Learning Health Systems最新文献

{"title":"Toward a Smart Learning Health System: An Ontology-Based Framework","authors":"Meg Ma,&nbsp;Ping Yu,&nbsp;Louise D. Hickman","doi":"10.1002/lrh2.70117","DOIUrl":"https://doi.org/10.1002/lrh2.70117","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Learning health system (LHS) frameworks have been presented in multiple forms, but there is no standardized representation that captures both their core concepts and the relationships among them. This limits their practical use by health services seeking to design, implement, and evaluate LHS capabilities in changing sociotechnical environments. This study developed the Smart Learning Health System ontology (SMARTLHS), a formal semantic framework that synthesizes concepts and relationships from published LHS frameworks.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Relevant literature was identified through a systematic database search. SMARTLHS was developed using an iterative system mapping and ontology engineering approach, comprising five steps: (1) ontology requirements specification, (2) iterative ontology conceptualization, (3) concept and relationship comparison and formalization, (4) ontology evaluation and refinement, and (5) ontology alignment with foundational ontology, to ensure semantic consistency and interoperability. The SMARTLHS ontology was implemented using the Web Ontology Language (OWL) and developed in Protégé.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>SMARTLHS contains 447 classes and 134 object properties. Its structure is organized around four high-level groupings: <i>LHSs, supporting pillars, cross-cutting themes,</i> and <i>common reference LHS frameworks</i>. The supporting pillars comprise seven domains: <i>Care delivery and organizational strategies</i>, <i>community and patient engagement, culture and change management, data and technology infrastructure, governance and leadership, research and education,</i> and <i>workforce and capacity building</i>. The cross-cutting themes include <i>ethics and oversight, evaluation and methodology</i>, and <i>value creation and benefits</i>. Together, these classes and relationships provide a computable semantic representation of LHS implementation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>SMARTLHS addresses key limitations of existing LHS frameworks by transforming narrative framework concepts into a formal, extensible semantic knowledge model. By explicitly representing both concepts and relationships, it provides a foundation for organizational assessment, semantic interoperability, and future AI-enabled applications, including ontology-guided retrieval and Retrieval-Augmented Generation (RAG).</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 4","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70117","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784324","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":"Operationalizing Patient Experience Data as a System Feedback Asset in Learning Health Systems: Learnings From Model of Care Redesign at Trillium Health Partners","authors":"Umair Majid,&nbsp;Arija Birze,&nbsp;Danielle Jacobson,&nbsp;Michelle Marcinow,&nbsp;Natalie Murray,&nbsp;Wanyue Chen,&nbsp;Marissa Bird,&nbsp;Adam Gdyczynski,&nbsp;Kerry Kuluski","doi":"10.1002/lrh2.70113","DOIUrl":"https://doi.org/10.1002/lrh2.70113","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Learning Health Systems (LHS) offer a pathway to continuously improve care by integrating data and lived experience, yet few practical examples demonstrate how LHS can be operationalized in hospital settings in ways that embed the voices of patients and families. This manuscript describes how patient experience (PX) data are integrated into the design of a new model of care (MoC) that responds to both system demands and the realities of patients and families.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Researchers partnered with clinical leaders to integrate patient and family voices into the design of a new MoC using PX survey data, interviews with patients and families admitted to medicine units, and naturalistic walk-throughs of each medicine unit. Data collection explored care priorities, interactions with staff, physical environments, and reflections on positive and challenging aspects of care. Analysis followed a three-step iterative process: unit-level synthesis, cross-unit thematic comparison, and data integration.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In total, 390 patients participated in the PX survey and 19 patients/families participated in interviews. Patients situated their experiences within a health care system under strain, highlighting both strengths and gaps. Many expressed appreciation for nurses' compassion, but emotional support for fears, anxieties, and worries was inconsistent, particularly when staff were overstretched or transient. Long wait times for transport, procedures, or basic care needs were reported as sources of anxiety and diminished dignity. Naturalistic walk-throughs documented how crowding, noise, lighting, and wayfinding shaped patients' comfort and privacy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This work provides a practical example of how an LHS approach can be operationalized by embedding the voices of patients and families into care redesign. Findings from the PX survey and interviews are directly shaping a new MoC at Trillium Health Partners (THP) that is evidence-informed and co-designed with patients and families, providing a practical example of how PX data can drive continuous, system-level learning.