Learning Health Systems最新文献

{"title":"Community-led transformation principles: Transforming public health learning systems by centering authentic collaboration with community-based organizations","authors":"Reba Meigs,&nbsp;Amina Sheik Mohamed,&nbsp;Adriana Bearse,&nbsp;Sarah Vicente,&nbsp;Nghi Dang,&nbsp;Asmaa Deiranieh,&nbsp;Reem Zubaidi,&nbsp;Valerie Nash,&nbsp;Maliha Ali,&nbsp;Trenita Childers,&nbsp;Mohammad Wahdatyar,&nbsp;Emily Treichler,&nbsp;Blanca Meléndrez","doi":"10.1002/lrh2.10451","DOIUrl":"10.1002/lrh2.10451","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>In the face of evolving public health challenges, including emerging diseases, pervasive health disparities, and escalating environmental threats, the integration of learning health system (LHS) principles emerges as a vital strategy for enhancing the adaptability and efficacy of public health initiatives. Traditional approaches within these systems often overlook the potential to deeply involve community-based organizations (CBO) that are led and staffed by the communities they serve as equal and essential partners in the public health discourse.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This commentary proposes a suite of nine community-led transformation (CLT) principles aimed at reimagining LHS frameworks to authentically incorporate CBOs at their core. Drawing on the experiences from initiatives supporting Afghan refugees, we illustrate the application of these principles through two detailed case studies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>These examples demonstrate the CLT principles in action and spotlight the enhanced cultural competency, effectiveness, and equitable power distribution that arise from such partnerships. Centering small to mid-sized CBOs including ethnic-led and/or faith based within LHS structures enables the system to access invaluable cultural insights, strengthen community bonds, and empower those communities to spearhead their transformative journey toward sustainable health, equity, and well-being improvements.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The CLT principles herald a shift toward a more inclusive and co-led public health paradigm by offering a blueprint for stakeholders eager to forge strong, trust-based coalitions and cocreate initiatives with community leaders including Black, Indigenous, and People of Color (BIPOC) leaders from ethnic-led and/or faith-based CBOs. By embracing these principles, public health systems can evolve into truly inclusive, responsive, and sustainable entities poised to advance health equity for all community members.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11493544/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510007","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 conceptual learning analysis of paired after action and intra action reviews for health emergencies","authors":"Elliot Brennan,&nbsp;Seye Abimbola","doi":"10.1002/lrh2.10447","DOIUrl":"10.1002/lrh2.10447","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Processes of self-reflection and the learning they allow are crucial before, during, and after acute emergencies, including infectious disease outbreaks. Tools—such as Action Reviews—offer World Health Organization (WHO) member states a platform to enhance learning. We sought to better understand the value of these tools and how they may be further refined and better used.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We searched the publicly available WHO Strategic Partnership for Health Security website for paired reports of Action Reviews, that is, reports with a comparable follow-up report. We complemented the paired action reviews, with a literature search, including the gray literature. The paired action reviews were analyzed using the “Learning Health Systems” framework.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We identified three paired action reviews: Lassa Fever After Action Reviews (AARs) in Nigeria (2017 and 2018), COVID-19 Intra-Action Reviews (IARs) in Botswana (2020 and 2021), and COVID-19 IARs in South Sudan (2020 and 2021). Action Reviews allowed for surfacing relevant knowledge and, by engaging the right (in different contexts) actors, asking “are we doing things right?” (single loop learning) was evident in all the reports. Single loop learning is often embedded within examples of double loop learning (“are we doing the right things?”), providing a more transformative basis for policy change. Triple loop learning (“are we learning right”?) was evident in AARs, and less in IARs. The range of participants involved, the level of concentrated focus on specific issues, the duration available for follow through, and the pressures on the health system to respond influenced the type (i.e., loop) and the effectiveness of learning.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Action Reviews, by design, surface knowledge. With favorable contextual conditions, this knowledge can then be applied and lead to corrective and innovative actions to improve health system performance, and in exceptional cases, continuous learning.