Learning Health Systems最新文献

{"title":"Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system","authors":"Yuan Luo,&nbsp;Chengsheng Mao,&nbsp;Lazaro N. Sanchez-Pinto,&nbsp;Faraz S. Ahmad,&nbsp;Andrew Naidech,&nbsp;Luke Rasmussen,&nbsp;Jennifer A. Pacheco,&nbsp;Daniel Schneider,&nbsp;Leena B. Mithal,&nbsp;Scott Dresden,&nbsp;Kristi Holmes,&nbsp;Matthew Carson,&nbsp;Sanjiv J. Shah,&nbsp;Seema Khan,&nbsp;Susan Clare,&nbsp;Richard G. Wunderink,&nbsp;Huiping Liu,&nbsp;Theresa Walunas,&nbsp;Lee Cooper,&nbsp;Feng Yue,&nbsp;Firas Wehbe,&nbsp;Deyu Fang,&nbsp;David M. Liebovitz,&nbsp;Michael Markl,&nbsp;Kelly N. Michelson,&nbsp;Susanna A. McColley,&nbsp;Marianne Green,&nbsp;Justin Starren,&nbsp;Ronald T. Ackermann,&nbsp;Richard T. D'Aquila,&nbsp;James Adams,&nbsp;Donald Lloyd-Jones,&nbsp;Rex L. Chisholm,&nbsp;Abel Kho","doi":"10.1002/lrh2.10417","DOIUrl":"10.1002/lrh2.10417","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>The rapid development of artificial intelligence (AI) in healthcare has exposed the unmet need for growing a multidisciplinary workforce that can collaborate effectively in the learning health systems. Maximizing the synergy among multiple teams is critical for Collaborative AI in Healthcare.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare. We built bulk-natural language processing pipelines to extract structured information from clinical notes and stored them in common data models. We developed multimodal AI/machine learning (ML) tools and tutorials to enrich the toolbox of the multidisciplinary workforce to analyze multimodal healthcare data. We have created a fertile ground to cross-pollinate clinicians and AI scientists and train the next generation of AI health workforce to collaborate effectively.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our work has democratized access to unstructured health information, AI/ML tools and resources for healthcare, and collaborative education resources. From 2017 to 2022, this has enabled studies in multiple clinical specialties resulting in 68 peer-reviewed publications. In 2022, our cross-discipline efforts converged and institutionalized into the Center for Collaborative AI in Healthcare.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our Collaborative AI in Healthcare initiatives has created valuable educational and practical resources. They have enabled more clinicians, scientists, and hospital administrators to successfully apply AI methods in their daily research and practice, develop closer collaborations, and advanced the institution-level learning health system.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10417","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140700049","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":"Healthcare systems collaborating to implement a shared decision-making tool in the electronic health record and build evidence on its adoption and use","authors":"Megan E. Branda,&nbsp;Jennifer L. Ridgeway,&nbsp;Devin Mann,&nbsp;Jeff Wieser,&nbsp;Yvonne Gomez,&nbsp;Ashlee Dagoberg,&nbsp;Vivek Nautiyal,&nbsp;Hugh Jackson,&nbsp;Patrick Jahn,&nbsp;Kathy Yaple,&nbsp;Charanjit Khurana,&nbsp;Hooman Gharai,&nbsp;Briana Giese,&nbsp;Tate Corcoran,&nbsp;Victor Montori,&nbsp;Victor M. Montori","doi":"10.1002/lrh2.10418","DOIUrl":"10.1002/lrh2.10418","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Shared decision-making (SDM) is a method of care by which patients and clinicians work together to co-create a plan of care. Electronic health record (EHR) integration of SDM tools may increase adoption of SDM. We conducted a “lightweight” integration of a freely available electronic SDM tool, CV Prevention Choice, within the EHRs of three healthcare systems. Here, we report how the healthcare systems collaborated to achieve integration.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This work was conducted as part of a stepped wedge randomized pragmatic trial. CV Prevention Choice was developed using guidelines for HTML5-based web applications. Healthcare systems integrated the tool in their EHR using documentation the study team developed and refined with lessons learned after each system integrated the electronic SDM tool into their EHR. CV Prevention Choice integration populates the tool with individual patient data locally without sending protected health information between the EHR and the web. Data abstraction and secure transfer systems were developed to manage data collection to assess tool implementation and effectiveness outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Time to integrate CV Prevention Choice in the EHR was 12.1 weeks for the first system, 10.4 weeks for the second, and 9.7 weeks for the third. One system required two 1-hour meetings with study team members and two healthcare systems required a single 1-hour meeting. Healthcare system information technology teams collaborated by sharing information and offering improvements to documentation. Challenges included tracking CV Prevention Choice use for reporting and capture of combination medications. Data abstraction required refinements to address differences in how each healthcare system captured data elements.