Learning Health Systems最新文献

{"title":"Four distinct models of learning health systems: Strength through diversity","authors":"Charles P. Friedman,&nbsp;Sarah M. Greene","doi":"10.1002/lrh2.70009","DOIUrl":"https://doi.org/10.1002/lrh2.70009","url":null,"abstract":"<p>The concept of a learning health system (LHS) was established nearly 20 years ago as a unifying commitment to speed the generation and use of evidence primarily by leveraging rapid advances in data and technologies, resulting in optimized care for each patient. In the ensuing decades, vanguard adopters of the LHS who have sought to move the LHS from conceptual to operational have done so in ways that fit with and reflect their organizational structure, mission, and culture—as well as their personal values and experiences. They have also extended the focus from health care to include individual and population health more broadly. This commentary describes four distinctive models that have evolved as learning health system activities have matured. Viewing this diversity as a strength, the features, commonalities, and unique differences of these models are described.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835793","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":"2024 Advances in Learning Health System Sciences (AiLHSS) Conference: Abstracts","authors":"","doi":"10.1002/lrh2.70000","DOIUrl":"https://doi.org/10.1002/lrh2.70000","url":null,"abstract":"&lt;p&gt;The &lt;i&gt;Advances in Learning Health System Sciences&lt;/i&gt; (&lt;i&gt;AiLHSS) Conference&lt;/i&gt; is an annual event that addresses pressing and emerging Learning Health System (LHS) topics while fostering collaboration among researchers advancing the field presented by the University of Minnesota (UMN) Center for Learning Health System Sciences (CLHSS), a collaboration between the Medical School and School of Public Health at the UMN. The 2024 conference was held on September 9–10 at Coffman Memorial Union at UMN in Minneapolis, MN. This year's conference featured 219 attendees including clinicians, researchers, policymakers, and trainees, with local, national, and international representation from professional organizations, research institutes, healthcare organizations, and universities serving the needs of diverse populations such as children, the elderly, rural communities, and veterans. The agenda was composed of keynote presentations, scientific forums, workshops, and poster sessions all designed to foster collaboration and innovation in the field of LHS. Throughout the conference, attendees had the opportunity to engage in various networking sessions. This year's agenda highlighted the importance of collaboration and continuous learning in improving healthcare delivery, with a focus on practical tools, real-world examples, and the integration of patient and community perspectives.&lt;/p&gt;&lt;p&gt;On the first day, September 9, the conference began with coffee and networking, followed by welcome and opening remarks from Genevieve Melton-Meaux, MD, PhD (Director, CLHSS), and Jakub Tolar, MD, PhD (Dean, UMN Medical School). The morning sessions included a keynote by Sarah Greene, MPH, from the National Academy of Medicine discussing the complexities and opportunities in building and sustaining learning health systems. This was followed by a spotlight session and a panel on active data sharing collaboratives. The afternoon featured scientific forums on topics such as organ donation data, opioid substance use treatment, and cardiac imaging in stroke evaluation, as well as a workshop on partnering with patients to improve study design and outcomes. The day ended with a poster session which included networking opportunities.&lt;/p&gt;&lt;p&gt;The second day, September 10, started with coffee and networking, followed by opening remarks from Timothy Beebe, PhD (Deputy Director, CLHSS), and a keynote by Sara Singer, PhD, MBA, from Stanford University on the importance of learning in health systems. The morning sessions included evidence synthesis case studies and a spotlight session on implementation science reporting and adherence challenges. The conference concluded with a keynote by Angela D. Thomas, DrPH, MPH, MBA, from MedStar Health discussing the realities of being a learning health system.&lt;/p&gt;&lt;p&gt;This article features a curated collection of abstracts from spotlight sessions, oral presentations, and the highest-scored posters, all of which were recognized for their innovative","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836471","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":"2024 MCBK North American chapter meeting—Lightning talk and demonstration abstracts","authors":"","doi":"10.1002/lrh2.10479","DOIUrl":"https://doi.org/10.1002/lrh2.10479","url":null,"abstract":"&lt;p&gt;&lt;b&gt;POSTERS&lt;/b&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;DEMONSTRATIONS&lt;/b&gt;&lt;/p&gt;&lt;p&gt;Saketh Boddapati, University of Michigan College of Literature, Science, and the Arts&lt;/p&gt;&lt;p&gt;&lt;span&gt;[email protected]&lt;/span&gt;&lt;/p&gt;&lt;p&gt;Yongqun “Oliver” He, University of Michigan Medical School&lt;/p&gt;&lt;p&gt;&lt;span&gt;[email protected]&lt;/span&gt;&lt;/p&gt;&lt;p&gt;Healthcare providers learn continuously as a core part of their work. However, as the rate of knowledge production in biomedicine increases, better support for providers' continuous learning is needed. Tools for learning from clinical data are widely available in the form of clinical quality dashboards and feedback reports. However, these tools seem to be frequently unused.