JAMIA Open最新文献

{"title":"Implementation of an EHR-integrated web-based depression assessment in primary care: PORTAL-Depression.","authors":"Melissa I Franco, Erin M Staab, Mengqi Zhu, William Deehan, John Moses, Robert Gibbons, Lisa Vinci, Sachin Shah, Daniel Yohanna, Nancy Beckman, Neda Laiteerapong","doi":"10.1093/jamiaopen/ooae094","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooae094","url":null,"abstract":"<p><strong>Objectives: </strong>To integrate a computerized adaptive test for depression into the electronic health record (EHR) and establish systems for administering assessments in-clinic and via a patient portal to improve depression care.</p><p><strong>Materials and methods: </strong>This article reports the adoption, implementation, and maintenance of a health information technology (IT) quality improvement (QI) project, Patient Outcomes Reporting for Timely Assessment of Life with Depression (PORTAL-Depression). The project was conducted in a hospital-based primary care clinic that serves a medically underserved metropolitan community. A 30-month (July 2017-March 2021) QI project was designed to create an EHR-embedded system to administer adaptive depression assessments in-clinic and via a patient portal. A multi-disciplinary team integrated 5 major health IT innovations into the EHR: (1) use of a computerized adaptive test for depression assessment, (2) 2-way secure communication between cloud-based software and the EHR, (3) improved accessibility of depression assessment results, (4) enhanced awareness and documentation of positive depression results, and (5) sending assessments via the portal. Throughout the 30-month observational period, we collected administrative, survey, and outcome data.</p><p><strong>Results: </strong>Attending and resident physicians who participated in the project were trained in depression assessment workflows through presentations at clinic meetings, self-guided online materials, and individual support. Developing stakeholder relationships, using an evaluative and iterative process, and ongoing training were key implementation strategies.</p><p><strong>Conclusions: </strong>The PORTAL-Depression project was a complex and labor-intensive intervention. Despite quick adoption by the clinic, only certain aspects of the intervention were sustained in the long term due to financial and personnel constraints.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 3","pages":"ooae094"},"PeriodicalIF":2.5,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11519040/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142548077","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 impact of laboratory data missingness on sepsis diagnosis timeliness.","authors":"Jonathan Y Lam, Aaron Boussina, Supreeth P Shashikumar, Robert L Owens, Shamim Nemati, Christopher S Josef","doi":"10.1093/jamiaopen/ooae085","DOIUrl":"10.1093/jamiaopen/ooae085","url":null,"abstract":"<p><strong>Objective: </strong>To investigate the impact of missing laboratory measurements on sepsis diagnostic delays.</p><p><strong>Materials and methods: </strong>In adult patients admitted to 2 University of California San Diego (UCSD) hospitals from January 1, 2021 to June 30, 2024, we evaluated the relative time of organ failure (<i>T</i> _OF) and time of clinical suspicion of sepsis (<i>T</i> _suspicion) in patients with sepsis according to the Centers for Medicare & Medicaid Services (CMS) definition.</p><p><strong>Results: </strong>Of the patients studied, 48.7% (<i>n</i> = 2017) in the emergency department (ED), 30.8% (<i>n</i> = 209) in the wards, and 14.4% (<i>n</i> = 167) in the intensive care unit (ICU) had <i>T</i> _OF after <i>T</i> _suspicion. Patients with <i>T</i> _OF after <i>T</i> _suspicion had significantly higher data missingness of 1 or more of the 5 laboratory components used to determine organ failure. The mean number of missing labs was 4.23 vs 2.83 in the ED, 4.04 vs 3.38 in the wards, and 3.98 vs 3.19 in the ICU.</p><p><strong>Discussion: </strong>Our study identified many sepsis patients with missing laboratory results vital for the identification of organ failure and the diagnosis of sepsis at or before the time of clinical suspicion of sepsis. Addressing data missingness via more timely laboratory assessment could precipitate an earlier recognition of organ failure and potentially earlier diagnosis of and treatment initiation for sepsis.</p><p><strong>Conclusions: </strong>More prompt laboratory assessment might improve the timeliness of sepsis recognition and treatment.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 3","pages":"ooae085"},"PeriodicalIF":2.5,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418648/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142308693","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 electronic health record to provide audit and feedback in medical student clerkships.","authors":"Jacqueline Xu, Matthew A Silver, Jung Kim, Lindsay Mazotti","doi":"10.1093/jamiaopen/ooae090","DOIUrl":"10.1093/jamiaopen/ooae090","url":null,"abstract":"<p><strong>Objectives: </strong>This article focuses on the role of the electronic health record (EHR) to generate meaningful formative feedback for medical students in the clinical setting. Despite the scores of clinical data housed within the EHR, medical educators have only just begun to tap into this data to enhance student learning. Literature to-date has focused almost exclusively on resident education.