Learning Health Systems最新文献

{"title":"Accelerating a learning public health system: Opportunities, obstacles, and a call to action","authors":"Jessica D. Tenenbaum","doi":"10.1002/lrh2.10449","DOIUrl":"10.1002/lrh2.10449","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Public health systems worldwide face increasing challenges in addressing complex health issues and improving population health outcomes. This experience report introduces the concept of a Learning Public Health System (LPHS) as a potential solution to transform public health practice. Building upon the framework of a Learning Health System (LHS) in healthcare, the LPHS aims to create a dynamic, data-driven ecosystem that continuously improves public health interventions and policies. This report explores the definition, benefits, challenges, and implementation strategies of an LPHS, highlighting its potential to revolutionize public health practice.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This report employs a comparative analysis approach, examining the similarities and differences between an LPHS and an LHS. It also identifies and elaborates on the potential benefits, challenges, and barriers to implementing an LPHS. Additionally, the study investigates promising national initiatives that exemplify elements of an LPHS in action.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>An LPHS integrates data from diverse sources to inform knowledge generation, policy development, and operational improvements. Key benefits of implementing an LPHS include improved disease prevention, evidence-informed policy-making, and enhanced health outcomes. However, several challenges were identified, such as interoperability issues, governance concerns, funding limitations, and cultural factors that may impede the widespread adoption of an LPHS.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Implementation of an LPHS has the potential to significantly transform public health practice. To realize this potential, a call to action is issued for stakeholders across the public health ecosystem. Recommendations include investing in informatics infrastructure, prioritizing workforce development, establishing robust data governance frameworks, and creating incentives to support the development and implementation of a LPHS. By addressing these key areas, public health systems can evolve to become more responsive, efficient, and effective in improving population health outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11493540/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510006","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":"Moving from crisis response to a learning health system: Experiences from an Australian regional primary care network","authors":"Bianca Forrester,&nbsp;Georgia Fisher,&nbsp;Louise A. Ellis,&nbsp;Andrew Giddy,&nbsp;Carolynn L. Smith,&nbsp;Yvonne Zurynski,&nbsp;Lena Sanci,&nbsp;Katherine Graham,&nbsp;Naomi White,&nbsp;Jeffrey Braithwaite","doi":"10.1002/lrh2.10458","DOIUrl":"https://doi.org/10.1002/lrh2.10458","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>The COVID-19 pandemic challenged primary care to rapidly innovate. In response, the Western Victorian Primary Health Network (WVPHN) developed a COVID-19 online Community of Practice comprising general practitioners (GPs), practice nurses, pharmacists, aged care and disability workers, health administrators, public health experts, medical specialists, and consumers. This Experience Report describes our progress toward a durable organizational learning health system (LHS) model through the COVID-19 pandemic crisis and beyond.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In March 2020, we commenced weekly Community of Practice sessions, adopting the Project ECHO (Extension of Community Health Outcomes) model for a virtual information-sharing network that aims to bring clinicians together to develop collective knowledge. Our work was underpinned by the LHS framework proposed by Menear et al. and aligned with Kotter's eight-step change model.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>There were four key phases in the development of our LHS: build a Community of Practice; facilitate iterative change; develop supportive organizational infrastructure; and establish a sustainable, ongoing LHS. In total, the Community of Practice supported 83 unique COVID-19 ECHO sessions involving 3192 h of clinician participation and over 10 000 h of organizational commitment. Six larger sessions were run between March 2020 and September 2022 with 3192 attendances. New models of care and care pathways were codeveloped in sessions and network leaders contributed to the development of guidelines and policy advice. These innovations enabled WVPHN to lead the Australian state of Victoria on rates of COVID vaccine uptake and GP antiviral prescribing.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The COVID-19 pandemic created a sense of urgency that helped stimulate a regional primary care-based Community of Practice and LHS. A robust theoretical framework and established change management theory supported the purposeful implementation of our LHS. Reflection on challenges and successes may provide insights to support the implementation of LHS models in other primary care settings.