&lt;/p&gt;&lt;p&gt;Making clinical data useful as feedback for learning appears to be a key challenge for health systems. Feedback can include coaching, evaluation, and appreciation, but systems developed for performance improvement do not adequately recognize these purposes in the context of provider learning. Moreover, providers have different information needs, motivational orientations, and workplace cultures, all of which affect the usefulness of data as feedback.&lt;/p&gt;&lt;p&gt;To increase the usefulness of data as feedback, we developed a Precision Feedback Knowledge Base (PFKB) for a precision feedback system. PFKB contains knowledge about how feedback influences motivation, to enable the precision feedback system to compute a motivational potential score for possible feedback messages. PFKB has four primary knowledge components: (1) causal pathway models, (2) message templates, (3) performance measures, and (4) annotations of motivating information in clinical data. We also developed vignettes about 7 diverse provider personas to illustrate how the precision feedback system uses PFKB in the context of anesthesia care. This ongoing research includes a pilot study that has demonstrated the technical feasibility of the precision feedback system, in preparation for a trial of precision feedback in an anesthesia quality improvement consortium.&lt;/p&gt;&lt;p&gt;Bruce Bray, University of Utah, on behalf of the HL7 Learning Health Systems Work Group&lt;/p&gt;&lt;p&gt;&lt;span&gt;[email protected]&lt;/span&gt;&lt;/p&gt;&lt;p&gt;Data is the lifeblood of computable biomedical knowledge (CBK) and must adhere to standards to achieve the interoperability needed to generate virtuous learning cycles within a learning health system (LHS). The HL7 Learning Health System Work Group (HL7 LHS WG) conducted a scoping review to compile an initial list of standards that can support the LHS across “quadrants” of a virtuous learning cycle: (1) knowledge to action, (2) action to data, (3) data to evidence, and (4) evidence to knowledge. We found that few standards explicitly refer to an overarching framework that aligns interoperability and data standards across the phases of the LHS. We will describe our initial work to identify relevant gaps and overlaps in standards in this environment. Future work should address standards coordination and pilot testing within an LHS framework. The","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10479","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143111476","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 machine learning approach to predicting inpatient mortality among pediatric acute gastroenteritis patients in Kenya","authors":"Billy Ogwel,&nbsp;Vincent H. Mzazi,&nbsp;Bryan O. Nyawanda,&nbsp;Gabriel Otieno,&nbsp;Kirkby D. Tickell,&nbsp;Richard Omore","doi":"10.1002/lrh2.10478","DOIUrl":"https://doi.org/10.1002/lrh2.10478","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Mortality prediction scores for children admitted with diarrhea are unavailable, early identification of at-risk patients for proper management remains a challenge. This study utilizes machine learning (ML) to develop a highly sensitive model for timelier identification of at-risk children admitted with acute gastroenteritis (AGE) for better management.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We used seven ML algorithms to build prognostic models for the prediction of mortality using de-identified data collected from children aged &lt;5 years hospitalized with AGE at Siaya County Referral Hospital (SCRH), Kenya, between 2010 through 2020. Potential predictors included demographic, medical history, and clinical examination data collected at admission to hospital. We conducted split-sampling and employed tenfold cross-validation in the model development. We evaluated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the curve (AUC) for each of the models.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>During the study period, 12 546 children aged &lt;5 years admitted at SCRH were enrolled in the inpatient disease surveillance, of whom 2271 (18.1%) had AGE and 164 (7.2%) subsequently died. The following features were identified as predictors of mortality in decreasing order: AVPU scale, Vesikari score, dehydration, sunken eyes, skin pinch, maximum number of vomits, unconsciousness, wasting, vomiting, pulse, fever, sunken fontanelle, restless, nasal flaring, diarrhea days, stridor, &lt;90% oxygen saturation, chest indrawing, malaria, and stunting. The sensitivity ranged from 46.3%–78.0% across models, while the specificity and AUC ranged from 71.7% to 78.7% and 56.5%–82.6%, respectively. The random forest model emerged as the champion model achieving 78.0%, 76.6%, 20.6%, 97.8%, and 82.6% for sensitivity, specificity, PPV, NPV, and AUC, respectively.