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Targeted documentation on tool features and resource mapping supported collaboration of IT teams across healthcare systems, enabling them to integrate a web-based SDM tool with little additional research team effort or oversight. Their collaboration helped overcome difficulties integrating the web application and address challenges to data harmonization for trial outcome analyses.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 S1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10418","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140702665","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 data to support health equity in an integrated delivery and finance system","authors":"Jane Kogan,&nbsp;Joan Eichner,&nbsp;Hyagriv Simhan,&nbsp;Erin Dalton,&nbsp;Alex Jutca,&nbsp;Beth Quinn,&nbsp;Jennifer Chaney,&nbsp;Anna Patterson,&nbsp;Donna Keyser","doi":"10.1002/lrh2.10423","DOIUrl":"10.1002/lrh2.10423","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>To accelerate healthcare transformation and advance health equity, scientists in learning health systems (LHSs) require ready access to integrated, comprehensive data that includes information on social determinants of health (SDOH).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We describe how an integrated delivery and finance system leveraged its learning ecosystem to advance health equity through (a) a cross-sector initiative to integrate healthcare and human services data for better meeting clients' holistic needs and (b) a system-level initiative to collect and use patient-reported SDOH data for connecting patients to needed resources.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Through these initiatives, we strengthened our health system's capacity to meet diverse patient needs, address health disparities, and improve health outcomes. By sharing and integrating healthcare and human services data, we identified 281 000 Shared Services Clients and enhanced care management for 100 adult Medicaid/Special Needs Plan members. Over a 1-year period, we screened 9173 (37%) patients across UPMC's Women's Health Services Line and connected over 700 individuals to social services and supports.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Opportunities exist for LHSs to improve, expand, and sustain their innovative data practices. As learnings continue to emerge, LHSs will be well positioned to accelerate healthcare transformation and advance health equity.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 S1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10423","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140701695","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":"Precision feedback: A conceptual model","authors":"Zach Landis-Lewis,&nbsp;Allison M. Janda,&nbsp;Hana Chung,&nbsp;Patrick Galante,&nbsp;Yidan Cao,&nbsp;Andrew E. Krumm","doi":"10.1002/lrh2.10419","DOIUrl":"10.1002/lrh2.10419","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>When performance data are provided as feedback to healthcare professionals, they may use it to significantly improve care quality. However, the question of how to provide effective feedback remains unanswered, as decades of evidence have produced a consistent pattern of effects—with wide variation. From a coaching perspective, feedback is often based on a learner's objectives and goals. Furthermore, when coaches provide feedback, it is ideally informed by their understanding of the learner's needs and motivation. We anticipate that a “coaching”-informed approach to feedback may improve its effectiveness in two ways. First, by aligning feedback with healthcare professionals' chosen goals and objectives, and second, by enabling large-scale feedback systems to use new types of data to learn what kind of performance information is motivating in general. Our objective is to propose a conceptual model of precision feedback to support these anticipated enhancements to feedback interventions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We iteratively represented models of feedback's influence from theories of motivation and behavior change, visualization, and human-computer interaction. Through cycles of discussion and reflection, application to clinical examples, and software development, we implemented and refined the models in a software application to generate precision feedback messages from performance data for anesthesia providers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We propose that precision feedback is feedback that is prioritized according to its motivational potential for a specific recipient. We identified three factors that influence motivational potential: (1) the motivating information in a recipient's performance data, (2) the surprisingness of the motivating information, and (3) a recipient's preferences for motivating information and its visual display.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>We propose a model of precision feedback that is aligned with leading theories of feedback interventions to support learning about the success of feedback interventions. We plan to evaluate this model in a randomized controlled trial of a precision feedback system that enhances feedback emails to anesthesia providers.