Learning Health Systems最新文献

{"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":"148094066","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}

{"title":"Identification of Patients for a Community Health Worker Program Using an Artificial Intelligence Algorithm.","authors":"Samuel T Savitz, Brendan Broderick, Margaret M Paul, Timethia J Bonner, Jennifer L Ridgeway, Mikaela Kall, Jane W Njeru","doi":"10.1002/lrh2.70089","DOIUrl":"10.1002/lrh2.70089","url":null,"abstract":"<p><strong>Introduction: </strong>Community health workers (CHWs) help patients navigate community resources. CHW programs can improve health outcomes and reduce healthcare utilization, but identifying eligible patients is challenging. We developed an electronic health record (EHR)-based algorithm trained to predict referrals to a CHW program. Our objective was to evaluate whether an algorithm trained on historical referrals identifies a subgroup of patients that differs from the subgroup identified through questionnaire-based health-related social needs (HRSN) screening alone.</p><p><strong>Methods: </strong>This analysis used data from Mayo Clinic primary care patients in Southeast Minnesota. We developed a gradient boosted time-to-event model that incorporated demographics, diagnoses, HRSN questionnaire responses, and HRSNs derived from clinical notes using natural language processing. We compared the characteristics of algorithm-identified patients with those identified through questionnaires and assessed the variable importance of model features.</p><p><strong>Results: </strong>The algorithm had AUC: 0.87 in the training and 0.92 in the hold out set. Patients flagged by the algorithm tended to use interpreter services more commonly (12.6% vs. 1.3%), have a non-English preferred language (14.1% vs. 2.3%), and have more instances of health literacy documented (5.36 vs. 1.29) compared to patients identified by the HRSN questionnaire. Only 47.0% of patients flagged by the algorithm had responded to the HRSN questionnaire within 2 years. Variable importance was highest for health literacy and financial strain from unstructured notes.</p><p><strong>Conclusions: </strong>The algorithm is a chart-review prioritization tool for referral to the CHW program for patients that may overcome challenges to identifying patients with HRSNs, especially when questionnaire data is incomplete.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 Suppl 1","pages":"e70089"},"PeriodicalIF":2.3,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13239720/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148200551","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":"Shared Experiences and Care Improvement Priorities for Multimorbidity Management: An Experience-Based Co-Design Study","authors":"Binu Koirala,&nbsp;Chitchanok Benjasirisan,&nbsp;Arum Lim,&nbsp;Katie E. Nelson,&nbsp;Lyndsay DeGroot,&nbsp;Rebecca Wright,&nbsp;Cheryl R. Dennison Himmelfarb,&nbsp;Patricia M. Davidson","doi":"10.1002/lrh2.70096","DOIUrl":"https://doi.org/10.1002/lrh2.70096","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Multimorbidity—coexistence of two or more chronic conditions in the same individual—is a growing global healthcare challenge. Despite recognition of the difficulties in managing multimorbidity, there is a limited understanding of lived experiences and care improvement priorities as identified by key stakeholders.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>The study aimed to explore lived experiences of patients with multimorbidity, their family caregivers, and interprofessional healthcare workers, and to identify shared care improvement priorities. These findings were synthesized into a multimorbidity management toolkit.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>An experience-based co-design study with six stages was implemented, considering human-centered design. Participants included patients aged 50 years and above living with heart failure and multimorbidity, family caregivers, and multidisciplinary healthcare professionals recruited in Maryland, USA. Following study preparation and ethical approval (Stage 1), in-depth individual interviews (Stages 2 and 3) were conducted, and the results were discussed during co-design events and working group meetings (Stages 4 and 5) to reach a consensus on care priorities and specific strategies. The study findings were summarized into a multimorbidity management toolkit that was shared with participants at a celebration event (Stage 6). Data analysis included descriptive statistics and inductive thematic methods.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Forty-six individual interviews were conducted: 24 patients (mean ± SD age 71 ± 9 years; 54% women; with an average of 8 morbidities), 7 family caregivers (mean ± SD age 64 ± 14 years; 57% women), and 15 healthcare professionals (mean ± SD age 36 ± 10 years). A total of 18 participants attended two co-design events. Multiple working group meetings were organized to receive diverse input. Four shared care improvement priorities emerged: (1) communication, (2) care coordination and follow-up care, (3) mental and emotional support, and (4) health education for patients and caregivers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Person-centered care models that emphasize shared strategies to improve communication, care coordination, education, and emotional support, as outlined in the toolkit, may enhance multimorbidity management and patient outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70096","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148174634","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":"148094065","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}