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study demonstrates promising predictive performance of the proposed algorithm for identifying patients at risk of mortality in resource-limited settings. However, further validation in real-world clinical settings is needed to assess its feasibility and potential impact on patient outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10478","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836302","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":"Setting the foundation for a national collaborative learning health system in acute TBI rehabilitation: CARE4TBI Year 1 experience","authors":"Cynthia L. Beaulieu,&nbsp;Jennifer Bogner,&nbsp;Chad Swank,&nbsp;Kimberly Frey,&nbsp;Mary K. Ferraro,&nbsp;Candace Tefertiller,&nbsp;Timothy R. Huerta,&nbsp;John D. Corrigan,&nbsp;Erinn M. Hade","doi":"10.1002/lrh2.10454","DOIUrl":"https://doi.org/10.1002/lrh2.10454","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>A learning health system (LHS) approach is a collaborative model that continuously examines, evaluates, and re-evaluates data eventually transforming it into knowledge. High quantity of high-quality data are needed to establish this model. The purpose of this article is to describe the collaborative discovery process used to identify and standardize clinical data documented during daily multidisciplinary inpatient rehabilitation that would then allow access to these data to conduct comparative effectiveness research.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>CARE4TBI is a prospective observational research study designed to capture clinical data within the standard inpatient rehabilitation documentation workflow at 15 TBI Model Systems Centers in the US. Three groups of stakeholders guided project development: therapy representative work group (TRWG) consisting of frontline therapists from occupational, physical, speech-language, and recreational therapies; rehabilitation leader representative group (RLRG); and informatics and information technology team (IIT). Over a 12-month period, the three work groups and research leadership team identified the therapeutic components captured within daily documentation throughout the duration of inpatient TBI rehabilitation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Data brainstorming among the groups created 98 distinct categories of data with each containing a range of data elements comprising a total of 850 discrete data elements. The free-form data were sorted into three large categories and through review and discussion, reduced to two categories of prospective data collection—session-level and therapy activity-level data. Twelve session data elements were identified, and 54 therapy activities were identified, with each activity containing discrete sub-categories for activity components, method of delivery, and equipment or supplies. A total of 561 distinct meaningful data elements were identified across the 54 activities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>The CARE4TBI data discovery process demonstrated feasibility in identifying and capturing meaningful high quantity and high-quality treatment data across multiple disciplines and rehabilitation sites, setting the foundation for a LHS coalition for acute traumatic brain injury rehabilitation.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10454","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836351","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 and launch of a regional learning network to improve physical and mental health outcomes","authors":"Ndidi Unaka,&nbsp;Jeff Steller,&nbsp;Sarah Eaton,&nbsp;Brandy Seger,&nbsp;Jessica M. McClure,&nbsp;Mona Mansour,&nbsp;Kate Rich,&nbsp;Andrew F. Beck,&nbsp;Mary Carol Burkhardt,&nbsp;Nicole Lacasse,&nbsp;Crystal Robinson,&nbsp;Jeff Anderson","doi":"10.1002/lrh2.10462","DOIUrl":"https://doi.org/10.1002/lrh2.10462","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Care gaps in routine and preventive care are common among youth. To close care gaps, health systems should take a population health approach and create opportunities for partnership, collaboration, shared learning, and scale via learning networks (LNs).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We describe the Pediatric Improvement Network for Quality (PINQ), a regional population health LN with the aim of closing well-child and mental and behavioral health (MBH) care gaps. We initially launched PINQ with 2 primary care domains: well-child care (WCC) and MBH and later added the third domain of PINQ focused on community MBH organizations. We defined measures for the primary care WCC (well-child visits for infants 0–15 months; lead screening by 2 years of age, childhood immunization status 3 completion) and MBH domains (depression screening in youth 12–17 years, 30-day follow-up for positive depression screen, mental health emergency department utilization) and established system-level key drivers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>PINQ launched in September 2022 with 7 teams (5 in primary care WCC and 2 in primary care MBH domains, respectively). All teams participate in a monthly meeting that alternates between the Action Period call and Solutions Labs. We highlight two case studies that illustrate the impact of shared learning and quality improvement support on Improvement Team efforts.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>We foresee PINQ as a means of moving the needle toward high quality, comprehensive health care for Greater Cincinnati youth. The next steps include growing PINQ by adding Improvement Teams and expanding the network focus to include other primary care-centric metrics and conditions.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10462","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835894","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 sites for health systems research: Reflections on five programs in Africa, Asia, and Central America","authors":"Sophie Witter,&nbsp;Shophika Regmi,&nbsp;Joanna Raven,&nbsp;Jacinta Nzinga,&nbsp;Maria van der Merwe,&nbsp;Walter Flores,&nbsp;Lucia D'Ambruoso","doi":"10.1002/lrh2.10475","DOIUrl":"https://doi.org/10.1002/lrh2.10475","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Learning sites have supported intervention development and testing in health care, but studies reflecting on lessons relating to their deployment for health policy and system research (HPSR) in low- and middle-income settings are limited.