Learning Health Systems最新文献

{"title":"Pathways to Real World Evidence From the Real World of School-Based Physical Fitness Testing (SB-PFT)","authors":"Anita Walden,&nbsp;Allen Yiu,&nbsp;Gregory Welk,&nbsp;Yawen Guo,&nbsp;Shlomit Radom-Aizik,&nbsp;Margaret Schneider,&nbsp;Allyson Reeds,&nbsp;AbdulMalik Shakir,&nbsp;Dan M. Cooper","doi":"10.1002/lrh2.70108","DOIUrl":"10.1002/lrh2.70108","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Unique challenges of data quality, standardization, and interoperability face non-traditional sources of real world data (RWD), those outside of the common RWD source, the electronic health record (EHR). The experience of our clinical, research, and community group in addressing the endangered school-based physical fitness testing (SB-PFT) data set may be useful as a paradigm for a variety of nontraditional instances of health-related RWD. Until recently, SB-PFT was mandated annually within public schools in ~60% of U.S. children and adolescents representing one of the most robust and inclusive data sets available in youth. When implemented rigorously, SB-PFT produced data that improved pediatric health and student learning. Driven by perceived and real gaps in data quality, inconsistent and insensitive school-site implementation, and uneven evidence for health-relevance, calls for revamping (and, in some cases, ending) school testing are growing.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>To preserve and enhance this unique resource, SB-PFT must incorporate emerging science and technology. We describe here the rationale, structure, and process involved in our building an HL7-associated Domain Analysis Model for SB-PFT. Our team consisted of clinicians, exercise scientists, school personnel, publish health officials, parents and students. The structure of the DAM was facilitated by data standard experts and skilled Unified Modeling Language professionals. Administrative support was rendered by our institution's NIH-funded Clinical Translational Science Award.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The DAM will serve as a necessary first step in integrating Fast Healthcare Interoperability Resources (FHIR) and Learning Health Systems (LHS) models essential to realize the full potential of SB-PFT data in advancing child health.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The experience presented here can serve as a roadmap for addressing critical issues of data standardization and RWE generation in nontraditional instances of RWD. The DAM can be deployed in SB-PFT data exchange in real world settings.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13402859/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148608587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning From Queues: Operational Analysis and Performance Evaluation of Dialysis Duration in a Paired Kidney Exchange With an M/M/1 System","authors":"Samrajya Raj Acharya,&nbsp;Sushil Ghimire,&nbsp;Ram Prasad Ghimire","doi":"10.1002/lrh2.70107","DOIUrl":"10.1002/lrh2.70107","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Introduction&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The allocation and timing for kidney matching and transplantation among incompatible donor-recipient pairs in the Paired Kidney Exchange face significant operational challenges of managing an uncertain waitlist. The time for dialysis of patients waiting for biologically compatible kidneys follows the concept of a stochastic queueing system. This study embodies the Learning Health System approach by integrating real-world data and mathematical modeling to generate actionable insights that guide decision-making.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This work employs an &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;M&lt;/mi&gt;\u0000 &lt;mo&gt;/&lt;/mo&gt;\u0000 &lt;mi&gt;M&lt;/mi&gt;\u0000 &lt;mo&gt;/&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ M/M/1 $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; model with Poisson arrivals and exponentially distributed service times for incompatible donor–recipient pairs in a single transplant facility. Inter-arrival and service times are used to derive queue length and waiting-time measures to characterize system congestion and instability, with performance measures evaluated as indicative approximations analyzing the corresponding metrics.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The system is persistently overloaded, with arrival rates exceeding service rates (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;λ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ lambda $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;&gt;&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ &gt; $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;μ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ mu $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;; &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ρ&lt;/mi&gt;\u0000 &lt;mo&gt;&gt;&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ rho &gt; $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; 1), causing prolonged waiting times. Analytical and Hutson-B bootstrap confide","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13381077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148521098","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":"Exploring Barriers, Enablers, and Strategies for Implementing Learning Health System Projects in Healthcare Organizations: A Multilevel Analysis in an Australian Setting","authors":"Louise Shaw,&nbsp;Meg Perrier,&nbsp;Kayley Lyons,&nbsp;Mikayla Wolfe,&nbsp;Rahul Barmanray,&nbsp;Daniel Capurro,&nbsp;Wendy Chapman,&nbsp;Sathana Dushyanthen","doi":"10.1002/lrh2.70103","DOIUrl":"10.1002/lrh2.70103","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To analyze the barriers, enablers, and strategies affecting the implementation of learning health systems (LHS) projects across individual (micro), organizational (meso), and system (macro) levels within Australian healthcare settings</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Semistructured interviews were conducted with 26 clinicians undertaking LHS projects in their organizations, as part of a LHS Academy fellowship program. Interviews were completed at two discrete time points: early in project implementation and again at project completion. They explored fellows' experiences with implementing LHS projects in real-world organizational contexts. Data were analyzed using a hybrid deductive-inductive approach guided by a multilevel ecological codebook developed by Shaw et al.