Learning Health Systems最新文献

{"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}

{"title":"System-failing creativity in health care","authors":"Stijn Horck,&nbsp;Rachel E. Gifford,&nbsp;Bram P. I. Fleuren,&nbsp;Cheryl Rathert,&nbsp;Tracy H. Porter,&nbsp;Afshan Rauf,&nbsp;Yuna S. H. Lee","doi":"10.1002/lrh2.10437","DOIUrl":"10.1002/lrh2.10437","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Health care professionals often generate novel solutions to solve problems during day-to-day patient care. However, less is known about generating novel and useful (i.e., creative) ideas in the face of health care system failure. System failures are high-impact and increasingly frequent events in health care organizations, and front-line professionals may have uniquely valuable expertise to address such occurrences.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Our interdisciplinary team, blending expertise in health care management, economics, psychology, and clinical practice, reviewed the literature on creativity and system failures in health care to generate a conceptual model that describes this process. Drawing on appraisal theory, we iteratively refined the model by integrating various theories with key concepts of system failures, creativity, and health care worker's well-being.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The SFC model provides a conceptualization of creativity from front-line care professionals as it emerges in situations of failure or crisis. It describes the pathways by which professionals respond proactively to a systems failure with creative ideas to effectively address the situation and affect these workers' well-being.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our conceptual model guides health care managers and leaders to use managerial practices to shape their systems and support creativity, especially when facing system failures. It introduces a framework for examining system-failing creativity (SFC) and general creativity, aiming to improve health care quality, health care workers' well-being, and organizational outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11733441/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013346","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 Complete Inpatient Record Using Comprehensive Electronic Data (CIRCE) project: A team-based approach to clinically validated, research-ready electronic health record data","authors":"Andrea L. C. Schneider,&nbsp;Jennifer C. Ginestra,&nbsp;Meeta Prasad Kerlin,&nbsp;Michael G. S. Shashaty,&nbsp;Todd A. Miano,&nbsp;Daniel S. Herman,&nbsp;Oscar J. L. Mitchell,&nbsp;Rachel Bennett,&nbsp;Alexander T. Moffett,&nbsp;John Chandler,&nbsp;Atul Kalanuria,&nbsp;Zahra Faraji,&nbsp;Nicholas S. Bishop,&nbsp;Benjamin Schmid,&nbsp;Angela T. Chen,&nbsp;Kathryn H. Bowles,&nbsp;Thomas Joseph,&nbsp;Rachel Kohn,&nbsp;Rachel R. Kelz,&nbsp;George L. Anesi,&nbsp;Monisha Kumar,&nbsp;Ari B. Friedman,&nbsp;Emily Vail,&nbsp;Nuala J. Meyer,&nbsp;Blanca E. Himes,&nbsp;Gary E. Weissman","doi":"10.1002/lrh2.10439","DOIUrl":"10.1002/lrh2.10439","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>The rapid adoption of electronic health record (EHR) systems has resulted in extensive archives of data relevant to clinical research, hospital operations, and the development of learning health systems. However, EHR data are not frequently available, cleaned, standardized, validated, and ready for use by stakeholders. We describe an in-progress effort to overcome these challenges with cooperative, systematic data extraction and validation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A multi-disciplinary team of investigators collaborated to create the Complete Inpatient Record Using Comprehensive Electronic Data (CIRCE) Project dataset, which captures EHR data from six hospitals within the University of Pennsylvania Health System. Analysts and clinical researchers jointly iteratively reviewed SQL queries and their output to validate desired data elements. Data from patients aged ≥18 years with at least one encounter at an acute care hospital or hospice occurring since 7/1/2017 were included. The CIRCE Project includes three layers: (1) raw data comprised of direct SQL query output, (2) cleaned data with errors removed, and (3) transformed data with standardized implementations of commonly used case definitions and clinical scores.