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This experience report draws from learning over three continents and five research and community engagement programs—the oldest starting in 2010—to reflect on the challenges and benefits of doing embedded HPSR in learning sites, and how those have been managed. Its objective is to generate better understanding of their potential and constraints. The report draws from team members' experiential insights and program publications.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Challenges relating to initial engagement in the sites included building and maintaining trust, managing partner expectations, and negotiating priority topics and stakeholders. Once the embedded research was underway, sustaining engagement, and managing power dynamics within the group, supporting all participants in developing new skills and managing rapidly changing settings were important. Finally, the complexity of reflecting on action and assessing impact are outlined, along with potential approaches to managing all of these challenges and the variety of gains that have been noted across the programs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>We highlight the potential of learning sites to develop relationships, capacities, and local innovations which can strengthen health systems in the long term and some lessons in relation to how to do that, including the importance of stable, long-term funding as well as developing and recognizing facilitation skills among researchers. Supporting spaces for learning is particularly important when health systems face resource constraints and everyday or acute stressors and shocks.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10475","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835719","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":"Analyzing electronic medical records to extract prepregnancy morbidities and pregnancy complications: Toward a learning health system","authors":"Yitayeh Belsti,&nbsp;Lisa Moran,&nbsp;Aya Mousa,&nbsp;Rebecca Goldstein,&nbsp;Daniel Lorber Rolnik,&nbsp;Mahnaz Bahri Khomami,&nbsp;Mihiretu M. Kebede,&nbsp;Helena Teede,&nbsp;Joanne Enticott","doi":"10.1002/lrh2.10473","DOIUrl":"https://doi.org/10.1002/lrh2.10473","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Preexisting and pregnancy-related medical conditions frequently co-occur, leading to multimorbidity (≥2 morbidities) in pregnant women, and much of this information is in semi-structured format in electronic medical records (EMRs). The aim was to advance the learning health system as a platform for automating information extraction from EMRs and to uncover the prevalence of common morbidities during pregnancy and their association with pregnancy-related complications.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This study included 48 502 pregnant women attending Monash Health maternity hospitals from 2016 to 2021. Natural language processing (NLP) was used to extract morbidities from semi-structured text in EMRs. Chi-squared tests were used to assess the association between morbidities of gestational diabetes mellitus (GDM) and other pregnancy complications. The <i>k</i>-means clustering algorithm identified clusters of comorbid conditions associated with GDM.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The most common comorbidities during pregnancy were vitamin deficiency (14 019; 28.9%), overweight (13 918; 28.7%), obesity (11 026; 22.7%), anemia and other blood-related disorders (4821; 9.9%), mental health disorders (4314; 9.8%), asthma (4126; 8.5%), thyroid diseases (3576; 7.4%), endometrial disease (1927; 3.9%), cardiovascular disease (1525; 3.1%), and polycystic ovary syndrome (PCOS) (1464; 3.0%). While 22.5% of women had no medical conditions, 77.5% had one or more. Multimorbidity was associated with conditions including overweight, obesity, vitamin deficiency, thyroid disease, substance use, PCOS, GDM, and endometrial diseases. On cluster analysis, aged 35 years or older, overweight, vitamin deficiency, obesity, thyroid disease, asthma, uterine disease, other blood disorders, mental disorders, and PCOS were associated with GDM.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>More than three-quarters of pregnant women in the Australian urban setting experienced one or more morbidities during pregnancy, which can be associated with adverse pregnancy outcomes. This project contributes to developing a learning health system infrastructure to deliver high-value maternal health care while reducing costs.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10473","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836479","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":"Pre-implementation patient, provider, and administrator perspectives of remote measurement-based care in a safety net outpatient psychiatry department","authors":"Lisa C. Rosenfeld,&nbsp;Miriam C. Tepper,&nbsp;Stephen H. Leff,&nbsp;Daisy Wang,&nbsp;Alice Zhang,&nbsp;Lia Tian,&nbsp;Eileen Huttlin,&nbsp;Carl Fulwiler,&nbsp;Rajendra Aldis,&nbsp;Philip Wang,&nbsp;Jennifer Stahr,&nbsp;Norah Mulvaney-Day,&nbsp;Margaret Lanca,&nbsp;Ana M. Progovac","doi":"10.1002/lrh2.10472","DOIUrl":"https://doi.org/10.1002/lrh2.10472","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;Psychiatric measurement-based care (MBC) can be more effective than usual care, but health systems face implementation challenges. Achieving attitudinal alignment before implementing MBC is critical, yet few studies incorporate perspectives from multiple stakeholders this early in planning. This analysis identifies alignment and themes in pre-implementation feedback from patients, providers, and administrators regarding a planned MBC implementation in a safety net psychiatry clinic.