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":"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":"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":"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}

{"title":"The Right Stuff: Getting the right data at the right time and using that data to drive evidence-based practice and policy","authors":"Lucy A. Savitz,&nbsp;Sarah M. Greene,&nbsp;Michael K. Gould,&nbsp;Harold S. Luft","doi":"10.1002/lrh2.10432","DOIUrl":"https://doi.org/10.1002/lrh2.10432","url":null,"abstract":"<p>When researchers are embedded within healthcare systems and collaborate with practitioners and operational leaders, they may be able to rapidly identify problems and opportunities that can be addressed to improve quality and affordability. While other industries have well-developed data exploration processes (e.g., banking), healthcare is still developing its methods with widely varying data sources, huge quantities of unstructured data, uncertain precision in measurement, uncertainties about data quality, and complicated and stringent regulations and policies on data access. In recognition of these challenges, the AcademyHealth Learning Health System (LHS) Interest Group (In 2021, <i>Learning Health Systems</i> journal established a formal relationship with AcademyHealth, serving as the official journal of its LHS Interest Group.) released a call for papers in June 2023 to focus on challenges encountered by investigators related to the use of real-world data in embedded research.</p><p>We use the term “embedded researcher” to characterize a broad range of people well-trained in research methods using real-world data. Being located inside a health system, they often have privileged access to data and the practitioners who may be observing new situations, problems, or opportunities for improvement. Unlike colleagues only involved in internal quality improvement efforts, embedded researchers also seek to broadly share their findings and create generalizable knowledge. The sharing is less focused on the specific findings—too many things may be unique about the setting, people, and other factors to be directly generalizable. The challenges faced and techniques used to overcome them, however, may offer important lessons for other embedded researchers.</p><p>As LHSs mature and internally tackle increasingly complex problems with embedded research, the challenges presented in using real-world data for locally applied health services research are important to understand. Taken together, the papers in this Special Issue offer insights into the frontiers of embedded research as LHSs embark on their own learning journey. Accelerating the transformation of data to knowledge requires an understanding of the underlying data and techniques needed to draw useful lessons from the data. Sharing experiences across teams and settings will help others in anticipating and addressing the challenges they are likely to encounter.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 S1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10432","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326796","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":"Patient care in complex Sociotechnological ecosystems and learning health systems","authors":"Shin-Ping Tu,&nbsp;Brittany Garcia,&nbsp;Xi Zhu,&nbsp;Daniel Sewell,&nbsp;Vimal Mishra,&nbsp;Khalid Matin,&nbsp;Alan Dow","doi":"10.1002/lrh2.10427","DOIUrl":"10.1002/lrh2.10427","url":null,"abstract":"<p>The learning health system (LHS) model was proposed to provide real-time, bi-directional flow of learning using data captured in health information technology systems to deliver rapid learning in healthcare delivery. As highlighted by the landmark National Academy of Medicine report “Crossing the Quality Chasm,” the U.S. healthcare delivery industry represents complex adaptive systems, and there is an urgent need to develop innovative methods to identify efficient team structures by harnessing real-world care delivery data found in the electronic health record (EHR). We offer a discussion surrounding the complexities of team communication and how solutions may be guided by theories such as the Multiteam System (MTS) framework and the Multitheoretical Multilevel Framework of Communication Networks. To advance healthcare delivery science and promote LHSs, our team has been building a new line of research using EHR data to study MTS in the complex real world of cancer care delivery. We are developing new network metrics to study MTSs and will be analyzing the impact of EHR communication network structures on patient outcomes. As this research leads to patient care delivery interventions/tools, healthcare leaders and healthcare professionals can effectively use health IT data to implement the most evidence-based collaboration approaches in order to achieve the optimal LHS and patient outcomes.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 S1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10427","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141103173","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":"Overcoming challenges in real-world evidence generation: An example from an Adult Medical Care Coordination program","authors":"Samuel T. Savitz,&nbsp;Michelle A. Lampman,&nbsp;Shealeigh A. Inselman,&nbsp;Ruchita Dholakia,&nbsp;Vicki L. Hunt,&nbsp;Angela B. Mattson,&nbsp;Robert J. Stroebel,&nbsp;Pamela J. McCabe,&nbsp;Stephanie G. Witwer,&nbsp;Bijan J. Borah","doi":"10.1002/lrh2.10430","DOIUrl":"10.1002/lrh2.10430","url":null,"abstract":"<p>The Adult Medical Care Coordination program (“the program”) was implemented at Mayo Clinic to promote patient self-management and improve 30-day unplanned readmission for patients with high risk for readmission after hospital discharge. This study aimed to evaluate the impact of the program compared to usual care using a pragmatic, stepped wedge cluster randomized trial (“stepped wedge trial”). However, several challenges arose including large differences between the study arms. Our goal is to describe the challenges and present lessons learned on how to overcome such challenges and generate evidence to support practice decisions. We describe the challenges encountered during the trial, the approach to addressing these challenges, and lessons learned for other learning health system researchers facing similar challenges. The trial experienced several challenges in implementation including several clinics dropping from the study and care disruptions due to COVID-19. Additionally, there were large differences in the patient population between the program and usual care arms. For example, the mean age was 76.8 for the program and 68.1 for usual care. Due to these differences, we adapted the methods using the propensity score matching approach that is traditionally applied to observational designs and adjusted for differences in observable characteristics. When conducting pragmatic research, researchers will encounter factors beyond their control that may introduce bias. The lessons learned include the need to weigh the tradeoffs of pragmatic design elements and the potential value of adaptive designs for pragmatic trials. Applying these lessons would promote the successful generation of evidence that informs practice decisions.