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Between July 1, 2017 and December 31, 2023, the dataset captured 1 629 920 encounters from 740 035 patients. Most encounters were emergency department only visits (<i>n</i> = 965 834, 59.3%), followed by inpatient admissions without an intensive care unit admission (<i>n</i> = 518 367, 23.7%). The median age was 46.9 years (25th–75th percentiles = 31.1–64.7) at the time of the first encounter. Most patients were female (<i>n</i> = 418 303, 56.5%), a significant proportion were of non-White race (<i>n</i> = 272 018, 36.8%), and 54 625 (7.4%) were of Hispanic/Latino ethnicity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The CIRCE Project represents a novel cooperative research model to capture clinically validated EHR data from a large diverse academic health system in the greater Philadelphia region and is designed to facilitate collaboration and data sharing to support learning health system activities. Ultimately, these data will be de-identified and converted to a publicly available resource.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11733450/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013272","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":"Validating a novel measure for assessing patient openness and concerns about using artificial intelligence in healthcare","authors":"Bryan A. Sisk,&nbsp;Alison L. Antes,&nbsp;Sunny C. Lin,&nbsp;Paige Nong,&nbsp;James M. DuBois","doi":"10.1002/lrh2.10429","DOIUrl":"10.1002/lrh2.10429","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>Patient engagement is critical for the effective development and use of artificial intelligence (AI)-enabled tools in learning health systems (LHSs). We adapted a previously validated measure from pediatrics to assess adults' openness and concerns about the use of AI in their healthcare.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>Cross-sectional survey.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We adapted the 33-item “Attitudes toward Artificial Intelligence in Healthcare for Parents” measure for administration to adults in the general US population (AAIH-A), recruiting participants through Amazon's Mechanical Turk (MTurk) crowdsourcing platform. AAIH-A assesses openness to AI-driven technologies and includes 7 subscales assessing participants' openness and concerns about these technologies. The openness scale includes examples of AI-driven tools for diagnosis, prediction, treatment selection, and medical guidance. Concern subscales assessed privacy, social justice, quality, human element of care, cost, shared decision-making, and convenience. We co-administered previously validated measures hypothesized to correlate with openness. We conducted a confirmatory factor analysis and assessed reliability and construct validity. We performed exploratory multivariable regression models to identify predictors of openness.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>A total of 379 participants completed the survey. Confirmatory factor analysis confirmed the seven dimensions of the concerns, and the scales had internal consistency reliability, and correlated as hypothesized with existing measures of trust and faith in technology. Multivariable models indicated that trust in technology and concerns about quality and convenience were significantly associated with openness.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The AAIH-A is a brief measure that can be used to assess adults' perspectives about AI-driven technologies in healthcare and LHSs. The use of AAIH-A can inform future development and implementation of AI-enabled tools for patient care in the LHS context that engage patients as key stakeholders.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10429","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141349079","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":"Enhancing data practices for Whole Health: Strategies for a transformative future","authors":"Lei Guo,&nbsp;Kavitha P. Reddy,&nbsp;Theresa Van Iseghem,&nbsp;Whitney N. Pierce","doi":"10.1002/lrh2.10426","DOIUrl":"https://doi.org/10.1002/lrh2.10426","url":null,"abstract":"<p>We explored the challenges and solutions for managing data within the Whole Health System (WHS), which operates as a Learning Health System and a patient-centered healthcare approach that combines conventional and complementary approaches. Addressing these challenges is critical for enhancing patient care and improving outcomes within WHS. The proposed solutions include prioritizing interoperability for seamless data exchange, incorporating patient-centered comparative clinical effectiveness research and real-world data to personalize treatment plans and validate integrative approaches, and leveraging advanced data analytics tools to incorporate patient-reported outcomes, objective metrics, robust data platforms. Implementing these measures will enable WHS to fulfill its mission as a holistic and patient-centered healthcare model, promoting greater collaboration among providers, boosting the well-being of patients and providers, and improving patient outcomes.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 S1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10426","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326698","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}