Learning Health Systems最新文献

{"title":"2025 Advances in Learning Health System Sciences (AiLHSS) Conference: Abstracts","authors":"","doi":"10.1002/lrh2.70066","DOIUrl":"https://doi.org/10.1002/lrh2.70066","url":null,"abstract":"<p>The Center for Learning Health System Sciences (CLHSS) at the University of Minnesota (UMN) hosted its third annual <i>Advances in Learning Health System Sciences</i> (AiLHSS) Conference on September 22-23 at McNamara Alumni Center in Minneapolis, MN. This year's conference theme, “Building Bridges in LHS”, highlights AiLHSS’ role in both advancing the field of learning health systems while also providing networking and collaboration opportunities.</p><p>Reflecting a growing interest in LHS work, this year's conference had a 58% increase in registration with 346 registrants, including public health professionals, researchers, clinicians, hospital administrators, and industry partners. Day 1 included three keynote speakers, a poster session with over 60 presenters, plenary sessions featuring local, regional, and national research, and breakout sessions. The Day 1 agenda purposely included time for networking, including extended poster session times and an evening reception. Day 2 featured half-day supplemental workshops.</p><p>Day 1 began with opening remarks from CLHSS Director Genevieve Melton-Meaux, MD, PhD, and Shashank Priya, PhD, Vice President for Research & Innovation at UMN. The morning included a keynote address by Amy Kilbourne, PhD, MPH (US Department of Veterans Affairs and the University of Michigan), who highlighted how implementation science (IS) can align research translation with public health goals. Following was a plenary session showcasing regional and national initiatives in advancing IS in health care. Topics included reducing racial disparities in cardiac rehabilitation, avoiding unnecessary emergency department admissions for seniors, and improving guideline adherence for children with cerebral palsy. An extended poster session closed the morning agenda, offering additional networking opportunities.</p><p>The midday agenda shifted to pragmatic trials. Over lunch, Areef Ishani, MD, MS (Minneapolis VA and the UMN Department of Medicine) delivered a keynote focused on using pragmatic trials to produce rigorous evidence. A plenary session followed featuring LHS partnerships with public health, including presentations on statewide public health tools, case studies of partnerships between public health and health care, and example comparisons of Electronic Health Record (EHR) data and local public health data to better understand community health trends. Following this were two concurrent breakout sessions: (1) <i>Digital Transformation in Healthcare: Where Strategy, AI, and Investment Meet</i>, which explored how emerging technologies and venture investment are connecting to drive innovation across health care systems, and (2) <i>Data Action: Stories from the MN EHR Consortium</i>, which highlighted how Minnesota's EHR data is being utilized to provide insights into community health.</p><p>The end of the first day's agenda centered on the role data plays in improving healthcare practice. Six presenters participated in fi","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge Architecture for Race and Ethnic Group Defining in Learning Health Systems","authors":"Matthew F. Hudson,&nbsp;Virginia M. S. van Staden,&nbsp;Alicia M. Oostdyk,&nbsp;Julie C. Martin","doi":"10.1002/lrh2.70071","DOIUrl":"10.1002/lrh2.70071","url":null,"abstract":"<p>Previous work affirms access and health care challenges persist for multiple racial groups. Race and ethnicity are socially and politically constructed terms conceived to describe and categorize people hierarchically (race) and describe people from a similar national or regional background also sharing common national, cultural, historical, and social experiences (ethnicity). Definitions of race and ethnicity may vary across historical, political, and geographic contexts. This variation challenges health care organizations to reliably measure health care outcomes longitudinally within and between racial and ethnic groups. Further, suboptimal concordance between race and ethnicity self-report vs. electronic health record suggests opportunities exist to improve race and ethnicity data solicitation and capture. Learning Health Systems (LHSs) may be particularly poised to address these challenges, as LHSs commit to equitable, transparent, and accountable engagement of individuals and families in care innovation. This discussion provides a rationale, model, and practical strategy to engage individuals and families in race data defining—an essential antecedent to health equity assessment and intervention development. The discussion encourages testing to ensure the proposed theory and steps beget comprehensive and precise race definitions. Enhanced precision in defining race may subsequently inform equity evaluation and interventions in LHSs.