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10419","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140724300","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":"Determinants of remote measurement-based care uptake in a safety net outpatient psychiatry department as part of learning health system transition","authors":"Rajendra Aldis,&nbsp;Lisa C. Rosenfeld,&nbsp;Norah Mulvaney-Day,&nbsp;Margaret Lanca,&nbsp;Kate Zona,&nbsp;Jeffrey A. Lam,&nbsp;Julia Asfour,&nbsp;Jonah C. Meltzer,&nbsp;H. Stephen Leff,&nbsp;Carl Fulwiler,&nbsp;Philip Wang,&nbsp;Ana M. Progovac","doi":"10.1002/lrh2.10416","DOIUrl":"https://doi.org/10.1002/lrh2.10416","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Behavioral measurement-based care (MBC) can improve patient outcomes and has also been advanced as a critical learning health system (LHS) tool for identifying and mitigating potential disparities in mental health treatment. However, little is known about the uptake of remote behavioral MBC in safety net settings, or possible disparities occurring in remote MBC implementation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This study uses electronic health record data to study variation in completion rates at the clinic and patient level of a remote MBC symptom measure tool during the first 6 months of implementation at three adult outpatient psychiatry clinics in a safety net health system. Provider-reported barriers to MBC adoption were also measured using repeated surveys at one of the three sites.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Out of 1219 patients who were sent an MBC measure request, uptake of completing at least one measure varied by clinic: General Adult Clinic, 38% (n = 262 of 696); Substance Use Clinic, 28% (n = 73 of 265); and Transitions Clinic, 17% (n = 44 of 258). Compared with White patients, Black and Portuguese or Brazilian patients had lower uptake. Older patients also had lower uptake. Spanish language of care was associated with much lower uptake at the patient level. Significant patient-level disparities in uptake persisted after adjusting for the clinic, mental health diagnoses, and number of measure requests sent. Providers cited time within visits and bandwidth in their workflow as the greatest consistent barriers to discussing MBC results with patients.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>There are significant disparities in MBC uptake at the patient and clinic level. From an LHS data infrastructure perspective, safety net health systems may need to address the need for possible ways to adapt MBC to better fit their populations and clinical needs, or identify targeted implementation strategies to close data gaps for the identified disparity populations.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 S1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10416","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326423","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":"Seeking American Society of Clinical Oncology-Quality Oncology Practice Initiative (ASCO-QOPI) certification in a northern New England rural health system and cancer care network","authors":"Hilary M. Perrey,&nbsp;Evelyn Taylor,&nbsp;Brett F. Cropp,&nbsp;Meaghan J. Bumpus,&nbsp;Shannon Lessard,&nbsp;Jeanette A. Pretorius,&nbsp;Jonathan H. Angus,&nbsp;Megan F. Duperreault,&nbsp;Amanda Snow,&nbsp;Dorothy Wang,&nbsp;Meredith Curtis,&nbsp;Lauren A. Couture,&nbsp;David R. Adolphson,&nbsp;Kimberly Smith,&nbsp;Joy H. Moody,&nbsp;Michael J. Bianchi,&nbsp;Mark G. Parker,&nbsp;Amit Sanyal,&nbsp;Scot C. Remick","doi":"10.1002/lrh2.10415","DOIUrl":"10.1002/lrh2.10415","url":null,"abstract":"<p>In 2006 following several years of preliminary study, the American Society of Clinical Oncology (ASCO) launched the Quality Oncology Practice Initiative (QOPI). This cancer-focused quality initiative evolved considerably over the next decade-and-a-half and is expanding globally. QOPI is undoubtedly the leading standard-bearer for quality cancer care and contemporary medical oncology practice. The program garners attention and respect among federal programs, private insurers, and medical oncology practices across the nation. The MaineHealth Cancer Care Network (MHCCN) has undergone expansive growth since 2017. The network provides cancer care to more than 70% of the cases in Maine in a largely rural health system in Northern New England. In fall 2020, the MHCCN QOPI project leadership, following collaborative discussions with the ASCO-QOPI team, elected to proceed with a health system–cancer network-wide QOPI certification. Key themes emerged over the course of our two-year journey including: (1) Developing a highly interprofessional team committed to the project; (2) Capitalizing on a single electronic medical record for data transmission to CancerLinQ; (3) Prior experience, especially policy development, in other cancer-focused accreditation programs across the network; and (4) Building consensus through quarterly stakeholder meetings and awarding Continuing Medical Education (CME) and American Board of Medical Specialists (ABMS) Maintenance of Certification (MOC) credits to oncologists. All participants demonstrated a genuine spirit to work together to achieve certification. We report our successful journey seeking ASCO-QOPI certification across our network, which to our knowledge is the <i>first-of-its-kind</i> endeavor.