&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 used interview guides informed by Conceptual Model of Implementation Research to gather qualitative pre-implementation attitudes about perceived Appropriateness, Acceptability, and Feasibility of an MBC measure (Computerized Adaptive Test—Mental Health; CAT-MH) from five patients, two providers, and six administrators. We applied rapid qualitative analysis methods to generate actionable feedback for department leadership still planning implementation. [Correction added on 22 January 2025, after first online publication: In the previous sentence, the word ‘general’ was replaced with the word ‘generate’.] We used a multistep process to generate thematic findings with potential relevance for other similar mental health settings.&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;There was more attitudinal alignment across stakeholder groups regarding MBC's Acceptability and Feasibility than its Appropriateness. All three groups agreed that it was important to contextualize MBC for patients and providers, anticipate MBC's impact on patient–provider relationships, and consider the system's capacity to respond to patient needs unearthed by CAT-MH before implementation began. Our thematic analysis suggests: (1) Introducing MBC may complicate patient–provider relationships by adding a new and potentially conflicting input for decision making, that is, MBC data, to the more typical inputs of patient report and provider expertise; [Correction added on 22 January 2025, after first online publication: In the previous sentence, the word ‘complicated’ was replaced with the word ‘complicate’.] (2) MBC poses theoretical risks to health equity for safety net patients because of limitations in access to MBC tools themselves and the resources needed to respond to MBC data; and (3) Tension exists between individual- and system-level applications of MBC.&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;Our analysis highlights shifting treatment dynamics, equity considerations, and tension between individu","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10472","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836443","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 Academic Community Early Psychosis Intervention Network: Toward building a novel learning health system across six US states","authors":"Jenifer L. Vohs,&nbsp;Vinod Srihari,&nbsp;Alexandra H. Vinson,&nbsp;Adrienne Lapidos,&nbsp;John Cahill,&nbsp;Stephan F. Taylor,&nbsp;Stephan Heckers,&nbsp;Ashley Weiss,&nbsp;Serena Chaudhry,&nbsp;Steve Silverstein,&nbsp;Ivy F. Tso,&nbsp;Nicholas J. K. Breitborde,&nbsp;Alan Breier","doi":"10.1002/lrh2.10471","DOIUrl":"https://doi.org/10.1002/lrh2.10471","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Compared to usual care, specialty services for first-episode psychosis (FES) have superior patient outcomes. The Early Psychosis Intervention Network (EPINET), comprised of eight U.S. regional clinical networks, aims to advance the quality of FES care within the ethos of learning healthcare systems (LHS). Among these, the Academic Community (AC) EPINET was established to provide FES care, collect common data elements, leverage informatics, foster a culture of continuous learning and quality improvement, and engage in practice-based research.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We designed and implemented a novel LHS of university-affiliated FES programs within a hub (academic leadership team) and spoke (FES clinics) model. A series of site implementation meetings engaged stakeholders, setting the stage for a culture that values data collection and shared learning. We built clinical workflows to collect common data elements at enrollment and at consecutive 6-month intervals in parallel to an informatics workflow to deliver outcome visualizations and drive quality improvement efforts.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>All six clinical sites successfully implemented data capture workflows and engaged in the process of designing the informatics platform. Upon developing the structure, processes, and initial culture of the LHS, a total of 614 patients enrolled in AC-EPINET, with the most common primary diagnoses of schizophrenia (32.1%) and unspecified psychotic disorders (23.6%). Visualized outcomes were delivered to clinical teams who began to consider locally relevant quality improvement projects.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>AC-EPINET is a novel LHS, with a simultaneous focus on science, informatics, incentives, and culture. The work of developing AC-EPINET thus far has highlighted the need for future LHS’ to be mindful of the complexities of data security issues, develop more automated informatic workflows, resource quality assurance efforts, and attend to building the cultural infrastructure with the input of all stakeholders.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10471","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836438","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}