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12945470/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147327661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Implementation of a State-Wide Network for Near Real-Time Public Health Surveillance and Research: The Minnesota Electronic Health Record Consortium Experience","authors":"Steven G. Johnson,&nbsp;Alanna M. Chamberlain,&nbsp;Paul Drawz,&nbsp;R. Adams Dudley,&nbsp;Stephen C. Waring,&nbsp;Tyler Winkelman,&nbsp;Peter Bodurtha,&nbsp;Kelly Bergmann,&nbsp;Inih Essien,&nbsp;Nayanjot K. Rai,&nbsp;Samuel T. Patnoe,&nbsp;Karen L. Margolis,&nbsp;MN EHR Consortium","doi":"10.1002/lrh2.70070","DOIUrl":"https://doi.org/10.1002/lrh2.70070","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>The Minnesota Electronic Health Record Consortium (MNEHRC) was established during the early days of the COVID-19 pandemic to provide data for public health surveillance from the eleven largest health care systems in Minnesota.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This is a descriptive study of the Consortium, which is a federated network that implements best practices for governance and data infrastructure to support public health surveillance and clinical research. We conducted an analysis of the Consortium members, governance structure, infrastructure, and the characteristics of the patient population.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The Consortium health systems collect information from 105 hospitals, 773 clinics, 100 emergency departments and 29 040 providers. Information about the health systems and the demographic and clinical characteristics of its 5 471 367 patients is provided, which represents more than 90% of the patients in Minnesota. This manuscript also details the MNEHRC governance structure, working groups, data use agreements and technical infrastructure. The Consortium has produced several studies with state-wide impact. One study, Health Trends Across Communities in Minnesota, is described in detail to illustrate aspects of this collaboration.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>MNEHRC has been a successful collaboration and vital resource for public health surveillance in the state of Minnesota. Initially, the Consortium focused on surveillance related to COVID-19 infections and vaccinations but has recently expanded into other public health and chronic disease research.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147288463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Development and Co-Production of a Caregiver Coping Resource for Pediatric Inflammatory Bowel Disease and Autoimmune Liver Disease","authors":"Jennie G. David-Rodgers,&nbsp;Christina E. Holbein,&nbsp;Hannah McKillop,&nbsp;Maria E. Lester,&nbsp;Victoria Levine,&nbsp;Ildiko Mehes,&nbsp;Heidi C. Riechel,&nbsp;Jane R. Weyer,&nbsp;ImproveCareNow Learning Health System,&nbsp;Autoimmune Liver Disease Network for Kids Learning Health System","doi":"10.1002/lrh2.70061","DOIUrl":"10.1002/lrh2.70061","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>Caregivers of children with chronic conditions have mental health needs, with no known resources for caregiver coping in pediatric Inflammatory Bowel Disease (IBD) and autoimmune liver disease (AILD), which can be co-morbid. Quality improvement (QI) has previously co-produced resources within Learning Health Networks (LHNs). This QI work sought to co-produce a caregiver coping resource for caregivers of children with IBD and/or AILD.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A multidisciplinary QI team of caregivers and psychosocial clinicians applied QI methodology to iteratively develop a caregiver coping resource. This work took place within two connected LHNs, ImproveCareNow (pediatric IBD) and Autoimmune Liver Disease Network for Kids (AILD).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Over 1.5 years, a multidisciplinary QI team of eight caregivers and four psychosocial clinicians co-produced a caregiver coping resource. The formatted caregiver coping resource is 164 pages long with sections including caregiver coping (e.g., anxiety), caregiver support of child coping (e.g., discussing emotions), special considerations (e.g., surgery in IBD), and logistical topics (e.g., navigating insurance). The resource integrates caregiver and psychosocial clinician quotes and reputable resources. The resource is freely available and information about how to access the resource is included in the manuscript text.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>While caregivers of children with IBD and/or AILD have known coping needs, caregiver-focused coping resources are urgently needed. A multidisciplinary team of caregivers and psychosocial clinicians within LHNs co-produced such a resource that is freely accessible. This QI work demonstrates the collaborative potential to build caregiver-focused resources for all families to benefit. Future work is needed to understand the clinical use of this resource as well as the impact of this resource for caregivers of children with IBD and/or AILD.