{"title":"RescueGPT: An Automated System for Detecting Adverse Safety Events in Prehospital Emergency Medical Service Notes With a Zero-Shot Approach With Large Language Models: A Proof-of-Concept Study.","authors":"Tina Yi Jin Hsieh, Carl Eriksson, Garth Meckler, Matthew Hansen, Nathan Bahr, Steven Bedrick, Diego Trujillo, Kyu Seo Kim, Byeongyeon Cho, Kai Zhang, Xiaoqian Jiang, Jeanne-Marie Guise","doi":"10.1002/lrh2.70088","DOIUrl":"10.1002/lrh2.70088","url":null,"abstract":"<p><strong>Introduction and objective: </strong>Traditional adverse safety events (ASE) identification relies on domain experts to manually review and annotate charts, which hinders the scalability of processing high-volume EMS data. This study explores the use of large language model (LLM) with a knowledge base to automate extraction of adverse safety events (ASE) from unstructured emergency medical service (EMS) notes for pediatric out-of-hospital cardiac arrest (OHCA) as proof of concept.</p><p><strong>Data sources and study design: </strong>Pediatric OHCA records from a national EMS provider were obtained from 2017 to 2020. Leveraging the Pediatric Prehospital Adverse Safety Event Detection System (PEDS) as a foundational knowledge base, we used the LinkML framework to develop an ontology to define ASEs across six essential EMS care domains. To convert unstructured EMS narratives into structured prompts, we used the Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES) method, which generated schema-driven prompts to guide the GPT-3.5 model in identifying ASEs. By mapping unstructured data into structured concepts consistent with PEDS guidelines, the model produced targeted prompts that supported effective entity extraction.</p><p><strong>Results: </strong>We evaluated framework effectiveness with accuracy, recall, precision, F1 score, and specificity across 42 pediatric OHCA cases covering ASE-related entities. RescueGPT showed high accuracy in detecting common ASEs (Patient Rhythm, Age, Weight, Length) but revealed challenges in rare events (Failure to Establish IV Access, Incorrect Airway Equipment Size, Failure to Ventilate Patient) likely due to more inconsistent and complex documentation.</p><p><strong>Conclusions: </strong>RescueGPT demonstrates potential in scaling automated ASE detection, but performance varies by completeness and clarity of EMS narrative, particularly with rare events. Fragmented clinical documentation limits accuracy and highlights the need for standardized collection protocols in EMS systems. Future directions will focus on implementing rebalancing strategies for rare events, applying explainability methods to improve decision-making transparency, and refining text segmentation techniques to handle mixed outcomes to further improve performance.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 Suppl 1","pages":"e70088"},"PeriodicalIF":2.3,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13239832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148200515","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":"Knowledge Traffic in Learning Health Systems: A Conceptual Framework for Organizational Phenotyping and Translational Governance","authors":"Octavian C. Ioachimescu","doi":"10.1002/lrh2.70095","DOIUrl":"10.1002/lrh2.70095","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The translational journey from biomedical discovery to population health is increasingly complex, requiring coordinated navigation across scientific, clinical, and operational domains. While the Learning Health System (LHS) is a unifying platform developed to accelerate knowledge cycles, evidence generation, testing, and application, its role in governing translational flows across different organization types remains insufficiently specified.&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;This perspective article proposes a conceptual, structured approach and taxonomy that links translational spectrum (&lt;i&gt;T&lt;/i&gt;&lt;sub&gt;0&lt;/sub&gt;–&lt;i&gt;T&lt;/i&gt;&lt;sub&gt;5&lt;/sub&gt;) to the health system phenotype and identifies the LHS as the governance and operational layer that enables safe, efficient, and speed-appropriate translation from &lt;i&gt;T&lt;/i&gt;&lt;sub&gt;4&lt;/sub&gt; to &lt;i&gt;T&lt;/i&gt;&lt;sub&gt;5&lt;/sub&gt;, where competing translational pathways converge. The proposed phenotyping schema is aimed at optimizing LHS configuration and translational performance.&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 synthesize prior work on translational science, LHS design, and organizational capability into an analytic typology (LHS&lt;sub&gt;1&lt;/sub&gt;–LHS&lt;sub&gt;4&lt;/sub&gt;) based on innovation generation and adoption, and their specific investigative capabilities. This new organizational typology shifts the LHS concept from a descriptive model to an actionable design and governance tool, enabling organizational leaders to align infrastructure, strategy, and translational intent.&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;Health systems differ in how they generate, test, and adopt new knowledge, yet all require explicit governance, prioritization, data infrastructure, stakeholder engagement, and improvement capacity to move evidence into routine care and population impact. The LHS&lt;sub&gt;1&lt;/sub&gt;–LHS&lt;sub&gt;4&lt;/sub&gt; are presented as descriptive phenotypes with distinct capability profiles and operationalizations, highlighting the central role of LHS governance in aligning priorities, resources, and implementation pathways.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;By explicitly linking system phenotype to translational function, leaders can build a robust foundation for operational decision-making and targeted investments, while clarifying the rights, rules, and permissions needed for safe knowledge movement, and offering a basis for evaluation, implementation, and further empirical t","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13180781/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147976161","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":"Foundation for Artificial Intelligence-Driven Democratization of Healthcare Access","authors":"Sandeep Reddy","doi":"10.1002/lrh2.70093","DOIUrl":"10.1002/lrh2.70093","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;Healthcare systems worldwide face unprecedented challenges, including escalating costs, workforce shortages, and access disparities, which threaten their sustainability. The WHO projects an 18 million healthcare worker deficit by 2030, while financial and geographical barriers prevent millions from receiving necessary care. The integration of Artificial Intelligence (AI) into healthcare delivery systems presents opportunities to transform medical service provision, accessibility, and experiences, potentially democratizing healthcare access.&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 perspective analysis employs a theoretical framework combining Levesque et al.'s patient-centered healthcare access model with AI democratization frameworks. The analysis synthesizes current evidence on AI healthcare applications and proposes an implementation framework encompassing four dimensions: accessibility, affordability, usability, and ethical regulation. The framework addresses stakeholder roles and governance mechanisms aligned with international standards including the EU's AI Act and WHO's AI ethics guidance.