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 4","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148783796","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":"Long-Term, Low-Maintenance, and Impactful: Lessons Learned From Sustaining the NOHARM Pragmatic Clinical Trial","authors":"Shritha Gayathri,&nbsp;Amanda Courtright-Lim,&nbsp;Alexandra Wicker,&nbsp;Jane Hein,&nbsp;Amanda Nelson,&nbsp;Sue Cutshall,&nbsp;Sarah A. Minteer,&nbsp;Andrea Cheville,&nbsp;Jon Tilburt","doi":"10.1002/lrh2.70075","DOIUrl":"https://doi.org/10.1002/lrh2.70075","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Sustaining interventions implemented as part of pragmatic clinical trials requires proactive planning, but how to approach this process remains understudied. The Non-pharmacological Options in Hospital-based and Rehabilitation pain Management stepped-wedge cluster-randomized pragmatic trial tested an electronic health record-based educational bundle (known as the Healing After Surgery [HAS] initiative) that encouraged the incorporation of non-pharmacological pain care in perioperative pain management. The HAS initiative was implemented in multiple surgical practices and hospital sites. One year prior to trial completion, a sustainment committee was established to support the transition of intervention components to clinical ownership and to support posttrial sustainment of the HAS initiative.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We reviewed meeting minutes from biweekly sustainment committee meetings and conducted debriefing sessions with committee members (informed by the Clinical Sustainability Assessment Tool) to identify sustainment strategies and challenges, which were then organized into meaningful themes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The following six themes emerged from meeting minutes and debriefings: (1) automation of low-touch components, (2) reliance on internal champions, (3) intentional handoff communication, (4) institutional attention, (5) relaxed fidelity, and (6) continuous evaluation. Together, they highlight the importance of early, structured planning and adaptable implementation strategies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Sustainment of the HAS initiative required extensive communication, adaptation, and stakeholder engagement across diverse institutional contexts. Proactive sustainment planning during trial design may help ensure interventions are successfully integrated into routine clinical practice posttrial.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 4","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148783797","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":"Development of the Learning Health Systems Toolkit: A Collaborative Effort to Bridge Concept and Practice","authors":"Sarah Gilman,&nbsp;Jessica R. Phillips,&nbsp;Tom Foley,&nbsp;Philip J. van der Wees,&nbsp;Jeffrey Braithwaite,&nbsp;Kenneth Harwood,&nbsp;Robert Verheij,&nbsp;Joshua C. Rubin,&nbsp;Remco Benthem de Grave,&nbsp;Eleni Margariti,&nbsp;Paige L. McDonald","doi":"10.1002/lrh2.70111","DOIUrl":"https://doi.org/10.1002/lrh2.70111","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Learning communities need access to resources to support efforts in building and sustaining learning health systems. This experience report details our development and initial assessments of the Learning Health System (LHS) Toolkit, a menu of resources purpose-designed to support users to build more proactive, responsive, and equitable systems of care by developing, implementing and sustaining LHSs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Toolkit development began in 2022, and the current version was completed in 2025. To optimize knowledge translation, we structured our methods to align with the phases of the Knowledge to Action (KTA) Model, including (1) Knowledge Inquiry; (2) Synthesis and Creation; (3) Knowledge Selection; (4) Adapting Knowledge to Local Context; (5) Assessing Barriers and Facilitators to Knowledge Use; (6) Selecting, Tailoring, and Implementing the Intervention, and (7) Monitoring Knowledge Use.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Development of the web-based toolkit and vetting of its contents were achieved over several phases. Feedback from toolkit users was positive overall and was integral to its development and refinement. Users from 72 countries have accessed the toolkit, although most engagement has occurred in high-income countries, chiefly the US, UK, Türkiye, Australia, Canada, The Netherlands, and New Zealand.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The toolkit is uniquely situated to support learning communities. It includes a dynamic design, is regularly updated, and is accessible free of cost. Additionally, unlike a literature search, the toolkit is developed with usability in mind and includes tools beyond peer-reviewed literature, curated by LHS experts. The next steps are to develop and action a dissemination plan based on implementation science principles to increase the reach and adoption of the toolkit.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 4","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70111","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753679","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":"Automatic Conversion of NICE Guidelines to an Executable Computational Model Using Large Language Models","authors":"Ashvin Gupta,&nbsp;Denys Prociuk,&nbsp;Alessandra Russo,&nbsp;Brendan C. Delaney","doi":"10.1002/lrh2.70114","DOIUrl":"https://doi.org/10.1002/lrh2.70114","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>The UK National Institute for Health and Care Excellence (NICE) produce guidelines that provide evidence-based recommendations to support clinical care across England and Wales, but remain available in unstructured natural language form. Converting these guidelines into computable, logically coherent representations is an active area of research yet existing approaches typically focus on individual diseases, require substantial manual encoding, and do not scale. Recent advances in large language models offer an opportunity to automate much of this translation process.