Learning Health Systems最新文献

{"title":"Care partners and consumer health information technology: A framework to guide systems-level initiatives in support of digital health equity","authors":"Jennifer L. Wolff,&nbsp;Aleksandra Wec,&nbsp;Danielle Peereboom,&nbsp;Kelly T. Gleason,&nbsp;Halima Amjad,&nbsp;Julia G. Burgdorf,&nbsp;Jessica Cassidy,&nbsp;Catherine M. DesRoches,&nbsp;Chanee D. Fabius,&nbsp;Ariel R. Green,&nbsp;C. T. Lin,&nbsp;Stephanie K. Nothelle,&nbsp;Danielle S. Powell,&nbsp;Catherine A. Riffin,&nbsp;Jamie Smith,&nbsp;Hillary D. Lum","doi":"10.1002/lrh2.10408","DOIUrl":"10.1002/lrh2.10408","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Consumer-oriented health information technologies (CHIT) such as the patient portal have a growing role in care delivery redesign initiatives such as the Learning Health System. Care partners commonly navigate CHIT demands alongside persons with complex health and social needs, but their role is not well specified.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We assemble evidence and concepts from the literature describing interpersonal communication, relational coordination theory, and systems-thinking to develop an integrative framework describing the care partner's role in applied CHIT innovations. Our framework describes pathways through which systematic engagement of the care partner affects longitudinal work processes and multi-level outcomes relevant to Learning Health Systems.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our framework is grounded in relational coordination, an emerging theory for understanding the dynamics of coordinating work that emphasizes role-based relationships and communication, and the Systems Engineering Initiative for Patient Safety (SEIPS) model. Cross-cutting work systems geared toward explicit and purposeful support of the care partner role through CHIT may advance work processes by promoting frequent, timely, accurate, problem-solving communication, reinforced by shared goals, shared knowledge, and mutual respect between patients, care partners, and care team. We further contend that systematic engagement of the care partner in longitudinal work processes exerts beneficial effects on care delivery experiences and efficiencies at both individual and organizational levels. We discuss the utility of our framework through the lens of an illustrative case study involving patient portal-mediated pre-visit agenda setting.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our framework can be used to guide applied embedded CHIT interventions that support the care partner role and bring value to Learning Health Systems through advancing digital health equity, improving user experiences, and driving efficiencies through improved coordination within complex work systems.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 S1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10408","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140427759","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":"Identifying virtual care modality in electronic health record data","authors":"Annie E. Larson,&nbsp;Kurt C. Stange,&nbsp;John Heintzman,&nbsp;Yui Nishiike,&nbsp;Brenda M. McGrath,&nbsp;Melinda M. Davis,&nbsp;S. Marie Harvey","doi":"10.1002/lrh2.10411","DOIUrl":"10.1002/lrh2.10411","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Virtual care increased dramatically during the COVID-19 pandemic. The specific modality of virtual care (video, audio, eVisits, eConsults, and remote patient monitoring) has important implications for the accessibility and quality of care, but rates of use are relatively unknown. Methods for identifying virtual care modalities, especially in electronic health records (EHR) are inconsistent. This study (a) developed a method to identify virtual care modalities using EHR data and (b) described the distribution of these modalities over a 3-year study period.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>EHR data from 316 primary care safety net clinics throughout the study period (4/1/2020-3/31/2023) were included. Visit type (in-person vs virtual) by adults &gt;18 years old were classified. Expert consultation informed the development of two algorithms to classify virtual care visit modalities; these algorithms prioritized different EHR data elements. We conducted descriptive analyses comparing algorithms and the frequency of virtual care modalities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Agreement between the algorithms was 96.5% for all visits and 89.3% for virtual care visits. The majority of disagreement between the algorithms was among encounters scheduled as audio-only but billed as a video visit. Restricting to visits where the algorithms agreed on visit modality, there were 2-fold more audio-only than video visits.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Visit modality classification varies depending upon which data in the EHR are prioritized. Regardless of which algorithm is utilized, safety net clinics rely on audio-only and video visits to provide care in virtual visits. Elimination of reimbursement for audio visits may exacerbate existing inequities in care for low-income patients.