique Falconer","doi":"10.1002/lrh2.10468","DOIUrl":"https://doi.org/10.1002/lrh2.10468","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>(1) Assess the harmonization of structured electronic health record data (laboratory results and medications) to reference terminologies and characterize the severity of issues. (2) Identify issues of data completeness by comparing complementary data domains, stratifying by time, care setting, and provenance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Queries were distributed to 3 Data Partners (DP). Using harmonization queries, we examined the top 200 laboratory results and medications by volume, identifying outliers and computing summary statistics. The completeness queries looked at 4 conditions of interest and related clinical concepts. Counts were generated for each condition, stratified by year, encounter type, and provenance. We analyzed trends over time within and across DPs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We found that the median number of codes associated with a given laboratory/medication name (and vice versa) generally met expectations, though there were DP-specific issues that resulted in outliers. In addition, there were drastic differences in the percentage of patients with a given concept depending on provenance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The harmonization queries surfaced several mapping errors, as well as issues with overly specific codes and records with “null” codes. The completeness queries demonstrated having access to multiple types of data provenance provides more robust results compared with any single provenance type. Harmonization errors between source data and reference terminologies may not be widespread but do exist within CDMs, affecting tens of thousands or even millions of records. Provenance information can help identify potential completeness issues with EHR data, but only if it is represented in the CDM and then populated by DPs.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10468","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836407","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}

{"title":"Envisioning public health as a learning health system","authors":"Theresa A. Cullen,&nbsp;Lisa Villarroel","doi":"10.1002/lrh2.10465","DOIUrl":"10.1002/lrh2.10465","url":null,"abstract":"&lt;p&gt;This Special Issue of &lt;i&gt;Learning Health Systems&lt;/i&gt; seeks to understand what it would take for public health to become a learning health system. The selected articles highlight the required organizational insights and foundational components, such as including public health partners in care networks and ensuring timely, relevant public health data in cycles of public health learning—both of which reflect the foundational public health core functions of Assessment, Assurance, and Policy.&lt;span&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;&lt;/p&gt;&lt;p&gt;The transition to a learning public health system may herald the next phase of public health. Public Health 1.0 envisioned governmental entities providing functions to improve public health during a time of growth of clinical and public healthcare. Public Health 2.0, as outlined in the 1988 Institute of Medicine's &lt;i&gt;The Future of Public Health&lt;/i&gt;,&lt;span&gt;&lt;sup&gt;2&lt;/sup&gt;&lt;/span&gt; focused on traditional public health agency programs. In 2016, Public Health 3.0 stressed multi-partner engagement around social determinants of health.&lt;span&gt;&lt;sup&gt;3&lt;/sup&gt;&lt;/span&gt;&lt;/p&gt;&lt;p&gt;We propose that Public Health 4.0 integrate historical lessons from public health with those from a learning healthcare system to embody a Learning Public Health System model.&lt;span&gt;&lt;sup&gt;4&lt;/sup&gt;&lt;/span&gt; By expanding stakeholders, integrating organizational learning into our processes, continually using data and evaluation to form new public health practices, and incorporating self-evaluation and communication transparency, public health can continually learn and improve.&lt;/p&gt;&lt;p&gt;As public health officials in state and local health departments, we acknowledge that our own institutions are not yet learning public health systems. Our foundational cycles of Assessment, Assurance, and Policy often buckle due to the lack of workforce, funding, and infrastructure. However, we believe that aligning with a learning health system framework would recommit public health to rapid cycle innovation and response as we face stubborn foes like heat, loneliness, substance use, and vaccine hesitancy.&lt;/p&gt;&lt;p&gt;This published collection of articles helps inform the framework of a learning health system that needs to be envisioned and actualized.&lt;/p&gt;&lt;p&gt;One approach for the creation of a learning public health system model is to broaden the conceptual framework of what is included in a learning health system. Rather than insulating the model within a healthcare system, participating partners would include public health and community-based organizations. The case study from Semprini et al.&lt;span&gt;&lt;sup&gt;5&lt;/sup&gt;&lt;/span&gt; presents how a rural cancer network worked with the public health cancer registry to access their data to enhance patient outcomes. Along a similar model, Meigs et al.&lt;span&gt;&lt;sup&gt;6&lt;/sup&gt;&lt;/span&gt; propose incorporating community-based organizations (CBOs) into a learning health system at all stages, with examples of successful integrations in refugee initiatives. These papers illustrate the expansion of l","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11493542/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510009","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}