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10415","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140371515","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":"An ethics framework for the transition to an operational learning healthcare system","authors":"Marieke J Hollestelle,&nbsp;Rieke van der Graaf,&nbsp;Miriam CJM Sturkenboom,&nbsp;Johannes JM van Delden","doi":"10.1002/lrh2.10414","DOIUrl":"10.1002/lrh2.10414","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>While Learning Healthcare Systems (LHSs) have received increasing attention in health care and research, the amount of operational LHSs remains limited. Given the investment of resources in these projects, a moral responsibility to pursue the transition toward an LHS falls on projects and their participating stakeholders. This paper provides an ethics framework for projects that have taken steps toward building an LHS and are in the position to transition to an operational LHS.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>To articulate relevant ethical requirements, we analyze established ethics frameworks in the fields of LHSs, data-intensive health research, and transitioning or innovating health systems. The overlapping content and shared values are used to articulate overarching ethical requirements. To provide necessary context, we apply the insights from the analysis to the Innovative Medicines Initiative ConcePTION project. This project is specifically designed to generate knowledge on the safety of medications used during pregnancy and lactation through the establishment of an LHS.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Upon analyzing the consulted frameworks, we identified four overlapping ethical requirements that are also of significant relevance within the scope of our ethics framework. These requirements are: (1) public benefit and favorable harm–benefit ratio; (2) equity and justice; (3) stakeholder engagement; and (4) sustainability. Additionally, we apply these ethical requirements to the context of an LHS for pregnant and lactating people.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Although tailored to the context of pregnancy and lactation, our ethics framework can provide guidance for the transition to an operational LHS across diverse healthcare domains.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10414","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140243266","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":"Post-trial responsibilities in pragmatic clinical trials: Fulfilling the promise of research to drive real-world change","authors":"Stephanie R. Morain,&nbsp;P. Pearl O'Rourke,&nbsp;Joseph Ali,&nbsp;Vasiliki Rahimzadeh,&nbsp;Devon K. Check,&nbsp;Hayden B. Bosworth,&nbsp;Jeremy Sugarman","doi":"10.1002/lrh2.10413","DOIUrl":"10.1002/lrh2.10413","url":null,"abstract":"<p>While considerable scholarship has explored responsibilities owed to research participants at the conclusion of explanatory clinical trials, no guidance exists regarding responsibilities owed at the conclusion of a pragmatic clinical trial (PCT). Yet post-trial responsibilities in PCTs present distinct considerations from those emphasized in existing guidance and prior scholarship. Among these considerations include the responsibilities of the healthcare delivery systems in which PCTs are embedded, and decisions about implementation for interventions that demonstrate meaningful benefit following their integration into usual care settings—or deimplementation for those that fail to do so. In this article, we present an overview of prior scholarship and guidance on post-trial responsibilities, and then identify challenges for post-trial responsibilities for PCTs. We argue that, given one of the key rationales for PCTs is that they can facilitate uptake of their results by relevant decision-makers, there should be a presumptive default that PCT study results be incorporated into future care delivery processes. Fulfilling this responsibility will require prospective planning by researchers, healthcare delivery system leaders, institutional review boards, and sponsors, so as to ensure that the knowledge gained from PCTs does, in fact, influence real-world practice.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10413","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140260944","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":"How to use communities of practice to support change in learning health systems: A landscape of roles and guidance for management","authors":"Stephanie P. Brooks,&nbsp;Esther Ekpe Adewuyi,&nbsp;Tracy Wasylak,&nbsp;Denise Thomson,&nbsp;Sara N. Davison,&nbsp;Kate Storey","doi":"10.1002/lrh2.10412","DOIUrl":"10.1002/lrh2.10412","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Communities of practice support evidence-based practice and can be, in and of themselves, applied learning spaces in organizations. However, the variety of ways that communities of practice can support learning health systems are poorly characterized. Furthermore, health system leaders have little guidance on designing and resourcing communities of practice to effectively serve learning health systems.