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10458","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836403","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":"Medical researchers' perception of sharing of metadata from case report forms","authors":"Alexandra Meidt,&nbsp;Carolin Walter,&nbsp;Christoph U. Lehmann,&nbsp;Martin Dugas","doi":"10.1002/lrh2.10456","DOIUrl":"10.1002/lrh2.10456","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Publishing medical metadata stored in case report forms (CRFs) is a prerequisite for the development of a learning health system (LHS) by fostering reuse of metadata and standardization in health research. The aim of our study was to investigate medical researchers' (MRs) willingness to share CRFs, to identify reasons for and against CRF sharing, and to determine if and under which conditions MRs might consider sharing CRF metadata via a public registry.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We examined CRF data sharing commitments for 1842 interventional trials registered on the German Clinical Trials Registry (DRKS) from January 1, 2020, to December 31, 2021. We invited 1360 individuals registered as contacts on DRKS to participate in a web-based survey between May 10, 2022, and June 30, 2022.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Only 0.3% (5/1842) of data sharing commitments in DRKS included a plan to share blank CRFs. Survey results showed high support for CRF sharing. More than 70% of respondents (223/301) were willing to share their CRFs, and 83.7% (252/301) were interested in CRF reuse. The most frequently reported reason for CRF sharing was improvement of comparability and interpretability of patient data (244/301; 81.0%). The most frequently reported reason against CRF sharing was missing approval by the sponsor (160/301; 53.2%). Researchers conducting commercial trials were significantly less likely to share CRFs than those conducting noncommercial trials (63.3% vs. 76.2%, OR 0.54, 95% CI 0.32–0.92) and they were less likely to reuse CRFs (78.5% vs. 84.6%, OR 0.66, 95% CI 0.35–1.24). The most frequently mentioned prerequisite for publication of CRFs in a public registry was its trustworthiness (244/301, 81.1%).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Data sharing commitments in DRKS revealed a low awareness of CRF sharing. Survey results showed generally strong support for CRF sharing, including the willingness to publish CRFs in a public registry, although legal and practical barriers were identified.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11733469/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013334","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":"Changing practice in cystic fibrosis: Implementing objective medication adherence data at every consultation, a learning health system and quality improvement collaborative","authors":"Carla Girling,&nbsp;India Davids,&nbsp;Nikki Totton,&nbsp;Madelynne A. Arden,&nbsp;Daniel Hind,&nbsp;Martin J. Wildman","doi":"10.1002/lrh2.10453","DOIUrl":"https://doi.org/10.1002/lrh2.10453","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Medication adherence data are an important quality indicator in cystic fibrosis (CF) care, yet real-time objective data are not routinely available. An online application (CFHealthHub) has been designed to deliver these data to people with CF and their clinical team. Adoption of this innovation is the focus of an National Health Service England-funded learning health system and Quality Improvement Collaborative (QIC). This study applies the capability, opportunity, and motivation model of behavior change to assess whether the QIC had supported healthcare professionals' uptake of accessing patient adherence data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>This was a mixed-method study, treating each multidisciplinary team as an individual case. Click analytic data from CFHealthHub were collected between January 1, 2018, and September 22, 2019. Thirteen healthcare practitioners participated in semi-structured interviews, before and after establishing the QIC. Qualitative data were analyzed using the behavior change model.