{"title":"Learning health systems to implement chronic disease prevention programs: A novel framework and perspectives from an Australian health service","authors":"Luke Wolfenden,&nbsp;John Wiggers,&nbsp;Courtney Barnes,&nbsp;Cassandra Lane,&nbsp;Daniel Groombridge,&nbsp;Katie Robertson,&nbsp;Jannah Jones,&nbsp;Sam McCrabb,&nbsp;Rebecca K. Hodder,&nbsp;Adam Shoesmith,&nbsp;Nayerra Hudson,&nbsp;Nicole McCarthy,&nbsp;Melanie Kingsland,&nbsp;Emma Doherty,&nbsp;Emily Princehorn,&nbsp;Meghan Finch,&nbsp;Nicole Nathan,&nbsp;Rachel Sutherland","doi":"10.1002/lrh2.10466","DOIUrl":"10.1002/lrh2.10466","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Chronic diseases are a considerable burden to health systems, communities, and patients. Much of this burden, however, could be prevented if interventions effective in reducing chronic disease risks were routinely implemented.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Aims</h3>\u0000 \u0000 <p>The aim of this paper is to discuss the role of public health agencies in preventing chronic disease through the application of learning health system (LHS) approaches to improve the implementation of evidence-based interventions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Materials and Methods</h3>\u0000 \u0000 <p>We draw on the literature and our experience operating a local LHS in Australia that has achieved rapid improvements in the implementation of chronic disease prevention interventions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The proposed LHS framework has been adapted to be both implementation and chronic disease prevention focused. The framework describes both broad improvement processes, and the infrastructure and other support (pillars) recommended to support its core functions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The framework serves as a basis for further exploration of the potentially transformative role LHS's may have in addressing the chronic disease health crisis.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11493556/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510010","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}

{"title":"Developing a codebook to characterize barriers, enablers, and strategies for implementing learning health systems from a multilevel perspective","authors":"Louise Shaw,&nbsp;Meg Perrier,&nbsp;Kasra Tahmasebian,&nbsp;Kimberly Wong,&nbsp;Pranjali Yajnik,&nbsp;Zihan Zhu,&nbsp;Kayley Lyons","doi":"10.1002/lrh2.10452","DOIUrl":"https://doi.org/10.1002/lrh2.10452","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Learning health systems (LHSs) play a crucial role in improving healthcare delivery and outcomes through continuous learning and data-driven decision-making. Implementation of LHSs spans individual, organization, and systemic levels of healthcare. This paper outlines a systematic approach for developing a comprehensive codebook to identify barriers, enablers, and strategies associated with the establishment and operation of LHS from a multilevel perspective.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The codebook development process was divided into two phases and employed a coding team. Phase 1 involved the synthesis of previous literature, which drove the development of initial codes. Phase 2 included the testing of the codebook with a pilot dataset to derive new codes or iterative refinement, ensuring robustness, and validity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The literature search revealed 12 papers that detailed the barriers, enablers, and strategies for LHS implementation. Micro-level codes were derived from a mixture of existing literature and our pilot dataset. Most meso-level codes barriers and enablers were derived from the literature, with some subcodes derived from participant interviews. All strategies for implementation at the meso-level were identified in the literature. At the macro-level, all codes and subcodes were from the literature.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The codebook contributes to the advancement of implementation science in LHS. The codebook facilitates effective analysis and understanding of the key factors influencing the success of LHS implementation, offering practical insights for policymakers, healthcare practitioners and researchers engaged in the ongoing evolution of LHS.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10452","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835859","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}

