Learning Health Systems最新文献

{"title":"System-failing creativity in health care","authors":"Stijn Horck,&nbsp;Rachel E. Gifford,&nbsp;Bram P. I. Fleuren,&nbsp;Cheryl Rathert,&nbsp;Tracy H. Porter,&nbsp;Afshan Rauf,&nbsp;Yuna S. H. Lee","doi":"10.1002/lrh2.10437","DOIUrl":"10.1002/lrh2.10437","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Health care professionals often generate novel solutions to solve problems during day-to-day patient care. However, less is known about generating novel and useful (i.e., creative) ideas in the face of health care system failure. System failures are high-impact and increasingly frequent events in health care organizations, and front-line professionals may have uniquely valuable expertise to address such occurrences.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Our interdisciplinary team, blending expertise in health care management, economics, psychology, and clinical practice, reviewed the literature on creativity and system failures in health care to generate a conceptual model that describes this process. Drawing on appraisal theory, we iteratively refined the model by integrating various theories with key concepts of system failures, creativity, and health care worker's well-being.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The SFC model provides a conceptualization of creativity from front-line care professionals as it emerges in situations of failure or crisis. It describes the pathways by which professionals respond proactively to a systems failure with creative ideas to effectively address the situation and affect these workers' well-being.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our conceptual model guides health care managers and leaders to use managerial practices to shape their systems and support creativity, especially when facing system failures. It introduces a framework for examining system-failing creativity (SFC) and general creativity, aiming to improve health care quality, health care workers' well-being, and organizational outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11733441/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013346","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 Complete Inpatient Record Using Comprehensive Electronic Data (CIRCE) project: A team-based approach to clinically validated, research-ready electronic health record data","authors":"Andrea L. C. Schneider,&nbsp;Jennifer C. Ginestra,&nbsp;Meeta Prasad Kerlin,&nbsp;Michael G. S. Shashaty,&nbsp;Todd A. Miano,&nbsp;Daniel S. Herman,&nbsp;Oscar J. L. Mitchell,&nbsp;Rachel Bennett,&nbsp;Alexander T. Moffett,&nbsp;John Chandler,&nbsp;Atul Kalanuria,&nbsp;Zahra Faraji,&nbsp;Nicholas S. Bishop,&nbsp;Benjamin Schmid,&nbsp;Angela T. Chen,&nbsp;Kathryn H. Bowles,&nbsp;Thomas Joseph,&nbsp;Rachel Kohn,&nbsp;Rachel R. Kelz,&nbsp;George L. Anesi,&nbsp;Monisha Kumar,&nbsp;Ari B. Friedman,&nbsp;Emily Vail,&nbsp;Nuala J. Meyer,&nbsp;Blanca E. Himes,&nbsp;Gary E. Weissman","doi":"10.1002/lrh2.10439","DOIUrl":"10.1002/lrh2.10439","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>The rapid adoption of electronic health record (EHR) systems has resulted in extensive archives of data relevant to clinical research, hospital operations, and the development of learning health systems. However, EHR data are not frequently available, cleaned, standardized, validated, and ready for use by stakeholders. We describe an in-progress effort to overcome these challenges with cooperative, systematic data extraction and validation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A multi-disciplinary team of investigators collaborated to create the Complete Inpatient Record Using Comprehensive Electronic Data (CIRCE) Project dataset, which captures EHR data from six hospitals within the University of Pennsylvania Health System. Analysts and clinical researchers jointly iteratively reviewed SQL queries and their output to validate desired data elements. Data from patients aged ≥18 years with at least one encounter at an acute care hospital or hospice occurring since 7/1/2017 were included. The CIRCE Project includes three layers: (1) raw data comprised of direct SQL query output, (2) cleaned data with errors removed, and (3) transformed data with standardized implementations of commonly used case definitions and clinical scores.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Between July 1, 2017 and December 31, 2023, the dataset captured 1 629 920 encounters from 740 035 patients. Most encounters were emergency department only visits (<i>n</i> = 965 834, 59.3%), followed by inpatient admissions without an intensive care unit admission (<i>n</i> = 518 367, 23.7%). The median age was 46.9 years (25th–75th percentiles = 31.1–64.7) at the time of the first encounter. Most patients were female (<i>n</i> = 418 303, 56.5%), a significant proportion were of non-White race (<i>n</i> = 272 018, 36.8%), and 54 625 (7.4%) were of Hispanic/Latino ethnicity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The CIRCE Project represents a novel cooperative research model to capture clinically validated EHR data from a large diverse academic health system in the greater Philadelphia region and is designed to facilitate collaboration and data sharing to support learning health system activities. Ultimately, these data will be de-identified and converted to a publicly available resource.