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11493552/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510004","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":"Leveraging public health cancer surveillance capacity to develop and support a rural cancer network","authors":"Jason Semprini,&nbsp;Ingrid M. Lizarraga,&nbsp;Aaron T. Seaman,&nbsp;Erin C. Johnson,&nbsp;Madison M. Wahlen,&nbsp;Jessica S. Gorzelitz,&nbsp;Sarah A. Birken,&nbsp;Mary C. Schroeder,&nbsp;Tarah Paulus,&nbsp;Mary E. Charlton","doi":"10.1002/lrh2.10448","DOIUrl":"10.1002/lrh2.10448","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>As the rural–urban cancer mortality gap widens, centering care around the needs of rural patients presents an opportunity to advance equity. One barrier to delivering patient-centered care at rural hospitals stems from limited analytic capacity to leverage data and monitor patient outcomes. This case study describes the experience of a public health cancer surveillance system aiming to fill this gap within the context of a rural cancer network.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>To support the implementation of a novel network model intervention in Iowa, the Iowa Cancer Registry began generating hospital-specific and catchment area reports. Then, the Iowa Cancer Registry supported adapting the network model to fit the context of Iowa's cancer care delivery system by performing data monitoring and reporting functions. Informed by a gap analysis, the Iowa Cancer Registry then identified which quality accreditation standards could be achieved with public health surveillance data and analytic support.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The network intervention in Iowa supported 5 rural cancer centers across the state, each concurrently pursuing quality accreditation standards. The Iowa Cancer Registry's hospital and catchment-specific reports illuminated the cancer burden and needs of rural cancer centers within the network. Our team identified 19 (of the 36 total) quality standards that can be supported by public health surveillance functions typically performed by the registry. These standards encompassed data-driven quality improvement, patient monitoring, and reporting guideline-concordant care standards.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>As rural hospitals continue to face resource constraints, multisectoral efforts informed by data from centralized public health surveillance systems can promote quality improvement initiatives across rural communities. While our work remains preliminary, we predict that analytic support provided by the Iowa Cancer Registry will enable the rural network hospitals to focus their capacity toward developing the infrastructure necessary to deliver high-quality care and serve the unique needs of rural cancer patients.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11493549/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510011","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":"US public health surveillance, reimagined","authors":"Elina Guralnik","doi":"10.1002/lrh2.10445","DOIUrl":"10.1002/lrh2.10445","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>This study presents two novel concepts for standardizing electronic health records (EHR)-based public health surveillance through utilization of existing informatics methods and data platforms.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Drawing from the collective experience in applied epidemiology, health services research and health informatics, the author presents a vision for an alternative path to public health surveillance by repurposing existing tools and resources, such as (1) computable phenotypes which have already been created and validated for a variety of chronic diseases of interest to public health and (2) large data platforms/collaboratives, such as All of Us Research Program and National COVID Cohort Collaborative. Opportunities and challenges are discussed regarding EHR-based chronic disease surveillance, as well as the concept of phenotype definitions and large data platforms reuse for public health needs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results/Framework</h3>\u0000 \u0000 <p>Reusing of computable phenotypes for EHR-based public health surveillance would require secure data platforms and nationally representative data. Standardization metrics for reuse of previously developed and validated computable phenotypes are also necessary and are currently being developed by the author. This study presents a reimagined Learning Health System framework by incorporating Public Health and two novel concept sets of solutions into the healthcare ecosystem.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion/Next Steps</h3>\u0000 \u0000 <p>Alternative approaches to limited resources and current infrastructure of the US Public Health System, especially as applied to disease surveillance, are needed and may be possible when repurposing the resources and methodologies across the Learning Health System.