{"title":"Data-driven prediction of prolonged air leak after video-assisted thoracoscopic surgery for lung cancer: Development and validation of machine-learning-based models using real-world data through the ePath system","authors":"Saori Tou,&nbsp;Koutarou Matsumoto,&nbsp;Asato Hashinokuchi,&nbsp;Fumihiko Kinoshita,&nbsp;Hideki Nakaguma,&nbsp;Yukio Kozuma,&nbsp;Rui Sugeta,&nbsp;Yasunobu Nohara,&nbsp;Takanori Yamashita,&nbsp;Yoshifumi Wakata,&nbsp;Tomoyoshi Takenaka,&nbsp;Kazunori Iwatani,&nbsp;Hidehisa Soejima,&nbsp;Tomoharu Yoshizumi,&nbsp;Naoki Nakashima,&nbsp;Masahiro Kamouchi","doi":"10.1002/lrh2.10469","DOIUrl":"https://doi.org/10.1002/lrh2.10469","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>The reliability of data-driven predictions in real-world scenarios remains uncertain. This study aimed to develop and validate a machine-learning-based model for predicting clinical outcomes using real-world data from an electronic clinical pathway (ePath) system.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>All available data were collected from patients with lung cancer who underwent video-assisted thoracoscopic surgery at two independent hospitals utilizing the ePath system. The primary clinical outcome of interest was prolonged air leak (PAL), defined as drainage removal more than 2 days post-surgery. Data-driven prediction models were developed in a cohort of 314 patients from a university hospital applying sparse linear regression models (least absolute shrinkage and selection operator, ridge, and elastic net) and decision tree ensemble models (random forest and extreme gradient boosting). Model performance was then validated in a cohort of 154 patients from a tertiary hospital using the area under the receiver operating characteristic curve (AUROC) and calibration plots.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>To mitigate bias, variables with missing data related to PAL or those with high rates of missing data were excluded from the dataset. Fivefold cross-validation indicated improved AUROCs when utilizing key variables, even post-imputation of missing data. Dichotomizing continuous variables enhanced performance, particularly when fewer variables were employed in the decision tree ensemble models. Consequently, regression models incorporating seven key variables in complete case analysis demonstrated superior discriminatory ability for both internal (AUROCs: 0.77–0.84) and external cohorts (AUROCs: 0.75–0.84). These models exhibited satisfactory calibration in both cohorts.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The data-driven prediction model implementing the ePath system exhibited adequate performance in predicting PAL post-video-assisted thoracoscopic surgery, optimizing variables and considering population characteristics in a real-world setting.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10469","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835833","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}

{"title":"The translation-to-policy learning cycle to improve public health","authors":"Amy M. Kilbourne,&nbsp;Melissa Z. Braganza,&nbsp;Dawn M. Bravata,&nbsp;Jack Tsai,&nbsp;Richard E. Nelson,&nbsp;Alex Meredith,&nbsp;Kenute Myrie,&nbsp;Rachel Ramoni","doi":"10.1002/lrh2.10463","DOIUrl":"10.1002/lrh2.10463","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>Learning Health Systems (LHSs) have not directly informed evidence-based policymaking. The Translation-to-Policy (T2P) Learning Cycle aligns scientists, end-users, and policymakers to support a repeatable roadmap of innovation and quality improvement to optimize effective policies toward a common public health goal. We describe T2P learning cycle components and provide examples of their application.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The T2P Learning Cycle is based on the U.S. Department of Veterans Affairs (VA) Office of Research and Development and Quality Enhancement Research Initiative (QUERI), which supports research and quality improvement addressing national public health priorities to inform policy and ensure programs are evidence-based and work for end-users. Incorporating LHS infrastructure, the T2P Learning Cycle is responsive to the Foundations for Evidence-based Policymaking Act, which requires U.S. government agencies to justify budgets using evidence.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The learning community (patients, providers, clinical/policy leaders, and investigators) drives the T2P Learning Cycle, working toward one or more specific, shared priority goals, and supports a repeatable cycle of evidence-building and evaluation. Core T2P Learning Cycle functions observed in the examples from housing/economic security, precision oncology, and aging include governance and standard operating procedures to promote effective priority-setting; complementary research and quality improvement initiatives, which inform ongoing data curation at the learning system level; and sustainment of continuous improvement and evidence-based policymaking.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The T2P Learning Cycle integrates research translation with evidence-based policymaking, ensuring that scientific innovations address public health priorities and serve end-users through a repeatable process of research and quality improvement that ensures policies are scientifically based, effective, and sustainable.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11493547/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510013","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}