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We present an end-to-end approach that automatically converts textual clinical guidelines into an executable model capable of generating explainable patient-specific recommendations. Our approach uses a stepwise LLM-based transformation with in-context examples that can be customized to the guideline of your choice. Each step generates human-inspectable intermediate artifacts, ensuring full transparency and modifiability. We apply the approach to both pancreatic and lung cancer NICE guidelines and use expert human review to assess the alignment of the produced rules as well as evaluating the executable model over 20 pancreatic cancer patient vignettes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Human experts review demonstrated strong alignment between the natural language guidelines and the generated executable models, with the majority of guideline recommendations translated correctly. Most discrepancies involved partial omissions of specific details rather than incorrect logic, and instances of hallucinated or fundamentally incorrect rules were rare. When executed on the vignettes, the resulting executable models produced patient-specific recommendations with an <i>F</i>1 score of 82.5%.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This work demonstrates that LLMs can be used to automatically transform natural language NICE guidelines into interpretable and executable models. The models preserve guideline structure, allow transparent inspection and modification, and can be executed to generate patient-specific recommendations. Our findings highlight the feasibility of automated guideline generation, opening the door to scalable computable guidelines.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 4","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70114","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753678","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":"Ambient AI Scribes as Emerging Infrastructure in the Learning Health System","authors":"Taofeeq Oluwatosin Togunwa,&nbsp;Jodyn Platt","doi":"10.1002/lrh2.70115","DOIUrl":"10.1002/lrh2.70115","url":null,"abstract":"<p>Ambient artificial intelligence (AI) scribes are systems that automatically generate clinical documentation from clinician-patient conversations and are being deployed at accelerating pace across US health systems. Early evaluations report reduced documentation burden, improved clinician well-being, and perceived efficiency gains, reinforcing a narrative of inevitability. Yet this frontline framing understates a more consequential issue: ambient scribes outsource the “first mile” of clinical documentation, thereby reshaping the production of clinical data and the learning health systems (LHSs) that depend on documentation as foundational infrastructure. This paper argues that ambient AI scribes should be understood not merely as workflow tools, but as emerging infrastructure that will materially shape the capacity and capabilities of LHSs. Drawing on infrastructure studies and LHS frameworks, we conceptualize clinical documentation as the epistemic substrate through which encounters are translated into analyzable data that power quality measurement, predictive modeling, clinical decision support, and institutional learning. When this translation is algorithmically mediated by proprietary systems, design choices, training data, and integration pathways can introduce systematic documentation errors that propagate downstream, often invisibly, through analytic pipelines. Synthesizing emerging evidence, we highlight risks including hallucinated clinical details, omission of safety-critical information, and differential performance across patient populations with diverse accents or speech patterns. These risks mirror classic infrastructural properties described by Star: embeddedness, dependence on the installed base, wide propagation, and visibility primarily upon breakdown. From this perspective, ambient scribes may quietly reshape documentation norms, data quality, and learning trajectories well before downstream effects are routinely assessed. We conclude by outlining a governance agenda grounded in LHS principles: documentation-quality metrics, drift monitoring, equity-focused evaluation, transparency, and multi-stakeholder stewardship. Without such oversight, ambient AI scribes risk stabilizing an infrastructural layer that delivers short-term relief while eroding the long-term integrity, equity, and trustworthiness of learning health systems.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 4","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13456971/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148707963","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":"Leading Learning Health Systems in the Asia Pacific: A Mixed-Method Evaluation of Health Professionals' Experiences in a Longitudinal Leadership Program","authors":"Lichin Lim,&nbsp;Carolyn Van Heerden,&nbsp;Amy Gray","doi":"10.1002/lrh2.70109","DOIUrl":"10.1002/lrh2.70109","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>The Learning Health System (LHS) concept is gaining traction in high-income countries, yet its implementation in low- and middle-income countries (LMICs) remains limited with few initiatives offering practical strategies tailored to these contexts. The Leading a Learning Health System (LLHS) Program, grounded in LHS principles, aimed to build leadership capacities among health professionals and leaders from five Asia-Pacific LMICs. This study explored participants' learning experiences and applications in their context.