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 S1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10411","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140429824","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":"Breast cancer learning health system: Patient information from a data and analytics platform characterizes care provided","authors":"Mark N. Levine,&nbsp;Joel Kemppainen,&nbsp;Morgan Rosenberg,&nbsp;Christopher Pettengell,&nbsp;Jessica Bogach,&nbsp;Tim Whelan,&nbsp;Ashirbani Saha,&nbsp;Jonathan Ranisau,&nbsp;Jeremy Petch","doi":"10.1002/lrh2.10409","DOIUrl":"10.1002/lrh2.10409","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>In a learning health system (LHS), data gathered from clinical practice informs care and scientific investigation. To demonstrate how a novel data and analytics platform can enable an LHS at a regional cancer center by characterizing the care provided to breast cancer patients.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Socioeconomic information, tumor characteristics, treatments and outcomes were extracted from the platform and combined to characterize the patient population and their clinical course. Oncologists were asked to identify examples where clinical practice guidelines (CPGs) or policy changes had varying impacts on practice. These constructs were evaluated by extracting the corresponding data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Breast cancer patients (5768) seen at the Juravinski Cancer Centre between January 2014 and June 2022 were included. The average age was 62.5 years. The commonest histology was invasive ductal carcinoma (74.6%); 77% were estrogen receptor-positive and 15.5% were HER2 Neu positive. Breast-conserving surgery (BCS) occurred in 56%. For the 4294 patients who received systemic therapy, the initial indications were adjuvant (3096), neoadjuvant (828) and palliative (370). Metastases occurred in 531 patients and 495 patients died. Lowest-income patients had a higher mortality rate. For the adoption of CPGs, the uptake for adjuvant bisphosphonate was very low, 8% as predicted, compared to 64% for pertuzumab, a HER2 targeted agent and 40.2% for CD4/6 inhibitors in metastases. During COVID-19, the provincial cancer agency issued a policy to shorten the duration of radiation after BCS. There was a significant reduction in the average number of fractions to the breast by five fractions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Our platform characterized care and the clinical course of breast cancer patients. Practice changes in response to regulatory developments and policy changes were measured. Establishing a data platform is important for an LHS. The next step is for the data to feedback and change practice, that is, close the loop.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10409","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139840758","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 regional learning health system of congregate care facilities for COVID-19 response","authors":"Muhammad A. Zafar,&nbsp;Andrew F. Beck,&nbsp;Chris Chirumbolo,&nbsp;Ken D. Wilson,&nbsp;Kate Haralson,&nbsp;Katherine Rich,&nbsp;Peter Margolis,&nbsp;David Hartley","doi":"10.1002/lrh2.10407","DOIUrl":"10.1002/lrh2.10407","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>The COVID-19 pandemic disproportionately affected congregate care (CC) facilities due to communal living, presence of vulnerable populations, inadequate preventive resources, and limited ability to respond to the pandemic's rapidly evolving phases. Most facilities function independently and are not organized for collaborative learning and operations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We formed a learning health system of CC facilities in our 14-county metropolitan region, coordinated with public health and health care sectors, to address challenges driven by COVID-19. A CC steering committee (SC) was formed that represented diverse institutions and viewpoints, including skilled nursing facilities, transitional care facilities, residential facilities, prisons, and shelters. The SC met regularly and was guided by situational awareness and systems thinking. A regional CC COVID-19 dashboard was developed based on publicly available data and weekly data submitted by participating facilities. Those experiencing outbreaks or supply shortages were quickly identified. As the pandemic progressed, the role of the SC shifted to address new and forecasted needs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Over 60 facilities participated in data sharing. The SC shared new guidelines, regulations, educational material, and best practices with the participating facilities. Information about testing sites, supplies, vaccination rollout, and facilities that had the capacity to accept COVID-19 patients was regularly disseminated. The SC was able to direct resources to those facilities experiencing outbreaks or supply shortages.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>A novel learning health system of regional CC facilities enabled preparedness, situational awareness, collaboration, and rapid dissemination of best practices across pandemic phases. Such collaborative efforts can play an important role in addressing other public and preventive health challenges.