{"title":"Creating a learning health system to include environmental determinants of health: The GroundsWell experience","authors":"Sarah E. Rodgers,&nbsp;Rebecca S. Geary,&nbsp;Roberto Villegas-Diaz,&nbsp;Iain E. Buchan,&nbsp;Hannah Burnett,&nbsp;Tom Clemens,&nbsp;Rebecca Crook,&nbsp;Helen Duckworth,&nbsp;Mark Alan Green,&nbsp;Elly King,&nbsp;Wenjing Zhang,&nbsp;Oliver Butters","doi":"10.1002/lrh2.10461","DOIUrl":"10.1002/lrh2.10461","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Policies aiming to prevent ill health and reduce health inequalities need to consider the full complexity of health systems, including environmental determinants. A learning health system that incorporates environmental factors needs healthcare, social care and non-health data linkage at individual and small-area levels. Our objective was to establish privacy-preserving household record linkage for England to ensure person-level data remain secure and private when linked with data from households or the wider environment.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A stakeholder workshop with participants from our regional health board, together with the regional data processor, and the national data provider. The workshop discussed the risks and benefits of household linkages. This group then co-designed actionable dataflows between national and local data controllers and processors.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>A process was defined whereby the Personal Demographics Service, which includes the addresses of all patients of the National Health Service (NHS) in England, was used to match patients to a home identifier, for the time they are recorded as living at that address. Discussions with NHS England resulted in secure and quality-assured data linkages and a plan to flow these pseudonymised data onwards into regional health boards. Methods were established, including the generation of matching algorithms, transfer processes and information governance approvals. Our collaboration accelerated the development of a new data governance application, facilitating future public health intervention evaluations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>These activities have established a secure method for protecting the privacy of NHS patients in England, while allowing linkage of wider environmental data. This enables local health systems to learn from their data and improve health by optimizing non-health factors. Proportionate governance of health and linked non-health data is practical in England for incorporating key environmental factors into a learning health system.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11493545/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510008","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}

{"title":"Engagement as a mechanism of action in collaborative learning health systems","authors":"Michael Seid,&nbsp;Breck Gamel,&nbsp;Becky Woolf,&nbsp;David M. Hartley,&nbsp;Maureen Dunn,&nbsp;Alexandra H. Vinson","doi":"10.1002/lrh2.10459","DOIUrl":"https://doi.org/10.1002/lrh2.10459","url":null,"abstract":"<p>Ongoing experience and emerging evidence suggest that Collaborative Learning Health Systems (CLHSs) are a promising approach to transforming population outcomes and organizational care processes. As implied in their name, this type of Learning Health System both promotes and relies upon collaboration to achieve its aims. However, to realize the promise of the Collaborative Learning Health System, a better understanding of engagement as a catalyst for collaboration is necessary. In this commentary, we elaborate the phenomenon of engagement. We define engagement in the context of CLHSs, consider dimensions of engagement, and further explore the role of engagement as a catalyst of network functioning. We conclude by offering an agenda for research and practice intended to develop an understanding of engagement so as to further advance the theory and practice of CLHS efforts and ultimately promote the uptake of the CLHS model.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10459","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835901","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}