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11493541/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510014","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 model of academic-practice collaboration for facilitating informatics capacity and building a learning health system framework in public health","authors":"Sripriya Rajamani,&nbsp;Sarah Solarz,&nbsp;Miriam Halstead Muscoplat,&nbsp;Aasa Dahlberg Schmit,&nbsp;Ann Gonderinger,&nbsp;Chris Brueske,&nbsp;Jennifer Fritz,&nbsp;Emily Emerson,&nbsp;Genevieve B. Melton","doi":"10.1002/lrh2.10446","DOIUrl":"10.1002/lrh2.10446","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background and Objective</h3>\u0000 \u0000 <p>The data modernization initiative (DMI) is a multi-year, multi-billion-dollar endeavor toward a robust public health information infrastructure. The various DMI projects (interoperability, analytics, workforce, governance) present an opportunity for a learning health system (LHS) framework in public health. The objective is to share an academic-practice partnership model between the University of Minnesota (UMN) and the Minnesota Department of Health (MDH) in advancing public health informatics (PHI) and its relationship to an LHS model.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The UMN-MDH partnership was conceptualized in 2018 as a 1-year pilot with annual renewals through a time/cost-sharing faculty position with PHI expertise. The partnership focus was decided based on MDH's needs and mutual interests, with the core collaborating faculty (SR) being an embedded researcher at MDH. Responsibilities included supporting electronic case reporting (eCR), interoperability projects, and assisting MDH staff with PHI presentations/publications. The partnership has expanded to PHI workforce development through a national grant and now includes an interest in applying the LHS framework to MDH-DMI work.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The MDH-DMI team has embarked upon 13 projects for assessment through an LHS approach: systems interoperability projects between MDH and healthcare/local public health (<i>n</i> = 6); systems modernization for MDH programs (<i>n</i> = 5); informatics workforce development (<i>n</i> = 1); and program governance (<i>n</i> = 1). Each project has been evaluated and/or has current/future assessment plans to synthesize learnings and create a feedback loop for iterative improvement. The partnership has been mutually beneficial as it met agreed upon metrics across both institutions. The program's productivity is showcased with shared authorship in 10 peer-reviewed proceedings/publications, 22 presentations and 16 posters across local/national conferences.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The current case report of the UMN-MDH partnership is a relatively recent exemplar to support tangible LHS demonstration in public health. Building LHS momentum at MDH and other public health entities will require LHS champion(s) and continued academic collaboration.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11493548/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510005","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":"2023 MCBK global meeting—Lightning talk abstracts","authors":"","doi":"10.1002/lrh2.10443","DOIUrl":"10.1002/lrh2.10443","url":null,"abstract":"&lt;p&gt;Muhammad Afzal, School of Computing and Digital Technology, Birmingham City University&lt;/p&gt;&lt;p&gt;&lt;span&gt;[email protected]&lt;/span&gt;&lt;/p&gt;&lt;p&gt;Contemporary scientific communication relies heavily on document-based systems like journal articles, books, and reports for sharing research findings. However, large documents limit opportunities for efficient knowledge dissemination due to limitation in processing of different subsections within a document to understand the meaning of information units. This research aims to develop a smart repository that moves beyond documents and introduces smaller, computable units of knowledge. By assessing biomedical data sources, we will build a repository to make scientific knowledge more representable, computable, and shareable. The rationale is to enhance how researchers communicate and manage information in the rapidly evolving digital era.&lt;/p&gt;&lt;p&gt;The work focuses on developing a new repository that goes beyond the document-based paradigm by fusing biomedical and health and life sciences data sources, such as PubMed Central. New protocols and methods will be designed to identify relevant sections in the documents to extract smaller knowledge units. The proposed repository with key features storage, retrieval, representation, and sharing will be optimized for the granular units. Integration strategies with existing platforms like PubMed will be devised. Usability testing will refine the interface to boost engagement. Interoperability mechanisms will ensure compatibility with existing systems.