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Participants' early interviews highlighted limited confidence, emerging leadership skills, and uncertainty in navigating implementation challenges, while later interviews described greater confidence, stronger leadership capability, and more refined adaptive strategies at the micro level. Organizational influences were described at both time points as slow and uneven, though later interviews included examples of small cultural shifts and early collaborative efforts alongside persistent constraints such as limited resources, siloed cultures, and inadequate digital infrastructure. At the system level, barriers including regulatory complexity, policy misalignment, and limited funding were reported consistently across both interviews and were perceived as largely unchanged.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Building individual capability was necessary but insufficient for successful LHS project implementation. Alignment across micro-, meso-, and macrolevels is critical, requiring parallel investment in organizational readiness, digital infrastructure, and policy reform. Fellowship programs may support individual change and foster proof-of-concept projects, but scaling impact demands coordinated, multilevel strategies.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13364508/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148449845","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":"Developing a Program Evaluation Training Within a National Federal Healthcare Setting: Veterans Affairs Evaluation Bootcamp Training","authors":"Nardeen M. Shafik,&nbsp;John P. Donnelly,&nbsp;Jessica Johnson,&nbsp;Linda M. Kawentel,&nbsp;Nicholas W. Bowersox","doi":"10.1002/lrh2.70102","DOIUrl":"10.1002/lrh2.70102","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Learning Health Systems (LHSs) depend on the ability to effectively evaluate programs and translate data into practice. Within the Veterans Affairs (VA) health system, formal mandates require programmatic decision-making to be data-driven; however, opportunities for staff to develop applied program evaluation skills that support LHS learning cycles remain limited.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>To address this need, the synchronous virtual Evaluation Bootcamp Training (EBcT) was developed to equip learners with practical, applied evaluation skills. This manuscript describes the design rationale, structure, and core concepts of the training, emphasizing design decisions intended to support evaluation capacity building within a large and complex health system.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>EBcT was delivered over 4 days across 2 training cohorts (8 learners in 4 project teams from Cohort 1; 12 learners in 5 project teams from Cohort 2). Project teams addressed a range of clinical and operational initiatives within VA. Learners represented diverse professional roles, including lead evaluators, clinical subject matter experts, project managers, operational leaders, and analysts. Learners reported high satisfaction throughout the training, with full attendance at application sessions and strong participation in didactic components. A majority reported meeting predefined evaluation learning targets at the end of the training.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Findings suggest that structured, team-based training models such as EBcT can strengthen evaluation capacity and support LHS functioning by equipping project teams to engage in repeated cycles of measurement, learning, and performance improvement.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13364505/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148449901","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":"Person-Centered Care Planning for People With Multiple Chronic Conditions: An Environmental Scan of Models and Approaches","authors":"Annette M. Totten,&nbsp;David A. Dorr,&nbsp;Arlene S. Bierman,&nbsp;Kisha Coa,&nbsp;Chandler Atchison,&nbsp;Cynthia Davis-O'Reilly,&nbsp;Katherine D. Peak,&nbsp;Shannon Sweeney,&nbsp;Deborah J. Cohen,&nbsp;Ana R. Quiñones","doi":"10.1002/lrh2.70101","DOIUrl":"https://doi.org/10.1002/lrh2.70101","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Introduction&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Person-centered care planning (PCCP) involves active collaboration between people seeking care, clinical teams, and others to co-create longitudinal treatment plans. It is a crucial part of care for people with multiple chronic conditions (MCCs) and other complex needs. Despite the widespread acceptance of the concept, the use of PCCP in the US is variable. Many effective models of PCCP exist but uptake has been limited.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Objective&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;We sought to identify and assess current models and approaches for PCCP by performing a multi-component environmental scan.