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11733450/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013272","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":"Validating a novel measure for assessing patient openness and concerns about using artificial intelligence in healthcare","authors":"Bryan A. Sisk,&nbsp;Alison L. Antes,&nbsp;Sunny C. Lin,&nbsp;Paige Nong,&nbsp;James M. DuBois","doi":"10.1002/lrh2.10429","DOIUrl":"10.1002/lrh2.10429","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>Patient engagement is critical for the effective development and use of artificial intelligence (AI)-enabled tools in learning health systems (LHSs). We adapted a previously validated measure from pediatrics to assess adults' openness and concerns about the use of AI in their healthcare.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>Cross-sectional survey.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We adapted the 33-item “Attitudes toward Artificial Intelligence in Healthcare for Parents” measure for administration to adults in the general US population (AAIH-A), recruiting participants through Amazon's Mechanical Turk (MTurk) crowdsourcing platform. AAIH-A assesses openness to AI-driven technologies and includes 7 subscales assessing participants' openness and concerns about these technologies. The openness scale includes examples of AI-driven tools for diagnosis, prediction, treatment selection, and medical guidance. Concern subscales assessed privacy, social justice, quality, human element of care, cost, shared decision-making, and convenience. We co-administered previously validated measures hypothesized to correlate with openness. We conducted a confirmatory factor analysis and assessed reliability and construct validity. We performed exploratory multivariable regression models to identify predictors of openness.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>A total of 379 participants completed the survey. Confirmatory factor analysis confirmed the seven dimensions of the concerns, and the scales had internal consistency reliability, and correlated as hypothesized with existing measures of trust and faith in technology. Multivariable models indicated that trust in technology and concerns about quality and convenience were significantly associated with openness.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The AAIH-A is a brief measure that can be used to assess adults' perspectives about AI-driven technologies in healthcare and LHSs. The use of AAIH-A can inform future development and implementation of AI-enabled tools for patient care in the LHS context that engage patients as key stakeholders.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10429","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141349079","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":"Enhancing data practices for Whole Health: Strategies for a transformative future","authors":"Lei Guo,&nbsp;Kavitha P. Reddy,&nbsp;Theresa Van Iseghem,&nbsp;Whitney N. Pierce","doi":"10.1002/lrh2.10426","DOIUrl":"https://doi.org/10.1002/lrh2.10426","url":null,"abstract":"<p>We explored the challenges and solutions for managing data within the Whole Health System (WHS), which operates as a Learning Health System and a patient-centered healthcare approach that combines conventional and complementary approaches. Addressing these challenges is critical for enhancing patient care and improving outcomes within WHS. The proposed solutions include prioritizing interoperability for seamless data exchange, incorporating patient-centered comparative clinical effectiveness research and real-world data to personalize treatment plans and validate integrative approaches, and leveraging advanced data analytics tools to incorporate patient-reported outcomes, objective metrics, robust data platforms. Implementing these measures will enable WHS to fulfill its mission as a holistic and patient-centered healthcare model, promoting greater collaboration among providers, boosting the well-being of patients and providers, and improving patient outcomes.