&lt;/p&gt;&lt;p&gt;By enabling scientific knowledge to be shared in smaller units, this repository has the potential to revolutionize scientific communication and collaboration. Breaking down information into granular components is expected to create new opportunities for innovation, discovery, and the development of advanced analytics tools. The repository will facilitate efficient access to health evidence, benefiting researchers, clinicians in creating systematic reviewers that require rapid evidence synthesis. Further, the computable units extracted from documents could be modeled into interoperable resources like FHIR, thereby support the Evidence Based Medicine on FHIR (EBMonFHIR) project is extending FHIR to provide a standard for machine-interpretable exchange of scientific knowledge. This would also allow developers to build innovative AI systems for objectives such as diagnostic and treatment support.&lt;/p&gt;&lt;p&gt;By reducing the need for manual effort in finding and formatting evidence, the repository will pave the way for automating knowledge synthesis and management and will empower various stakeholders with enhanced efficiency, interoperability, and analytical capabilities to progress research and practice.&lt;/p&gt;&lt;p&gt;Miguel Aljibe, University of the Philippines&lt;/p&gt;&lt;p&gt;&lt;span&gt;[email protected]&lt;/span&gt;&lt;/p&gt;&lt;p&gt;Alvin Marcelo, University of the Philippines-Manila&lt;/p&gt;&lt;p&gt;&lt;span&gt;[email protected]&lt;/span&gt;&lt;/p&gt;&lt;p&gt;Janus Ong, University of the Philippines-Manila","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10443","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141672062","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 system research as a catalyst for promoting physician wellness: EHR InBasket Spring cleaning and team-based compassion practice","authors":"Ming Tai-Seale,&nbsp;Amanda Walker,&nbsp;Yuwei Cheng,&nbsp;Nathan Yung,&nbsp;Sophie Webb,&nbsp;Ottar Lunde,&nbsp;David Bazzo,&nbsp;Ammar Mandvi,&nbsp;Neal Doran,&nbsp;Gene Kallenberg,&nbsp;Christopher A. Longhurst,&nbsp;Sidney Zisook,&nbsp;Thomas J. Savides,&nbsp;Marlene Millen,&nbsp;Lin Liu","doi":"10.1002/lrh2.10444","DOIUrl":"10.1002/lrh2.10444","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Addressing physician burnout is critical for healthcare systems. As electronic health record (EHR) workload and teamwork have been identified as major contributing factors to physician well-being, we aimed to mitigate burnout through EHR-based interventions and a compassion team practice (CTP), targeting EHR workload and team cohesion.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A modified stepped wedge-clustered randomized trial was conducted, involving specialties with heavy InBasket workloads. EHR interventions included quick-action shortcuts and recommended practice for secure chats. The CTP comprised 30-s practice between physicians and their dyad partners. Survey and EHR data were collected over four assessment periods. Linear and generalized mixed-effects models assessed intervention effects, accounting for covariates.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Forty-four physicians participated (20% participation rate). While burnout prevalence decreased from 58.5% at baseline to 50.0% at the end of the study, burnout reduction was not statistically significant after EHR (OR 0.43, 95% CI 0.12 to 1.61, <i>p</i> = 0.21) or EHR + CTP (OR 0.60, 95% CI 0.17 to 2.10, <i>p</i> = 0.42) interventions. Statistically significant greater perceived ease of EHR work resulted from both the EHR intervention (coefficient 0.76, 95% CI 0.22 to 1.29, <i>p</i> = 0.01) and EHR + CTP intervention (coefficient 0.80, 95% CI 0.26 to 1.35, <i>p</i> &lt; 0.01). EHR + CTP increased perceived workplace supportiveness (coefficient 0.61, 95% CI −0.04 to 1.26, <i>p</i> = 0.07). Total number of InBasket messages/week increased significantly after EHR interventions (coefficient = 27.4, 95% CI 6.69 to 48.1, <i>p</i> = 0.011) and increased after EHR + CTP (18.5, 95% CI −3.15 to 40.2, <i>p</i> = 0.097).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>While burnout reduction was not statistically significant, EHR interventions positively impacted workload perceptions. CTP showed potential for improving perceived workplace supportiveness. Further research is needed to explore the efficacy of CTP with more participants. The interventions gained interest beyond our institution and prompted consideration for broader implementation.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10444","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141676390","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":"2023 Inaugural Healthcare Delivery Science: Innovation and Partnerships for Health Equity Research (DESCIPHER) Symposium","authors":"Allison Z. Orechwa,&nbsp;Anshu Abhat,&nbsp;Lilyana Amezcua,&nbsp;Bernadette Boden-Albala,&nbsp;Thomas A. Buchanan,&nbsp;Steve Chen,&nbsp;Lauren P. Daskivich,&nbsp;Brett Feldman,&nbsp;Michael K. Gould,&nbsp;Wei-an Lee,&nbsp;Christopher Lynch,&nbsp;Carolyn C. Meltzer,&nbsp;Brian S. Mittman,&nbsp;Margarita Pereyda,&nbsp;Evan Raff,&nbsp;Jehni Robinson,&nbsp;Sonali Saluja,&nbsp;Barbara J. Turner,&nbsp;Breena R. Taira,&nbsp;Rebecca Trotzky-Sirr,&nbsp;Linda Williams,&nbsp;Shinyi Wu,&nbsp;Hal Yee Jr.,&nbsp;Amytis Towfighi","doi":"10.1002/lrh2.10442","DOIUrl":"10.1002/lrh2.10442","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>This article provides an overview of presentations and discussions from the inaugural Healthcare Delivery Science: Innovation and Partnerships for Health Equity Research (DESCIPHER) Symposium.