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The cases showed varied improvement trajectories. While two cases reported reduced barriers, one faced persistent challenges. Participation in the QIC led to enhanced confidence in the platform's utility. Reduced capability, opportunity, and motivation barriers correlated with increased uptake, demonstrating value in integrating behavior change theory into QICs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>QICs can successfully reduce barriers and enable uptake of e-health innovations such as adherence monitoring technology. However, ongoing multi-level strategies are needed to embed changes. Further research should explore sustainability mechanisms and their impact on patient outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10453","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835799","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":"Lessons for a learning health system: Effectively communicating to patients about research with their health information and biospecimens","authors":"Kayte Spector-Bagdady,&nbsp;Kerry A. Ryan,&nbsp;Luyun Chen,&nbsp;Camille Giacobone,&nbsp;Reshma Jagsi,&nbsp;Reema Hamasha,&nbsp;Katherine Hendy,&nbsp;J. Denard Thomas,&nbsp;Jessie M. Milne,&nbsp;Alexandra H. Vinson,&nbsp;Jodyn Platt","doi":"10.1002/lrh2.10450","DOIUrl":"10.1002/lrh2.10450","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Sharing patient health information and biospecimens can improve health outcomes and accelerate breakthroughs in medical research. But patients generally lack understanding of how their clinical data and biospecimens are used or commercialized for research. In this mixed methods project, we assessed the impact of communication materials on patient understanding, attitudes, and perceptions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Michigan Medicine patients were recruited for a survey (<i>n</i> = 480) or focus group (<i>n</i> = 33) via a web-based research study portal. The survey assessed the impact of mode of communication about health data and biospecimen sharing (via an informational poster vs. a news article) on patient perceptions of privacy, transparency, comfort, respect, and trust. Focus groups provided in-depth qualitative feedback on three communication materials, including a poster, FAQ webpage, and a consent form excerpt.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Among survey respondents, the type of intervention (poster vs. news) made no statistically significant difference in its influence on any characteristic. However, 95% preferred that Michigan Medicine tell them about patient data and biospecimen research sharing versus hearing it from the news. Focus group participants provided additional insights, discussing values and perceptions of altruism and reciprocity, concerns about commercialization, privacy, and security; and the desire for consent, control, and transparency.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Developing our understanding of patient data-sharing practices and integrating patient preferences into health system policy, through this work and continued exploration, contributes to building infrastructure that can be used to support the development of a learning health system across hospital systems nationally.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11733471/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143012572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linking The Cancer Imaging Archive and GenBank to the National Clinical Cohort Collaborative","authors":"Ahmad Baghal,&nbsp;Joel Saltz,&nbsp;Tahsin Kurc,&nbsp;Prateek Prasanna,&nbsp;Samantha Baghal,&nbsp;Janos Hajagos,&nbsp;Erich Bremer,&nbsp;Shaymaa Al-Shukri,&nbsp;Joshua L. Kennedy,&nbsp;Michael Rutherford,&nbsp;Tracy Nolan,&nbsp;Kirk Smith,&nbsp;Christopher G. Chute,&nbsp;Fred Prior","doi":"10.1002/lrh2.10457","DOIUrl":"10.1002/lrh2.10457","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This project demonstrates the feasibility of connecting medical imaging data and features, SARS-CoV-2 genome variants, with clinical data in the National Clinical Cohort Collaborative (N3C) repository to accelerate integrative research on detection, diagnosis, and treatment of COVID-19-related morbidities. The N3C curated a rich collection of aggregated and de-identified electronic health records (EHR) data of over 18 million patients, including 7.5 million COVID-positive patients, seen at hospitals across the United States. Medical imaging data and variant samples are important data modalities used in the study of COVID-19.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Materials and Methods</h3>\u0000 \u0000 <p>Imaging data and features are hosted on the Cancer Imaging Archive (TCIA), and sequenced variant samples are analyzed and stored at the NIH GenBank. The University of Arkansas for Medical Sciences (UAMS) published the first COVID-19 data set of 105 patients on TCIA and 37 patients on GenBank. We developed a process to link imaging and genomic variants and N3C EHR data through Privacy Preserving Record Linkage (PPRL) using de-identified cryptographic hashes to match records associated with the same individuals without using patient identifiers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The PPRL techniques were piloted using clinical and imaging data sets provided by UAMS. Developed software components and processes executed properly, and linked data were returned and processed for visualization.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Linking across clinical data sources at the patient level provides opportunities to gain insights from data that may not be known otherwise. The PPRL prototype and the pilot serve as a model to link disparate and diverse data repositories to enhance clinical research.