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12912881/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146228923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Priorities for the Department of Veterans Affairs Strategic Plan: A Rapid Multi-Method Evaluation","authors":"John P. Donnelly,&nbsp;C. Ann Vitous,&nbsp;Kaylee W. Burgan,&nbsp;Gezan M. Yahya,&nbsp;Jessica L. Johnson,&nbsp;Jessica A. McDonald,&nbsp;Nicholas W. Bowersox,&nbsp;Linda M. Kawentel","doi":"10.1002/lrh2.70068","DOIUrl":"10.1002/lrh2.70068","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Understanding the priorities of patients and leaders can help organizations prepare for the future. This evaluation sought to generate actionable data on priorities in support of the Department of Veterans Affairs (VA) strategic plan, with priorities identified within the literature as well as among Veterans and VA leaders.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A rapid qualitative evaluation was conducted, including a document analysis based on published information, focus groups with Veterans associated with research centers, and semi-structured interviews with VA leaders. All data were analyzed using rapid qualitative methods, resulting in comprehensive templated descriptions of topics discussed and representative quotes. Summative content analysis was used to report frequencies and identify the most frequently mentioned priorities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>A total of 170 documents (e.g., academic/research articles, government publications) were analyzed. Seventeen Veteran focus groups (92 participants) and 26 semi-structured interviews with VA leaders (representing 19 program offices) were conducted. There were differences in the most frequently mentioned priorities across sources, with none ranked in the top 5 for all. However, “Access and Continuity of Care,” “Health Benefits,” “Special Groups,” and “Workforce” were among the top 10 mentioned within all sources. Among these, “Workforce” was most mentioned within the document analysis, whereas “Access and Continuity of Care” was most mentioned in focus groups. Both “Health Benefits” and “Access and Continuity of Care” were most mentioned during leadership interviews.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our findings suggest shared priorities to emphasize as VA looks to the future: improving care access, efficient and effective benefits processing, ensuring the needs of special Veteran groups are met, and sustaining an adequate workforce. However, further work should focus on understanding how Veterans and staff engage with the system to guide the investment of limited organizational resources. This work serves as an example of how rapid evaluation could be applied for strategic planning in other settings.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12900616/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146203217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Embedded Research in a Learning Health System: How a Research-Operations Partnership Informed the Development, Implementation, and Scaling of VA's Whole Health System","authors":"Rendelle Bolton,&nbsp;Steven B. Zeliadt,&nbsp;Justeen Hyde,&nbsp;Bella Etingen,&nbsp;Ekaterina Anderson,&nbsp;Anna Barker,&nbsp;Juliet Wu,&nbsp;Benjamin Kligler,&nbsp;Barbara G. Bokhour","doi":"10.1002/lrh2.70062","DOIUrl":"10.1002/lrh2.70062","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Objective&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Embedded research partnerships can advance the implementation of evidence-based policies and practices, including those aligned with person-centered care. Care delivery model transformations, such as the VA's person-centered Whole Health System (WHS), can benefit from an ongoing cycle of program implementation and their evaluation to inform future evolution. This paper describes how embedded researchers partnered with policy makers leading VA's WHS transformation to support its development, implementation, and scaling to illustrate lessons learned for embedded research.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Data Sources and Study Setting&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Fifty-eight embedded research projects were conducted from FY2013 to FY2024.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Study Design&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;We recorded each project's scope, methodology, results, products, and impact. Through a group reflection process, we identified common cross-project lessons that fostered this successful embedded research partnership. Finally, we mapped projects to phases of Kilbourne's Knowledge to Action Framework (Pre-Implementation, Implementation, and Sustainment) to demonstrate how embedded researchers defined evaluation questions, evaluated WHS transformation, and assessed outcomes to inform the implementation and sustainment of VA's WHS transformation.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Data Collection/Extraction Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Projects used multiple qualitative, survey, and large database methods.