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;We conducted targeted literature reviews based on streamlined systematic review methods and qualitative interviews with key informants, identified for their relevant expertise and frontline knowledge of PCCP models.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;In all, 966 abstracts and 187 full articles met review criteria. We identified 40 models with elements fitting into 7 categories of how they differed from usual care (adding People/Roles, innovative Payment/Incentives, novel Technology, decision support Tools, Services, Functions, and Changing Focus). Most were multi-component models with evidence of effectiveness in outcomes that address the quintuple aim. Barriers to adoption and integration across sectors were primarily lack of time and appropriate payment mechanisms. A small set of measures for PCCP was identified that focused primarily on patient experience and goal setting. Similarly, a small number of PCCP models connected with social services organizations to address health-related social needs and highlighted challenges with data sharing and payment as well as growing collaborations in community care hubs to address social needs. KI interviews echoed and deepened our understanding of these findings by contextualizing experiences with barriers and factors facilitating the delivery of PCCP.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Discussion&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;PCCP is a key aspect of high-quality care for people with MCCs; uptake is limited with barriers related to time and resources. Facilitators include alignment with healthcare system objectives and practices, and ready-to-deploy resources that enable coordinated PCCP care across providers. To promote greater adoption of PCCP concerted efforts are needed to share implementation details, success stories, and build the case for PCCP ","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324883","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 HALO Model: A Learning Health System Framework for Artificial Intelligence","authors":"Adrian H. Zai,&nbsp;Mohammad Adibuzzaman,&nbsp;David D. McManus,&nbsp;Allan Walkey","doi":"10.1002/lrh2.70100","DOIUrl":"10.1002/lrh2.70100","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Artificial intelligence is increasingly embedded in healthcare delivery, yet existing Learning Health System (LHS) models do not fully account for the lifecycle management and continuous assurance requirements of AI systems. This gap limits health systems' ability to safely and sustainably integrate AI as a learning component of care.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We conducted a conceptual system modeling investigation grounded in LHS theory and contemporary AI governance frameworks. Through structured theoretical integration, we aligned the classical LHS learning cycle with an action-oriented AI lifecycle and five continuous assurance dimensions, developing a unified framework to support operational implementation within health systems.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The resulting Health AI Learning and Oversight (HALO) model specifies how AI functions as a dynamic knowledge artifact within an LHS. Application of the model illustrates how integrating lifecycle stages and continuous assurance instantiates iterative learning loops, enables adaptive governance, and supports operational lifecycle management, including ongoing monitoring of performance, safety, equity, transparency, and security across clinical environments.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>By extending LHS theory to incorporate AI lifecycle and assurance requirements explicitly, the HALO framework operationalizes continuous learning and oversight for AI-enabled health systems. This model provides a foundation for designing, governing, and sustaining responsible and adaptive AI deployment as healthcare environments evolve.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13247301/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148220053","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":"Surveillance of Pharmaceutical Risk-Mitigation Behavior: Applying and Comparing Statistical Process Control Methods Using Real World Data","authors":"Harris Butler,&nbsp;John D. Rice,&nbsp;Nichole E. Carlson,&nbsp;Elaine H. Morrato","doi":"10.1002/lrh2.70098","DOIUrl":"10.1002/lrh2.70098","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Active post-marketing surveillance of prescribing behavior of high-risk drugs may provide early warning of unforeseen issues in a population, yet analysis approaches for surveillance using real-world data are underdeveloped. This paper evaluates a modified statistical process control (SPC) method for surveillance of risk minimization measures derived from administrative claims data. The approach detects changes in population-level prescribing behaviors and informs investigators of the timing and nature of any detected changes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We investigated prescription drug claims for tapentadol extended release (ER) from the Colorado All-Payers Claims Database (2012–2019). The cohort was 2702 unique patients receiving their first prescriptions of tapentadol ER (an opiate with an FDA Risk Evaluation and Mitigation Strategy). The risk minimization measures were the prescribing rate and the proportion of prescriptions with appropriate dosing. A statistical model was fitted to data from January–December 2012 to establish a stable baseline and then updated biweekly through June 2019. We conducted surveillance with our modified SPC method and a classical SPC method, controlling the false alarm rate to 0.005 for each, and compared how detections aligned with external policy actions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our method detected two periods of unusual prescribing behavior beginning in March 2015 (<i>p</i> &lt; 0.005) and April 2016 (<i>p</i> &lt; 0.005). The classical method detected a single period in October 2016 (<i>p</i> &lt; 0.005). Our detections aligned with risk minimization activities in Colorado; the classical method aligned with only the second activity, 6 months later.