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10407","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139602241","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":"Socio-technical infrastructure for a learning health system","authors":"Charles P. Friedman,&nbsp;Edwin A. Lomotan,&nbsp;Joshua E. Richardson,&nbsp;Jennifer L. Ridgeway","doi":"10.1002/lrh2.10405","DOIUrl":"https://doi.org/10.1002/lrh2.10405","url":null,"abstract":"<p>This commentary is in many ways a follow-on to, and elaboration of, the commentary published in the July issue of this journal.<span>¹</span> The previous commentary introduced three characteristics that contribute to the uniqueness of learning health systems (LHSs) as an approach to health improvement. The three characteristics introduced there were: “(1) a multi-stakeholder learning community that is focused on the (targeted) problem and collaboratively executes the entire cycle; (2) embracing, at the outset, the uncertainty of how to improve against the problem by undertaking a rigorous discovery process before any implementation takes place; and (3) supporting multiple co-occurring cycles with a socio-technical infrastructure to create a learning system.”</p><p>This commentary focuses on the very important third characteristic, infrastructure. It examines the role of infrastructure in the overall architecture of an LHS and describes LHS infrastructure in terms of 10 interconnected socio-technical services accompanied by a brief description of each. Like the previous commentary, this one seeks to bring an increased level of focus to discussions of LHSs and move an emerging field, what is coming to be called “Learning Health System Science”,<span>²</span> toward a sharper conception of its core principles.</p><p>Critically, LHS infrastructure must extend beyond digital technology in order to support improvement of individual and population health. The infrastructure must be socio-technical in the sense that it incorporates the roles that a wide range of people must play at different levels of social organization: as individuals, as teams, as members of organizations, and as citizens of civil society.<span>⁵</span> Technology, alone, only establishes a potential for health improvement through an LHS.</p><p>Viewing its infrastructure in terms of socio-technical services could be beneficial in several ways beyond working toward a consensus view of LHS structure and function. Most notably, such a modular approach could lead to sharing of interoperable infrastructure components and the possibility that sharing of such components might promote the more rapid adoption of LHS methods. Moreover, compatibility of LHS architectures could enable smaller scale LHSs to compose into a single system that functions at larger scale. Logical next steps to mature LHS infrastructure would include building consensus around the constituent services and developing specifications for each one.</p><p>The authors have no conflicts of interest to declare.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10405","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139494393","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":"Privacy-preserving record linkage across disparate institutions and datasets to enable a learning health system: The national COVID cohort collaborative (N3C) experience","authors":"Umberto Tachinardi,&nbsp;Shaun J. Grannis,&nbsp;Sam G. Michael,&nbsp;Leonie Misquitta,&nbsp;Jayme Dahlin,&nbsp;Usman Sheikh,&nbsp;Abel Kho,&nbsp;Jasmin Phua,&nbsp;Sara S. Rogovin,&nbsp;Benjamin Amor,&nbsp;Maya Choudhury,&nbsp;Philip Sparks,&nbsp;Amin Mannaa,&nbsp;Saad Ljazouli,&nbsp;Joel Saltz,&nbsp;Fred Prior,&nbsp;Ahmen Baghal,&nbsp;Kenneth Gersing,&nbsp;Peter J. Embi","doi":"10.1002/lrh2.10404","DOIUrl":"https://doi.org/10.1002/lrh2.10404","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Research driven by real-world clinical data is increasingly vital to enabling learning health systems, but integrating such data from across disparate health systems is challenging. As part of the NCATS National COVID Cohort Collaborative (N3C), the N3C Data Enclave was established as a centralized repository of deidentified and harmonized COVID-19 patient data from institutions across the US. However, making this data most useful for research requires linking it with information such as mortality data, images, and viral variants. The objective of this project was to establish privacy-preserving record linkage (PPRL) methods to ensure that patient-level EHR data remains secure and private when governance-approved linkages with other datasets occur.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Separate agreements and approval processes govern N3C data contribution and data access. The Linkage Honest Broker (LHB), an independent neutral party (the Regenstrief Institute), ensures data linkages are robust and secure by adding an extra layer of separation between protected health information and clinical data. The LHB's PPRL methods (including algorithms, processes, and governance) match patient records using “deidentified tokens,” which are hashed combinations of identifier fields that define a match across data repositories without using patients' clear-text identifiers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>These methods enable three linkage functions: Deduplication, Linking Multiple Datasets, and Cohort Discovery. To date, two external repositories have been cross-linked. As of March 1, 2023, 43 sites have signed the LHB Agreement; 35 sites have sent tokens generated for 9 528 998 patients. In this initial cohort, the LHB identified 135 037 matches and 68 596 duplicates.