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11733468/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143012968","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":"Community-led transformation principles: Transforming public health learning systems by centering authentic collaboration with community-based organizations","authors":"Reba Meigs,&nbsp;Amina Sheik Mohamed,&nbsp;Adriana Bearse,&nbsp;Sarah Vicente,&nbsp;Nghi Dang,&nbsp;Asmaa Deiranieh,&nbsp;Reem Zubaidi,&nbsp;Valerie Nash,&nbsp;Maliha Ali,&nbsp;Trenita Childers,&nbsp;Mohammad Wahdatyar,&nbsp;Emily Treichler,&nbsp;Blanca Meléndrez","doi":"10.1002/lrh2.10451","DOIUrl":"10.1002/lrh2.10451","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>In the face of evolving public health challenges, including emerging diseases, pervasive health disparities, and escalating environmental threats, the integration of learning health system (LHS) principles emerges as a vital strategy for enhancing the adaptability and efficacy of public health initiatives. Traditional approaches within these systems often overlook the potential to deeply involve community-based organizations (CBO) that are led and staffed by the communities they serve as equal and essential partners in the public health discourse.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This commentary proposes a suite of nine community-led transformation (CLT) principles aimed at reimagining LHS frameworks to authentically incorporate CBOs at their core. Drawing on the experiences from initiatives supporting Afghan refugees, we illustrate the application of these principles through two detailed case studies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>These examples demonstrate the CLT principles in action and spotlight the enhanced cultural competency, effectiveness, and equitable power distribution that arise from such partnerships. Centering small to mid-sized CBOs including ethnic-led and/or faith based within LHS structures enables the system to access invaluable cultural insights, strengthen community bonds, and empower those communities to spearhead their transformative journey toward sustainable health, equity, and well-being improvements.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The CLT principles herald a shift toward a more inclusive and co-led public health paradigm by offering a blueprint for stakeholders eager to forge strong, trust-based coalitions and cocreate initiatives with community leaders including Black, Indigenous, and People of Color (BIPOC) leaders from ethnic-led and/or faith-based CBOs. By embracing these principles, public health systems can evolve into truly inclusive, responsive, and sustainable entities poised to advance health equity for all community members.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11493544/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A conceptual learning analysis of paired after action and intra action reviews for health emergencies","authors":"Elliot Brennan,&nbsp;Seye Abimbola","doi":"10.1002/lrh2.10447","DOIUrl":"10.1002/lrh2.10447","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Processes of self-reflection and the learning they allow are crucial before, during, and after acute emergencies, including infectious disease outbreaks. Tools—such as Action Reviews—offer World Health Organization (WHO) member states a platform to enhance learning. We sought to better understand the value of these tools and how they may be further refined and better used.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We searched the publicly available WHO Strategic Partnership for Health Security website for paired reports of Action Reviews, that is, reports with a comparable follow-up report. We complemented the paired action reviews, with a literature search, including the gray literature. The paired action reviews were analyzed using the “Learning Health Systems” framework.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We identified three paired action reviews: Lassa Fever After Action Reviews (AARs) in Nigeria (2017 and 2018), COVID-19 Intra-Action Reviews (IARs) in Botswana (2020 and 2021), and COVID-19 IARs in South Sudan (2020 and 2021). Action Reviews allowed for surfacing relevant knowledge and, by engaging the right (in different contexts) actors, asking “are we doing things right?” (single loop learning) was evident in all the reports. Single loop learning is often embedded within examples of double loop learning (“are we doing the right things?”), providing a more transformative basis for policy change. Triple loop learning (“are we learning right”?) was evident in AARs, and less in IARs. The range of participants involved, the level of concentrated focus on specific issues, the duration available for follow through, and the pressures on the health system to respond influenced the type (i.e., loop) and the effectiveness of learning.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Action Reviews, by design, surface knowledge. With favorable contextual conditions, this knowledge can then be applied and lead to corrective and innovative actions to improve health system performance, and in exceptional cases, continuous learning.