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We conducted a collective case study, examining a cross-section of Canadian-based communities of practice dedicated to supporting evidence-based practice. We held semi-structured interviews with 21 participants representing 16 communities of practice and 5 community of practice facilitation platforms that provide administration support, tools, and oversight for multiple communities of practice. Using the Conceptual Framework for Value-Creating Learning Health Systems, we characterized the numerous roles that communities of practice can take to support learning health systems. We also pulled insights from the interviews on properly resourcing and managing communities of practice.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Communities of practice can advance learning health systems across learning cycles (ie, identifying learning priorities, generating data and knowledge, and implementing and evaluating change). They also act as important infrastructure required to share and coordinate across learning health systems. Community of practice facilitation platforms reduce staff members' workload, in turn, creating greater efficiency and effectiveness across community of practice lifespans. Furthermore, these platforms can be a mechanism to coordinate critical activities (e.g., priority alignment, knowledge brokerage/sharing across the broader system).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>To the authors' knowledge, this is the first study to characterize communities of practice across the learning health system landscape. With these results, learning health system leaders have a catalog that clarifies the potential communities of practice roles in knowledge generation, implementation, and uptake of new evidence. Furthermore, the results provide evidence that organizational investment in overarching community of practice facilitation platforms will strengthen and accelerate community of practice supports in learning health systems.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10412","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140264724","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":"Wonderings to research questions: Engaging patients in long COVID research prioritization within a learning health system","authors":"Ann Blair Kennedy,&nbsp;Ariana Mitcham,&nbsp;Katherine Parris,&nbsp;Faith Albertson,&nbsp;Luis Sanchez Ferrer,&nbsp;Conor O'Boyle,&nbsp;Maushmi K. Patel,&nbsp;Tracey Gartner,&nbsp;Amy M. Broomer,&nbsp;Evan Katzman,&nbsp;Jeanette Coffin,&nbsp;Jennifer T. Grier,&nbsp;Nabil Natafgi","doi":"10.1002/lrh2.10410","DOIUrl":"https://doi.org/10.1002/lrh2.10410","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>An integral component of research within a learning health system is patient engagement at all stages of the research process. While there are well-defined best practices for engaging with patients on predetermined research questions, there is little specific methodology for engaging patients at the stage of research question formation and prioritization. Further, with an emerging disease such as Long COVID, population-specific strategies for meaningful engagement have not been characterized.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The COVID-19 Focused Virtual Patient Engagement Studio (CoVIP studio) was a virtual panel created to facilitate patient-centered studies surrounding the effects of long-term COVID (“Long COVID”) also known as post-acute SARS-CoV-2 syndrome (PASC). A diverse group of panelists was recruited and trained in several different areas of knowledge, competencies, and abilities regarding research and Long COVID. A three-step approach was developed that consisted of recording panelists' broad wonderings to generate patient-specific research questions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The “wonderings” discussed in panelists' training sessions were analyzed to identify specific populations, interventions, comparators, outcomes, and timeframes (PICOT) elements, which were then used to create a survey to identify the elements of greatest importance to the panel. Based on the findings, 10 research questions were formulated using the PICOT format. The panelists then ranked the questions on perceived order of importance and distributed one million fictional grant dollars between the five chosen questions in the second survey. Through this stepwise prioritization process, the project team successfully translated panelists' research wonderings into investigable research questions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This methodology has implications for the advancement of patient-engaged prioritization both within the scope of Long COVID research and in research on other rare or emerging diseases.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 S1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10410","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326462","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}