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 S1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10430","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141111069","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 incident reports to diagnose communication challenges for precision intervention in learning health systems: A methods paper","authors":"Rebecca R. S. Clark,&nbsp;Tamar Klaiman,&nbsp;Kathy Sliwinski,&nbsp;Rebecca F. Hamm,&nbsp;Emilia Flores","doi":"10.1002/lrh2.10425","DOIUrl":"10.1002/lrh2.10425","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Poor communication is a leading root cause of preventable maternal mortality in the United States. Communication challenges are compounded with the presence of biases, including racism. Hospital administrators and clinicians are often aware that communication is a problem, but understanding where to intervene can be difficult to determine. While clinical leadership routinely reviews incident reports and acts on them to improve care, we hypothesized that reviewing incident reports in a systematic way might reveal thematic patterns, providing targeted opportunities to improve communication in direct interaction with patients and within the healthcare team itself.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We abstracted incident reports from the Women's Health service and linked them with patient charts to join patient's race/ethnicity, birth outcome, and presence of maternal morbidity and mortality to the incident report. We conducted a qualitative content analysis of incident reports using an inductive and deductive approach to categorizing communication challenges. We then described the intersection of different types of communication challenges with patient race/ethnicity and morbidity outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The use of incident reports to conduct research on communication was new for the health system. Conversations with health system-level stakeholders were important to determine the best way to manage data. We developed a thematic codebook based on prior research in healthcare communication. We found that we needed to add codes that were equity focused, as this was missing from the existing codebook. We also found that clinical and contextual expertise was necessary for conducting the analysis—requiring more resources to conduct coding than initially estimated. We shared our findings back with leadership iteratively during the work.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Incident reports represent a promising source of health system data for rapid improvement to transform organizational practice around communication. There are barriers to conducting this work in a rapid manner, however, that require further iteration and innovation.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 S1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10425","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140997446","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":"Building to learn: Information technology innovations to enable rapid pragmatic evaluation in a learning health system","authors":"Geetanjali Rajamani,&nbsp;Genevieve B. Melton,&nbsp;Deborah L. Pestka,&nbsp;Maya Peters,&nbsp;Iva Ninkovic,&nbsp;Elizabeth Lindemann,&nbsp;Timothy J. Beebe,&nbsp;Nathan Shippee,&nbsp;Bradley Benson,&nbsp;Abraham Jacob,&nbsp;Christopher Tignanelli,&nbsp;Nicholas E. Ingraham,&nbsp;Joseph S. Koopmeiners,&nbsp;Michael G. Usher","doi":"10.1002/lrh2.10420","DOIUrl":"10.1002/lrh2.10420","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Learning health systems (LHSs) iteratively generate evidence that can be implemented into practice to improve care and produce generalizable knowledge. Pragmatic clinical trials fit well within LHSs as they combine real-world data and experiences with a degree of methodological rigor which supports generalizability.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>We established a pragmatic clinical trial unit (“RapidEval”) to support the development of an LHS. To further advance the field of LHS, we sought to further characterize the role of health information technology (HIT), including innovative solutions and challenges that occur, to improve LHS project delivery.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>During the period from December 2021 to February 2023, eight projects were selected out of 51 applications to the RapidEval program, of which five were implemented, one is currently in pilot testing, and two are in planning. We evaluated pre-study planning, implementation, analysis, and study closure approaches across all RapidEval initiatives to summarize approaches across studies and identify key innovations and learnings by gathering data from study investigators, quality staff, and IT staff, as well as RapidEval staff and leadership.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Implementation (Results)</h3>\u0000 \u0000 <p>Implementation approaches spanned a range of HIT capabilities including interruptive alerts, clinical decision support integrated into order systems, patient navigators, embedded micro-education, targeted outpatient hand-off documentation, and patient communication. Study approaches include pre-post with time-concordant controls (1), randomized stepped-wedge (1), cluster randomized across providers (1) and location (3), and simple patient level randomization (2).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Study selection, design, deployment, data collection, and analysis required close collaboration between data analysts, informaticists, and the RapidEval team.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10420","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140697315","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}