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Principal Findings&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Across 58 projects, 380 discrete products were used by our operational partner to refine the WHS model, improve implementation support, scale effective practices, inform new policy, and sustain transformation. Three practices cut across these projects to contribute to our successful embedded research partnership: agility, collaboration, and continuous learning and improvement. Additionally, the purpose, questions, and methods of embedded research projects varied as operational partner activities moved across pre-implementation, implementation, and sustainment phases, iteratively impacting WHS transformation.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Embedded research can transform a healthcare system through the timely translation of data into practice, enabling evidence-based policy and practice decisions","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12828349/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146054291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Perspective on Software Intelligence for Autonomous Transformations in Biomedical Data and Knowledge","authors":"Vivek Navale","doi":"10.1002/lrh2.70063","DOIUrl":"https://doi.org/10.1002/lrh2.70063","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Persistent knowledge is essential for propagating the learning health system (LHS) cycle. Integral to the cycle are iterative transformations of data into knowledge. However, human efforts to undertake these transformations are increasingly challenged when dealing with larger data scales and complexities. Data sets within repositories and archives are often underutilized unless specifically requested for research programs. Specialized software algorithms (agents) can use existing knowledge for learning tasks, explore their environment, discover and create goals, and interact with humans.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This paper examines the potential role of software intelligence for autonomous transformations of data and knowledge. Agents can perform various goal-directed tasks. Multi-agent systems can be utilized for data collection, description, preparation, modeling, and knowledge-mining tasks. Knowledge representation, ontologies, semantic web standards, knowledge bases, and graphs can lead to a higher level of directed learning. Agents can develop reasoning abilities and self-generate goals by leveraging semantic relationships between various datasets.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>A conceptual framework for an intelligent biomedical platform (IBP) is proposed. The IBP comprises four layers: infrastructure (IS), user interface (UI), coordination system (CS), and data and knowledge (DK). It also integrates a network of multi-agent systems for clinical decision-making and knowledge-mining tasks. Intelligence in the platform results from the interaction of the IS, UI, CS, and DK agents. These agents can implement multiple inferential steps using the data and knowledge within accessible repositories. Large language models can be integrated with various knowledge resources and domain-specific databases, thereby improving the accuracy of results.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>An IBP supported by a multi-agent system can enhance the autonomous transformation of data and knowledge. Including software intelligence within current repositories and archives enhances data reuse and the generation of new knowledge. With the addition of software reasoning capabilities in biomedical platforms, the LHS cycle can be efficiently propagated to aid in newer biomedical discoveries.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146002549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linking FHIR-Based Medication Data to a Computable Algorithm for Heart Medication Optimization: A Critical Component of Any Medication Learning Health System","authors":"Joey LeGrand,&nbsp;Mohamed S. Ali,&nbsp;Allen Flynn,&nbsp;Juan Arzac,&nbsp;Corey Lester,&nbsp;Michael P. Dorsch","doi":"10.1002/lrh2.70064","DOIUrl":"10.1002/lrh2.70064","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Despite strong evidence supporting guideline-directed medical therapy (GDMT) for heart failure with reduced ejection fraction (HFrEF), a significant gap persists in the consistent application of these therapies. This shortfall has prompted organizations like the American College of Cardiology to recommend leveraging electronic health records (EHR) to optimize GDMT. This paper discusses the development of SmartHF, a clinical decision support system designed to enhance therapy adherence by effectively linking Fast Healthcare Interoperability Resources (FHIR)-based medication data with clinical algorithms tailored for the management of HFrEF.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The SmartHF system integrates FHIR-based medication data with clinical algorithms through a multi-step approach. Central to this process is data from CodeRx, a platform that utilizes streamlined data pipelines to map medication products to their ingredients using RxNorm. The methodology addresses the challenge of interpreting both structured and unstructured medication instructions, ensuring a precise linkage of product identifiers to algorithm-relevant ingredients and their corresponding strengths. Specific attention is given to the data granularity needed for distinguishing precise ingredients within complex formulations, such as sacubitril/valsartan and metoprolol salt form variants.