&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;Evidence demonstrates significant democratization potential through the implementation of AI. AI-powered platforms eliminate geographical barriers, reduce diagnostic timeframes, optimize resources, and enhance preventive care. Implementation challenges include algorithmic bias, data privacy concerns, digital divide risks, and regulatory fragmentation.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusion&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;AI integration holds transformative potential for democratizing healthcare across demographic and socioeconomic boundaries. Successful implementation requires structured, ethically grounded approaches that prioritize accessibility, affordability, usability, and regulation while maintaining a human-centered care approach. The framework offers actionable guidance for healthcare professionals and policymakers on deploying AI technologies to reduce disparities. Continuous research, interdisciplinary collaboration, and robust governance are crucial to ensuring that AI advances healthcare equity while preserving patient autonomy and clinical judgment.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Patient or Public Contribution&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Patient and Public Involvement and Engagement was not appropriate for this theoretical framework and perspective analysis, as it represents a conceptual ","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13159101/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147934468","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":"Assessing the Importance of Variation in Diagnostic Coding Among the Three Countries in the UK Biobank","authors":"Lei Clifton,&nbsp;Wenyu Liu,&nbsp;Jennifer A. Collister,&nbsp;Thomas J. Littlejohns,&nbsp;Raphael R. Goldacre,&nbsp;Naomi Allen,&nbsp;David J. Hunter","doi":"10.1002/lrh2.70083","DOIUrl":"10.1002/lrh2.70083","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The UK Biobank (UKB) study has linked hospital inpatient data collected from England, Scotland, and Wales, which use different clinical coding systems to record health outcomes. Scotland records up to 6 different diagnostic codes for one inpatient episode, compared with up to 20 in England and 14 in Wales. We assessed the relationship of the variations in diagnostic coding among countries on observed disease incidence rates.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We examined the number of diagnoses coded by each country, and then compared the incidence of three diseases between countries: Parkinson's disease (PD), type 2 diabetes (T2D), and dementia. We constructed Cox models for each disease, adjusting for “country.”</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Compared with England, Scotland appears to have the lowest risk (hazard ratio, HR) for all three diseases: HR [95% CI] = 0.62 [0.54, 0.72] for PD, 0.49 [0.45, 0.54] for T2D, and 0.88 [0.78, 0.99] for dementia.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The observed incidence of these diseases and the estimated effect of “country” in Cox models are likely influenced by the clinical coding variations among countries. Researchers need to be aware of this and account for these variations in their analyses.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13142220/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147844111","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":"Supporting Cultural and Learning Enablers for a Learning Health System (SCALE): A Program Theory Approach","authors":"Laura Desveaux,&nbsp;Ruoxi Wang,&nbsp;Jayshree Joshi,&nbsp;Marissa Bird","doi":"10.1002/lrh2.70082","DOIUrl":"10.1002/lrh2.70082","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Health systems around the world are investing in learning health system (LHS) infrastructure to strengthen their capacity to adapt, improve, and respond in real time. While prior work has focused on technical infrastructure, less is known about the conditions that enable learning to happen as envisioned. We aimed to address this knowledge gap by developing a multi-level, empirically grounded theory for operationalizing organizational attributes and leadership practices that influence learning in LHS.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We adopted an iterative, theory-informed qualitative approach inspired by realist evaluation principles. Drawing on organizational and LHS literature, we developed an initial program theory hypothesizing key antecedents of organizational learning and conducted semi-structured interviews with formal leaders in a large community hospital using a “teacher-learner cycle” to test and refine the initial program theory.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Based on interviews with 12 formal leaders, we developed a mid-range program theory outlining how structural, leadership, and cultural factors interact to create (or impede) the conditions for organizational learning in LHS. Resource investment, organizational governance, shared organizational identity, and leadership approaches emerged as key factors. These factors influence both the relational dynamics and execution dynamics that drive how work is done, determining whether the culture is characterized by learning catalysts or inertial loops.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Leadership practices shape the cultural conditions that enable or stall system-level learning in LHS. While structural supports matter, they do not create the conditions for learning without leadership practices that reinforce clarity, connection, and curiosity.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13129495/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147821802","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":"Correction to \"Learning Health Systems Symposium: Charting the Future of Saskatchewan Healthcare\".","authors":"","doi":"10.1002/lrh2.70081","DOIUrl":"10.1002/lrh2.70081","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1002/lrh2.70060.].</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 ","pages":"e70081"},"PeriodicalIF":2.3,"publicationDate":"2026-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13111144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147784591","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}