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This large-scale linkage study using deidentified datasets of varying characteristics established secure methods for protecting the privacy of N3C patient data when linked for research purposes. This technology has potential for use with registries for other diseases and conditions.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10404","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139494384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the use of ChatGPT as a virtual health coach for chronic disease management","authors":"Fahad M. Al-Anezi","doi":"10.1002/lrh2.10406","DOIUrl":"10.1002/lrh2.10406","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>ChatGPT has been widely researched for its potential in gealthcare applications. However, its efficcy as a virtual health coach is one of the important areas, which can significantly contribute to the sustainablility in healthcare operations, especially in managing critical illnesses. Therefore, this study aims to analyze the use of ChatGPT as a virtual health coach for chronic disease managemet.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This study used a quasi-experimental design because ChatGPT is a relatively new technology and few people have experience with it. Patients who were receiving care outside of the hospital were included. Semi-structured interviews were conducted after a 2-week period in which participants used ChatGPT to search for health information about chronic disease management. Thirty-nine outpatients were interviewed and thematic analysis was used to analyze the interview data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The findings suggested both opportunities and challenges of using ChatGPT as a virtual health coach for chronic disease management. The major opportunities identified included life-long learning, improved health literacy, cost-effectiveness, behavioral change support, scalability, and accessibility. The major challenges identified included limited physical examination, lack of human connection, legal and ethical complications, and lack of accuracy and reliability.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>ChatGPT-based technologies may serve as a supplementary or intermediate support system. However, such applications for managing chronic diseases must protect privacy and promote both short- and long-term positive outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10406","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139625872","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":"Using the consolidated framework for implementation research (CFIR) to guide implementation of cardio-oncology services","authors":"Jessica Miller Clouser,&nbsp;Colleen A. McMullen,&nbsp;Akosua K. Adu,&nbsp;Gretchen Wells,&nbsp;Amit Arbune,&nbsp;Jing Li","doi":"10.1002/lrh2.10402","DOIUrl":"10.1002/lrh2.10402","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Cardio-oncology focuses on diagnosing and preventing adverse cardiovascular outcomes in cancer patients. Interdisciplinary cardio-oncology services address the spectrum of prevention, detection, monitoring, and treatment of cancer patients at risk of cardio-toxicity and aim to improve the continuum of cardiac care for oncology patients. The goal of this study was to engage clinician and administrative stakeholders to assess multilevel needs, barriers, and expectations regarding cardio oncology services.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We interviewed clinicians and administrators at an academic medical center using the Consolidated Framework for Implementation Research (CFIR) to understand multilevel determinants influencing cardio-oncology service implementation. We also conducted a web-based survey to assess the knowledge, attitude, and perceptions of cardio-oncology services held by local and regional clinicians who may refer cardio-oncology patients to the study site.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Multiple facilitators to cardio-oncology service implementation emerged. Interview participants believed cardio-oncology services could benefit patients and the organization by providing a competitive advantage. A majority (74%) of clinicians surveyed thought a cardio-oncology service would significantly improve cancer patients’ prognoses. Implementation barriers discussed included costs and a siloed organizational structure that complicated cross-service collaboration. In the clinician survey, differences in the views toward cardio-oncology services held by cardiology versus oncology providers would need to be negotiated in future cardio-oncology service development. For example, while most providers accepted similar risk of cardio-toxicity when consenting patients for cancer therapy in a curative setting, cardiologists accepted significantly higher levels of risk than oncologists in an incurable setting: 75% of oncologists accepted 1-5% risk; 77% of cardiologists accepted ≥5% risk).