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The symposium brought together esteemed experts from various disciplines to explore models for translating evidence-based interventions into practice.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The symposium highlighted the importance of disruptive innovation in healthcare, the need for multi-stakeholder engagement, and the significance of family and community involvement in healthcare interventions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The article concluded with a call to action for advancing healthcare delivery science to achieve health equity.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10442","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141679467","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":"Using gaming to prepare health professional students to practice systems thinking","authors":"Sarah Watts,&nbsp;Rachel Helms,&nbsp;Erin Thornton,&nbsp;Andrew D. Fruge,&nbsp;Clay Young,&nbsp;Jeanna Sewell","doi":"10.1002/lrh2.10441","DOIUrl":"10.1002/lrh2.10441","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Healthcare professionals face numerous challenges regarding the delivery of care. Creating solutions to these challenges is imperative to improve the quality and safety of care to positively impact patient outcomes. However, health professional students rarely receive formal training regarding systems thinking during didactic components of their professional training.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Aims, Materials, and Methods</h3>\u0000 \u0000 <p>Thus, our institution incorporated the Friday Night at the ER (FNER) activity in the interprofessional education curricula to provide an experiential learning experience focused on systems thinking.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>A total of 1033 students (483 nursing, 289 pharmacy, 48 nutrition, 26 speech–language pathology, and 187 preferred not to share their discipline or complete the survey) participated in FNER across six separate semester cohorts. The Systems Thinking Scale was completed immediately before and after FNER by 81.5% (pre) and 80.3% (post) of students. Repeated measures ANOVA was conducted and noted combined nursing, pharmacy, nutrition, and speech–language pathology students' total Systems Thinking Scale scores increased significantly from pretest (<i>M</i> = 82.8, SD = 10.6) to posttest (<i>M</i> = 89.7, SD = 10.8), <i>p</i> &lt; 0.001.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>Our experience indicates this is a meaningful interprofessional activity that prepares students to practice systems thinking in their future careers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Practicing these skills has the potential to improve learners' ability to implement changes that will positively impact healthcare systems.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11733448/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013650","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":"Diagnostic accuracy of GPT-4 on common clinical scenarios and challenging cases","authors":"Geoffrey W. Rutledge","doi":"10.1002/lrh2.10438","DOIUrl":"https://doi.org/10.1002/lrh2.10438","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Large language models (LLMs) have a high diagnostic accuracy when they evaluate previously published clinical cases.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We compared the accuracy of GPT-4's differential diagnoses for previously unpublished challenging case scenarios with the diagnostic accuracy for previously published cases.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>For a set of previously unpublished challenging clinical cases, GPT-4 achieved 61.1% correct in its top 6 diagnoses versus the previously reported 49.1% for physicians. For a set of 45 clinical vignettes of more common clinical scenarios, GPT-4 included the correct diagnosis in its top 3 diagnoses 100% of the time versus the previously reported 84.3% for physicians.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>GPT-4 performs at a level at least as good as, if not better than, that of experienced physicians on highly challenging cases in internal medicine. The extraordinary performance of GPT-4 on diagnosing common clinical scenarios could be explained in part by the fact that these cases were previously published and may have been included in the training dataset for this LLM.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10438","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730353","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}