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A mixed-method longitudinal study was conducted (13-months), informed by social constructivism and Kirkpatrick's evaluation framework. Surveys and semi-structured interviews were conducted after the intensive course (Survey A and Interview A) and 1 year later (Survey B and Interview B). Quantitative data were analyzed descriptively, and qualitative data were analyzed through inductive content analysis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Thirteen participants (13/20, 65%) completed Survey A. All (100%) rated the program and cross-country network as “high value.” Eleven participants (11/20, 55%) completed Survey B. All participants reported applying program learnings regularly on most days (6/11, 55%) or most weeks (5/11, 45%). Nine participants (82%) reported influencing team culture and five (45%) reported influencing organizational change. Qualitative analysis of 20 Interview A and 10 Interview B identified four themes: (1) developing a systems leadership identity, (2) translating leadership intent into practice, (3) contextual challenges to applying learnings, (4) resilience via adaptive leadership capacity and programmatic support.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This study demonstrates how a longitudinal leadership program based on the LHS framework supported LMIC health professionals to develop systems leadership identity, foster learning culture, and cultivate adaptive, distributed leadership to build resilience in resource-limited settings. The variability of health information systems in LMICs should not be seen as a barrier to implementing LHS, as improvement can begin with the effective use of locally available data. These findings can inform future capacity-building initiatives to advance LHSs in LMICs.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 4","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13449691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148698105","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":"Availability and Quality of Electronic Health Record Data to Track Physical Function Across a Care Transition","authors":"Robin L. Marcus,&nbsp;Kelly Daley,&nbsp;Margaret A. French,&nbsp;Anne Thackeray,&nbsp;Erik H. Hoyer,&nbsp;Donna Beck,&nbsp;Daniel L. Young","doi":"10.1002/lrh2.70110","DOIUrl":"10.1002/lrh2.70110","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Physical function (PF) is critical to quality of life and healthcare value, especially for older adults following hospitalization. Monitoring PF supports recovery, reduces adverse events, and improves care transitions. Despite the potential of electronic health records (EHRs) enabling systematic PF tracking, such data are rarely captured consistently. Here we examine the availability of PF-related data in EHRs for patients transitioning from hospital to homecare in a large health system, highlighting challenges and offering recommendations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We assessed availability of elements previously identified important to PF measurement from a single healthcare system. Working with Johns Hopkins Health System informatics and homecare leaders, we determined which recommended elements were captured in the EHR and which were feasible to extract within our resource constraints. We then requested an extraction of a refined data set for adult patients with a hospital admission between July 2016 and March 2021. After validation, data were securely transferred to University of Utah Health.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Data from 21 702 patients were included. Of 27 desired elements, 17 were available and successfully extracted. Individual elements were marked “present” if documented at least once during admission, or “missing” if absent. Administrative data had low missingness, although missingness for assessments of cognition and mobility performance in hospital was over 65%, and assessments of PF capacity in home health were missing in over 80% of patients. However, 81.7% of those receiving home health rehabilitation had the expected mobility measure. Overall, 73% of patients had at least 75% of the extracted data elements.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Assembling a comprehensive view of PF across a care transition using EHR data proved highly challenging. Our recommendations address data element identification, generation and storage; data extraction, cleaning, and validation; interoperability across care settings; adequate resources to manage complex data; and prospective infrastructure development.