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 S1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10426","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326698","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 power of personas: Exploring an innovative model for understanding stakeholder perspectives in an oncology learning health network","authors":"Dylan J. Cooper,&nbsp;Jessie Karten,&nbsp;Sarah E. Hoffe,&nbsp;Daniel A. King,&nbsp;Matthew Weiss,&nbsp;Danielle K. DePeralta,&nbsp;Andrew L. Coveler,&nbsp;Sunil R. Hingorani,&nbsp;Tracey Shefter,&nbsp;Cheryl Meguid,&nbsp;Hannah Roberts,&nbsp;Theodore S. Hong,&nbsp;Amol Narang,&nbsp;Amy Hacker-Prietz,&nbsp;George A. Fisher,&nbsp;Jay Sandler,&nbsp;Laurie Singer,&nbsp;Bobby Korah,&nbsp;William Hoos,&nbsp;Carrie T. Stricker,&nbsp;Joseph M. Herman","doi":"10.1002/lrh2.10422","DOIUrl":"10.1002/lrh2.10422","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Learning health networks (LHNs) improve clinical outcomes by applying core tenets of continuous quality improvements (QI) to reach community-defined outcomes, data-sharing, and empowered interdisciplinary teams including patients and caregivers. LHNs provide an ideal environment for the rapid adoption of evidence-based guidelines and translation of research and best practices at scale. When an LHN is established, it is critical to understand the needs of all stakeholders. To accomplish this, we used ethnographic methods to develop personas of different stakeholders within The Canopy Cancer Collective, the first oncology LHN.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We partnered with a firm experienced in qualitative research and human-centered design to conduct interviews with stakeholders of The Canopy Cancer Collective, a newly developed pancreatic cancer LHN. Together with the firm, we developed a personas model approach to represent the wide range of diverse perspectives among the representative stakeholders, which included care team members, patients, and caregivers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Thirty-one stakeholders from all facets of pancreatic cancer care were interviewed, including 20 care team members, 8 patients, and 3 caregivers. Interview transcripts were analyzed to construct 10 personas felt to represent the broad spectrum of stakeholders within The Cancer Canopy Collective. These personas were used as a foundation for the design and development of The Cancer Canopy Cancer Collective key drivers and aims.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>As LHNs continue to facilitate comprehensive approaches to patient-centered care, interdisciplinary teams who understand each other's needs can improve Network unity and cohesion. We present the first model utilizing personas for LHNs, demonstrating this framework holds significant promise for further study. If validated, such an approach could be used as a dynamic foundation for understanding individual stakeholder needs in similar LHN ecosystems in the future.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11733431/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013415","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 Right Stuff: Getting the right data at the right time and using that data to drive evidence-based practice and policy","authors":"Lucy A. Savitz,&nbsp;Sarah M. Greene,&nbsp;Michael K. Gould,&nbsp;Harold S. Luft","doi":"10.1002/lrh2.10432","DOIUrl":"https://doi.org/10.1002/lrh2.10432","url":null,"abstract":"<p>When researchers are embedded within healthcare systems and collaborate with practitioners and operational leaders, they may be able to rapidly identify problems and opportunities that can be addressed to improve quality and affordability. While other industries have well-developed data exploration processes (e.g., banking), healthcare is still developing its methods with widely varying data sources, huge quantities of unstructured data, uncertain precision in measurement, uncertainties about data quality, and complicated and stringent regulations and policies on data access. In recognition of these challenges, the AcademyHealth Learning Health System (LHS) Interest Group (In 2021, <i>Learning Health Systems</i> journal established a formal relationship with AcademyHealth, serving as the official journal of its LHS Interest Group.) released a call for papers in June 2023 to focus on challenges encountered by investigators related to the use of real-world data in embedded research.</p><p>We use the term “embedded researcher” to characterize a broad range of people well-trained in research methods using real-world data. Being located inside a health system, they often have privileged access to data and the practitioners who may be observing new situations, problems, or opportunities for improvement. Unlike colleagues only involved in internal quality improvement efforts, embedded researchers also seek to broadly share their findings and create generalizable knowledge. The sharing is less focused on the specific findings—too many things may be unique about the setting, people, and other factors to be directly generalizable. The challenges faced and techniques used to overcome them, however, may offer important lessons for other embedded researchers.</p><p>As LHSs mature and internally tackle increasingly complex problems with embedded research, the challenges presented in using real-world data for locally applied health services research are important to understand. Taken together, the papers in this Special Issue offer insights into the frontiers of embedded research as LHSs embark on their own learning journey. Accelerating the transformation of data to knowledge requires an understanding of the underlying data and techniques needed to draw useful lessons from the data. Sharing experiences across teams and settings will help others in anticipating and addressing the challenges they are likely to encounter.