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Participants supported implementation and development of cardio-oncology services. Respondents also noted multi-level barriers that could be addressed to maximize the potential for success. Engaging administrators and clinicians from cardiology and oncology disciplines in the future development of such services can help ensure maximal relevance and uptake.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10402","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139005833","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":"Building a regional pediatric asthma learning health system in support of optimal, equitable outcomes","authors":"Andrew F. Beck,&nbsp;Michael Seid,&nbsp;Karen M. McDowell,&nbsp;Mfonobong Udoko,&nbsp;Susan C. Cronin,&nbsp;Dimitrios Makrozahopoulos,&nbsp;Tricia Powers,&nbsp;Sonja Fairbanks,&nbsp;Jonelle Prideaux,&nbsp;Lisa M. Vaughn,&nbsp;Elizabeth Hente,&nbsp;Sophia Thurmond,&nbsp;Ndidi I. Unaka","doi":"10.1002/lrh2.10403","DOIUrl":"10.1002/lrh2.10403","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Asthma is characterized by preventable morbidity, cost, and inequity. We sought to build an Asthma Learning Health System (ALHS) to coordinate regional pediatric asthma improvement activities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We generated quantitative and qualitative insights pertinent to a better, more equitable care delivery system. We used electronic health record data to calculate asthma hospitalization rates for youth in our region. We completed an “environmental scan” to catalog the breadth of asthma-related efforts occurring in our children's hospital and across the region. We supplemented the scan with group-level assessments and focus groups with parents, clinicians, and community partners. We used insights from this descriptive epidemiology to inform the definition of shared aims, drivers, measures, and prototype interventions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Greater Cincinnati's youth are hospitalized for asthma at a rate three times greater than the U.S. average. Black youth are hospitalized at a rate five times greater than non-Black youth. Certain neighborhoods bear the disproportionate burden of asthma morbidity. Across Cincinnati, there are many asthma-relevant activities that seek to confront this morbidity; however, efforts are largely disconnected. Qualitative insights highlighted the importance of cross-sector coordination, evidence-based acute and preventive care, healthy homes and neighborhoods, and accountability. These insights also led to a shared, regional aim: to equitably reduce asthma-related hospitalizations. Early interventions have included population-level pattern recognition, multidisciplinary asthma action huddles, and enhanced social needs screening and response.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Learning health system methods are uniquely suited to asthma's complexity. Our nascent ALHS provides a scaffold atop which we can pursue better, more equitable regional asthma outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10403","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138980917","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 prototype of a frailty learning health system: The HARMONY Model","authors":"Kirsten J. Parker,&nbsp;Louise D. Hickman,&nbsp;Julee McDonagh,&nbsp;Richard I. Lindley,&nbsp;Caleb Ferguson","doi":"10.1002/lrh2.10401","DOIUrl":"10.1002/lrh2.10401","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Rapid translation of research findings into clinical practice through innovation is critical to improve health systems and patient outcomes. Access to efficient systems of learning underpinned with real-time data are the future of healthcare. This type of health system will decrease unwarranted clinical variation, accelerate rapid evidence translation, and improve overall healthcare quality.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This paper aims to describe The HARMONY model (acHieving dAta-dRiven quality iMprovement to enhance frailty Outcomes using a learNing health sYstem), a new frailty learning health system model of implementation science and practice improvement. The HARMONY model provides a prototype for clinical quality registry infrastructure and partnership within health care.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The HARMONY model was applied to the Western Sydney Clinical Frailty Registry as the prototype exemplar. The model networks longitudinal frailty data into an accessible and useable format for learning. Creating local capability that networks current data infrastructures to translate and improve quality of care in real-time.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This prototype provides a model of registry data feedback and quality improvement processes in an inpatient aged care and rehabilitation hospital setting to help reduce clinical variation, enhance research translation capacity, and improve care quality.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10401","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139242712","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}