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 4","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13429356/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148670801","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 Standards-Based, Cloud-Hosted CDS System for Gonorrhea Treatment and HIV Screening","authors":"Edna Shenvi,&nbsp;Aziz Boxwala,&nbsp;Sarah Shaw,&nbsp;Andre Berro,&nbsp;Saugat Karki,&nbsp;Matthew Pooser,&nbsp;Jane E. Yang,&nbsp;Gema Dumitru,&nbsp;Jeanne Ocampo,&nbsp;Amrita Tailor,&nbsp;Sanjat Kanjilal,&nbsp;Carlos Paredes,&nbsp;Ritche Hao,&nbsp;Nitu Kashyap,&nbsp;David Liebovitz,&nbsp;Alejandro Pérez","doi":"10.1002/lrh2.70104","DOIUrl":"https://doi.org/10.1002/lrh2.70104","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p><i>Neisseria gonorrhoeae</i> (or gonococcus, GC) infections have increased in incidence and antibiotic resistance, prompting updates to management guidelines. Additionally, patients with recent GC infections have been identified as a target population for human immunodeficiency virus (HIV) screening and prevention. With the rapidly changing nature of such biomedical knowledge, there is an imperative to adopt public health guidelines speedily to deliver knowledge to clinicians at the point of care. Therefore, the Public Health Informatics Institute and the United States Centers for Disease Control and Prevention sought to create a scalable clinical decision support (CDS) solution that could be easily integrated using interoperability standards with widely used electronic health record (EHR) systems in the United States.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A project team including informaticians and clinical partners developed a cloud-hosted CDS system, with rule logic written in the Health Level Seven (HL7) Clinical Quality Language and using the HL7 CDS Hooks protocol. The system was designed to present alerts to clinicians who, based on available data, appeared to be treating uncomplicated GC with doses or agents that were not recommended based on the patient's weight, allergies, and other characteristics. Actionable suggestions were offered for the correct regimen, and HIV testing, if applicable.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>After extensive testing, the system was successfully integrated with the EHR system at two academic medical institutions. Subsequently, a three-month pilot was conducted in emergency and urgent care settings.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>There were limitations related to functionality, workflow issues, and data quality. This successful implementation, however, demonstrates the feasibility of this approach for select use cases of CDS, and yields further understanding of the requirements for developing scalable solutions for disseminating public health guidance.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 4","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616893","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":"Introduction to the Special Collection: Person-Centered Care Planning for Persons With Multiple Chronic Conditions","authors":"David A. Dorr,&nbsp;Ana R. Quiñones","doi":"10.1002/lrh2.70112","DOIUrl":"https://doi.org/10.1002/lrh2.70112","url":null,"abstract":"&lt;p&gt;Multiple chronic conditions (MCC) affect approximately 130 million American adults, representing a critical health care challenge that standard, fragmented disease management has failed to adequately resolve. Evidence increasingly supports person-centered care planning (PCCP)—defined as a longitudinal care approach that places patient goals, values, and preferences at the center of treatment—as a necessary standard of practice. To address the gaps in current approaches and care models for management of MCC, this special collection presents four manuscripts emerging from a multi-faceted initiative to advance PCCP for those with or at risk for MCC, supported collectively by funding from the Agency for Healthcare Research and Quality (AHRQ), the John A. Hartford Foundation, and the Patient-Centered Outcomes Research Institute (PCORI). Together, these papers evaluate the current evidence base, operational challenges, on-the-ground clinician and patient experiences, and the evidence-based integration of symptom management into primary care.&lt;/p&gt;&lt;p&gt;The collection opens with foundational work by Totten, Dorr, Bierman, and colleagues, who conducted an environmental scan that informed the broader AHRQ initiative. Through a rigorous multi-component review of published and gray literature, the authors identified 40 PCCP models currently in use across diverse US health care settings. These models share a common commitment to whole-person care but differ substantially in how they achieve it—through making changes in the way care is provided. The authors characterized this change in care as focused on team roles and composition, technology, payment structures, clinical functions, and care focus. Approximately two-thirds of evaluated models demonstrated better or equivalent outcomes compared with usual care. The scan was complemented by qualitative interviews with 18 key informants who were able to provide rich contextual details on what works, how approaches can be improved, and what is impeding broadscale change. Barriers to adoption were consistent across settings: insufficient time, misaligned payment structures, and the cultural challenge of reorienting care delivery from disease management to patient-defined goals. Facilitators included flexible team structures, alignment with organizational mission, and leadership support. The environmental scan makes clear that a strong evidence base exists—what is lacking is the infrastructure, incentives, and implementation support to bring these models to scale.&lt;/p&gt;&lt;p&gt;The summary article by Davis, Coury, Nagykaldi, and colleagues presents findings from the PCCP for Persons with MCC Summit, a culminating event convened at AHRQ headquarters in Rockville, Maryland in March 2025. Drawing on 14 months of prior work, the Summit brought together 115 participants across sectors, including researchers, payers, health system leaders, community organizations, patients, and policymakers. Three broad takeaways emerged as essent","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 4","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70112","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616461","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}