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The deployment of SmartHF involved rigorous testing using actual and synthetic patient datasets to validate its functionality. Results demonstrated the system's ability to process FHIR MedicationRequest data resources accurately, convert free-text dosing instructions into usable formats, and handle edge cases, including non-standard products and missing dose information.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This article describes the potential of FHIR-based medication data integration for enhancing clinical decision support tools and improving care quality. It highlights the challenges and solutions for this integration.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12816155/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146020188","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 Symposium: Charting the Future of Saskatchewan Healthcare","authors":"Charlene Haver,&nbsp;Amir Reza Azizian,&nbsp;Christina Weise,&nbsp;Miranda Cary,&nbsp;Maggie King,&nbsp;Susan Shaw,&nbsp;Kyla Avis,&nbsp;Emiliana Bomfim,&nbsp;Beliz Açan Osman,&nbsp;Gary Groot","doi":"10.1002/lrh2.70060","DOIUrl":"10.1002/lrh2.70060","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>This article shares insights from the Learning Health Systems Symposium, “Charting the Future of Saskatchewan Healthcare: Generating Value within our Health Systems.”</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Patient Partners and over 100 professionals in the fields of research, healthcare, and policy joined the Saskatchewan Centre for Patient-Oriented Research (SCPOR) in Regina, Saskatchewan Canada, for a day filled with knowledge about learning health systems (LHSs). Attendees provided key insights on how to build provincial capacity in LHSs and thereby improve the health of people in Saskatchewan.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Key insights from the symposium included strengthening meaningful partnerships with patients and the community; establishing a shared vision for LHSs; harmonizing conflicting priorities; removing silos; recognizing the natural tensions between academia and the healthcare system; and building on and aligning infrastructure to support LHSs development.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The LHSs symposium provided a way for health system leaders, Patient Partners, researchers, clinicians, and students to develop a common understanding of LHSs and envision a future for LHSs in Saskatchewan, Canada. Key next steps were to strengthen patient and community engagement, work collaboratively to establish a shared vision of LHSs, better understand existing assets to support LHSs, and identify opportunities to build future LHSs infrastructure.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12819043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146031149","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 Southern California Healthcare Delivery Science Center: A Model for Aligning Patients, Healthcare Systems, and Researchers to Develop, Test, and Disseminate Innovations to Achieve Health Equity","authors":"Amytis Towfighi,&nbsp;Aidan R. Lindgren,&nbsp;Thomas A. Buchanan,&nbsp;Hal F. Yee Jr,&nbsp;Allison Orechwa","doi":"10.1002/lrh2.70059","DOIUrl":"10.1002/lrh2.70059","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>A key barrier to translating evidence into practice is the paucity of learning health system (LHS) researchers elucidating and addressing barriers to implementation and maintenance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In response to the growing need for a workforce dedicated to translating evidence into practice, we developed the Southern California Healthcare Delivery Science Center (SC HDSC). In this experience report, we describe the establishment of SC HDSC and its impact to date.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>SC HDSC was established in 2021 to develop and support an LHS researcher workforce that develops, tests, and disseminates innovative interventions to enhance healthcare access, quality of care, and health outcomes. The Center leverages academic-public sector partnerships and has an overarching goal of achieving health equity. The conceptual model for SC HDSC involves the solicitation of priorities from patients, families, healthcare system leaders, frontline staff, and researchers; support of interdisciplinary teams to design, implement, and evaluate interventions addressing these priorities; and the creation of toolkits for dissemination of effective interventions. SC HDSC provides grant funding, training, networking, team building, consultations, and career building opportunities for LHS researchers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>SC HDSC may offer a new model for educating LHS researchers and building a broader community dedicated to translating evidence into practice.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12900611/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146203243","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}