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11493552/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging public health cancer surveillance capacity to develop and support a rural cancer network","authors":"Jason Semprini,&nbsp;Ingrid M. Lizarraga,&nbsp;Aaron T. Seaman,&nbsp;Erin C. Johnson,&nbsp;Madison M. Wahlen,&nbsp;Jessica S. Gorzelitz,&nbsp;Sarah A. Birken,&nbsp;Mary C. Schroeder,&nbsp;Tarah Paulus,&nbsp;Mary E. Charlton","doi":"10.1002/lrh2.10448","DOIUrl":"10.1002/lrh2.10448","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>As the rural–urban cancer mortality gap widens, centering care around the needs of rural patients presents an opportunity to advance equity. One barrier to delivering patient-centered care at rural hospitals stems from limited analytic capacity to leverage data and monitor patient outcomes. This case study describes the experience of a public health cancer surveillance system aiming to fill this gap within the context of a rural cancer network.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>To support the implementation of a novel network model intervention in Iowa, the Iowa Cancer Registry began generating hospital-specific and catchment area reports. Then, the Iowa Cancer Registry supported adapting the network model to fit the context of Iowa's cancer care delivery system by performing data monitoring and reporting functions. Informed by a gap analysis, the Iowa Cancer Registry then identified which quality accreditation standards could be achieved with public health surveillance data and analytic support.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The network intervention in Iowa supported 5 rural cancer centers across the state, each concurrently pursuing quality accreditation standards. The Iowa Cancer Registry's hospital and catchment-specific reports illuminated the cancer burden and needs of rural cancer centers within the network. Our team identified 19 (of the 36 total) quality standards that can be supported by public health surveillance functions typically performed by the registry. These standards encompassed data-driven quality improvement, patient monitoring, and reporting guideline-concordant care standards.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>As rural hospitals continue to face resource constraints, multisectoral efforts informed by data from centralized public health surveillance systems can promote quality improvement initiatives across rural communities. While our work remains preliminary, we predict that analytic support provided by the Iowa Cancer Registry will enable the rural network hospitals to focus their capacity toward developing the infrastructure necessary to deliver high-quality care and serve the unique needs of rural cancer patients.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11493549/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"US public health surveillance, reimagined","authors":"Elina Guralnik","doi":"10.1002/lrh2.10445","DOIUrl":"10.1002/lrh2.10445","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>This study presents two novel concepts for standardizing electronic health records (EHR)-based public health surveillance through utilization of existing informatics methods and data platforms.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Drawing from the collective experience in applied epidemiology, health services research and health informatics, the author presents a vision for an alternative path to public health surveillance by repurposing existing tools and resources, such as (1) computable phenotypes which have already been created and validated for a variety of chronic diseases of interest to public health and (2) large data platforms/collaboratives, such as All of Us Research Program and National COVID Cohort Collaborative. Opportunities and challenges are discussed regarding EHR-based chronic disease surveillance, as well as the concept of phenotype definitions and large data platforms reuse for public health needs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results/Framework</h3>\u0000 \u0000 <p>Reusing of computable phenotypes for EHR-based public health surveillance would require secure data platforms and nationally representative data. Standardization metrics for reuse of previously developed and validated computable phenotypes are also necessary and are currently being developed by the author. This study presents a reimagined Learning Health System framework by incorporating Public Health and two novel concept sets of solutions into the healthcare ecosystem.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion/Next Steps</h3>\u0000 \u0000 <p>Alternative approaches to limited resources and current infrastructure of the US Public Health System, especially as applied to disease surveillance, are needed and may be possible when repurposing the resources and methodologies across the Learning Health System.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11493541/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}