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 S1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10432","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326796","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 a participatory approach to identify priorities to advance LHS implementation at an academic medical center","authors":"Reid M. Eagleson,&nbsp;Madeline Gibson,&nbsp;Carletta Dobbins,&nbsp;Frederick Van Pelt,&nbsp;Allyson Hall,&nbsp;Larry Hearld,&nbsp;Andrea L. Cherrington,&nbsp;Jacob McMahon,&nbsp;Keith Tony Jones,&nbsp;Michael J. Mugavero","doi":"10.1002/lrh2.10431","DOIUrl":"10.1002/lrh2.10431","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Like many other academic medical centers, the University of Alabama at Birmingham (UAB) aspires to adopt learning health system (LHS) principles and practices more fully. Applying LHS principles establishes a culture where clinical and operational practices constantly generate questions and leverage information technology (IT) and methodological expertise to facilitate systematic evaluation of care delivery, health outcomes, and the effects of improvement initiatives. Despite the potential benefits, differences in priorities, timelines, and expectations spanning an academic medical center's clinical care, administrative operations, and research arms create barriers to adopting and implementing an LHS.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>UAB's Center for Outcomes and Effectiveness Research and Education, in partnership with UAB Medicine's Department of Clinical Practice Transformation, applied part of the Precision Problem Solving methodology to advance the implementation of LHS principles at UAB.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Sixty-two stakeholders across the UAB health system and academic schools noted 131 concerns regarding the development of an LHS at UAB. From the 131 items, eight major themes were identified, named, and prioritized through a series of focus groups. Of the eight major themes, “Creating a Structure for Aligned and Informed Prioritization” and “Right Data, Right Time, Improved Performance” ranked in the top three most important themes across all focus groups and became the critical priorities as UAB enhances its LHS. A task force comprised of diverse constituents from across UAB's academic medical center is taking first steps toward addressing these priority areas. Initial funding supports a prototype for enhanced health system data access and pilot projects conducted by researchers embedded in health system teams.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>We suggest that our experience conducting a deliberate process with broad engagement across both the health system and academic arms of the university may be informative to others seeking to advance LHS principles at academic health centers across a myriad of settings.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11733440/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013614","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":"Patient care in complex Sociotechnological ecosystems and learning health systems","authors":"Shin-Ping Tu,&nbsp;Brittany Garcia,&nbsp;Xi Zhu,&nbsp;Daniel Sewell,&nbsp;Vimal Mishra,&nbsp;Khalid Matin,&nbsp;Alan Dow","doi":"10.1002/lrh2.10427","DOIUrl":"10.1002/lrh2.10427","url":null,"abstract":"<p>The learning health system (LHS) model was proposed to provide real-time, bi-directional flow of learning using data captured in health information technology systems to deliver rapid learning in healthcare delivery. As highlighted by the landmark National Academy of Medicine report “Crossing the Quality Chasm,” the U.S. healthcare delivery industry represents complex adaptive systems, and there is an urgent need to develop innovative methods to identify efficient team structures by harnessing real-world care delivery data found in the electronic health record (EHR). We offer a discussion surrounding the complexities of team communication and how solutions may be guided by theories such as the Multiteam System (MTS) framework and the Multitheoretical Multilevel Framework of Communication Networks. To advance healthcare delivery science and promote LHSs, our team has been building a new line of research using EHR data to study MTS in the complex real world of cancer care delivery. We are developing new network metrics to study MTSs and will be analyzing the impact of EHR communication network structures on patient outcomes. As this research leads to patient care delivery interventions/tools, healthcare leaders and healthcare professionals can effectively use health IT data to implement the most evidence-based collaboration approaches in order to achieve the optimal LHS and patient outcomes.