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Our modified SPC method, which monitors model misspecification rather than raw prescribing data, detects more periods of changing behavior that better temporally align with risk minimization actions. This tool may be useful for regulatory agencies to independently monitor for emerging risks and prescribing trends to complement REMS assessments.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13242977/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148212247","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 Value and Challenges of Stakeholder Engagement in Rehabilitation Learning Health Systems: A Qualitative Pilot Study of Rehabilitation Directors","authors":"Nichole E. Stetten,&nbsp;Kristin Ressel,&nbsp;Linda Resnik,&nbsp;Janet K. Freburger","doi":"10.1002/lrh2.70099","DOIUrl":"https://doi.org/10.1002/lrh2.70099","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Stakeholder engagement is a core element of a learning health system (LHS). Meaningful engagement of stakeholders can improve learning within a health system by informing program development and service delivery and by increasing buy-in and sustainability of evidence-based practices. While health systems recognize the value of stakeholder engagement, conducting stakeholder engagement activities requires time, resources, and the support of health system leadership. The objective of this pilot study was to obtain preliminary data on the value of stakeholder engagement from the perspective of rehabilitation directors.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We conducted a general qualitative study utilizing 90-min focus groups. The main outcomes were domains identified from analysis of the focus group transcripts.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Six individuals from 4 health systems participated in one of two focus groups. Seven domains were identified: (1) how stakeholder engagement supports organizational mission and priorities; (2) types of stakeholders working with health systems; (3) past stakeholder engagement experiences; (4) engagement with stakeholders with lived experiences; (5) sustaining stakeholder relationships; (6) challenges with stakeholder engagement; and (7) future stakeholder engagement goals.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This qualitative pilot study of the perspectives of rehabilitation LHS leaders on stakeholder engagement provides preliminary data that can be used to inform future research. While we found consistent themes recognizing the value of stakeholder engagement and participation in stakeholder engagement, respondents noted challenges in engaging stakeholders and using the information gained to effect change.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148174129","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":"Developing Suicide Prevention Research Priorities for the Department of Veterans Affairs Office of Research and Development","authors":"Joseph I. Constans,&nbsp;David Atkins,&nbsp;Kara Beck,&nbsp;Lisa A. Brenner,&nbsp;Stephanie Gamble,&nbsp;Brian P. Marx,&nbsp;Melissa Mehalick,&nbsp;Matthew A. Miller,&nbsp;Stephen O'Connor,&nbsp;Susan Strickland,&nbsp;Wendy Tenhula,&nbsp;Jodie Trafton,&nbsp;Edgar Villareal,&nbsp;Bradley V. Watts,&nbsp;Steven K. Dobscha","doi":"10.1002/lrh2.70084","DOIUrl":"https://doi.org/10.1002/lrh2.70084","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The VA Office of Research and Development (ORD) has re-organized such that certain high priority health areas, such as suicide prevention, are managed using an Actively Managed Portfolio (AMP) model. One key capability of AMPs is to establish priorities for the portfolio; here, we describe the process and outcome of the priority setting process for the Suicide Prevention AMP (SP-AMP).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>The SP-AMP utilized a three-phase process for determining suicide prevention research priorities. Team members initially identified existing priority statements from organizations invested in suicide prevention, and in a second phase, obtained feedback from internal VA and non-VA partners as well as Veterans on the perceived importance of these domains. In the final phase, an executive steering committee (ESC) considered the qualitative and quantitative data for five potential areas for prioritization.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The ESC selected “lethal means safety (LMS) approaches to suicide prevention” as the priority area for 2025.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>SP-AMP team solicited feedback from multiple relevant partners in suicide prevention. “LMS approaches to suicide prevention” was selected as the key immediate priority because advances could lead to immediate impact in suicide prevention. Other candidate domains will be highlighted by the SP-AMP in upcoming funding cycles.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70084","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148174028","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 S1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148094069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}