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 S1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10427","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141103173","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":"Overcoming challenges in real-world evidence generation: An example from an Adult Medical Care Coordination program","authors":"Samuel T. Savitz,&nbsp;Michelle A. Lampman,&nbsp;Shealeigh A. Inselman,&nbsp;Ruchita Dholakia,&nbsp;Vicki L. Hunt,&nbsp;Angela B. Mattson,&nbsp;Robert J. Stroebel,&nbsp;Pamela J. McCabe,&nbsp;Stephanie G. Witwer,&nbsp;Bijan J. Borah","doi":"10.1002/lrh2.10430","DOIUrl":"10.1002/lrh2.10430","url":null,"abstract":"<p>The Adult Medical Care Coordination program (“the program”) was implemented at Mayo Clinic to promote patient self-management and improve 30-day unplanned readmission for patients with high risk for readmission after hospital discharge. This study aimed to evaluate the impact of the program compared to usual care using a pragmatic, stepped wedge cluster randomized trial (“stepped wedge trial”). However, several challenges arose including large differences between the study arms. Our goal is to describe the challenges and present lessons learned on how to overcome such challenges and generate evidence to support practice decisions. We describe the challenges encountered during the trial, the approach to addressing these challenges, and lessons learned for other learning health system researchers facing similar challenges. The trial experienced several challenges in implementation including several clinics dropping from the study and care disruptions due to COVID-19. Additionally, there were large differences in the patient population between the program and usual care arms. For example, the mean age was 76.8 for the program and 68.1 for usual care. Due to these differences, we adapted the methods using the propensity score matching approach that is traditionally applied to observational designs and adjusted for differences in observable characteristics. When conducting pragmatic research, researchers will encounter factors beyond their control that may introduce bias. The lessons learned include the need to weigh the tradeoffs of pragmatic design elements and the potential value of adaptive designs for pragmatic trials. Applying these lessons would promote the successful generation of evidence that informs practice decisions.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 S1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10430","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141111069","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 bridge between worlds: Embedding research in telepractice co-design with disability community","authors":"Cloe Benz","doi":"10.1002/lrh2.10428","DOIUrl":"10.1002/lrh2.10428","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Co-production approaches are increasingly being advocated for as a way of addressing the research translatory gap while including patient and public involvement in development of services they access, and particularly in disability service provision. Embedded research (ER) is a method which integrates the researcher within the target organization to better facilitate both co-production of research outputs and the reduction of the research translation gap. The aim of this reflection is to better understand the commonalities and differences between ER in a disability context to accounts published in academic literature.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>A review of embedded researcher literature was completed in combination with a personal reflection of lived experience as an embedded researcher within a disability support service organization. The reflective process included review of research journal entries and other records of lived experience (photographs, audio recordings, drawings) maintained throughout the period in the role of embedded researcher. A reflexive thematic analysis process was used.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>I reflect throughout the article upon five themes which highlight both the commonalities between my experiences and those of other embedded researchers as well as instances where they differed. The five themes include (1) A knowledge bridge, (2) Considerations of positionality, (3) Ethical complexity, (4) Anticipating change, and (5) Existing in the in-between together.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Experiences of ER appear to transcend the discipline in which the research is being embedded, and while the lived experience in a disability host organization was invaluable in facilitating a successful co-produced research project, significant avenues for improvement exist in terms of ethical frameworks, methodological guidance, and communities of support.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10428","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140993618","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}