</p><p><strong>Materials and methods: </strong>Development of EHR auto-logging and triggered notifications are discussed as specific use cases in providing enhanced feedback for medical students.</p><p><strong>Results: </strong>By incorporating predictive and prescriptive analytics into the EHR, there is an opportunity to create powerful educational tools which may also support general clinical activity.</p><p><strong>Discussion: </strong>This article explores the possibilities of EHR as an educational resource. This serves as a call to action for educators and technology developers to work together on creating health record user-centric tools, acknowledging the ongoing work done to improve student-level attribution to patients.</p><p><strong>Conclusion: </strong>EHR analytics and tools present a novel approach to enhancing clinical clerkship education for medical students.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 3","pages":"ooae090"},"PeriodicalIF":2.5,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418647/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142309719","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 validation of a deep learning algorithm for the prediction of serum creatinine in critically ill patients.","authors":"Ghodsieh Ghanbari, Jonathan Y Lam, Supreeth P Shashikumar, Linda Awdishu, Karandeep Singh, Atul Malhotra, Shamim Nemati, Zaid Yousif","doi":"10.1093/jamiaopen/ooae097","DOIUrl":"10.1093/jamiaopen/ooae097","url":null,"abstract":"<p><strong>Objectives: </strong>Serum creatinine (SCr) is the primary biomarker for assessing kidney function; however, it may lag behind true kidney function, especially in instances of acute kidney injury (AKI). The objective of the work is to develop Nephrocast, a deep-learning model to predict next-day SCr in adult patients treated in the intensive care unit (ICU).</p><p><strong>Materials and methods: </strong>Nephrocast was trained and validated, temporally and prospectively, using electronic health record data of adult patients admitted to the ICU in the University of California San Diego Health (UCSDH) between January 1, 2016 and June 22, 2024. The model features consisted of demographics, comorbidities, vital signs and laboratory measurements, and medications. Model performance was evaluated by mean absolute error (MAE) and root-mean-square error (RMSE) and compared against the prediction day's SCr as a reference.</p><p><strong>Results: </strong>A total of 28 191 encounters met the eligibility criteria, corresponding to 105 718 patient-days. The median (interquartile range [IQR]) MAE and RMSE in the internal test set were 0.09 (0.085-0.09) mg/dL and 0.15 (0.146-0.152) mg/dL, respectively. In the prospective validation, the MAE and RMSE were 0.09 mg/dL and 0.14 mg/dL, respectively. The model's performance was superior to the reference SCr.</p><p><strong>Discussion and conclusion: </strong>Our model demonstrated good performance in predicting next-day SCr by leveraging clinical data routinely collected in the ICU. The model could aid clinicians in in identifying high-risk patients for AKI, predicting AKI trajectory, and informing the dosing of renally eliminated drugs.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 3","pages":"ooae097"},"PeriodicalIF":2.5,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11421473/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355732","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":"Recreating Fall Risk Appraisal matrix using R to support fall prevention programs.","authors":"Jethro Raphael M Suarez, Kworweinski Lafontant, Amber Blount, Joon-Hyuk Park, Ladda Thiamwong","doi":"10.1093/jamiaopen/ooae088","DOIUrl":"10.1093/jamiaopen/ooae088","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to optimize Fall Risk Appraisal (FRA) graphing for use in intervention programs tailored toward reducing the fall risk of older adults by using computing graphic functions in the R language.</p><p><strong>Materials and methods: </strong>We utilized RStudio, a free development environment for the R language, as well as the functions within the \"ggplot2\" and \"grid\" packages, to develop a code that would recreate the FRA matrix for use in data visualization and analysis, as well as feedback for older adults.</p><p><strong>Results: </strong>The developed code successfully recreates the FRA matrix in R and allows researchers and clinicians to graph participant data onto the matrix itself.</p><p><strong>Discussion: </strong>The use of an R code allows for a streamlined approach to manipulating the FRA matrix for use in data visualization and feedback for older adults, which improves upon the traditional paper-pencil method that has been previously used.</p><p><strong>Conclusions: </strong>The code presented in this study recreates the FRA matrix instrument in the R language and gives researchers the ability to instantaneously add, remove, or change different aspects of the instrument to improve its readability for researchers and older adults.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 3","pages":"ooae088"},"PeriodicalIF":2.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11410192/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142297353","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":"Establishing data elements and exchange standards to support long COVID healthcare and research.","authors":"Gay Dolin, Himali Saitwal, Karen Bertodatti, Savanah Mueller, Arlene S Bierman, Jerry Suls, Katie Brandt, Djibril S Camara, Stephanie Leppry, Emma Jones, Evelyn Gallego, Dave Carlson, Jenna Norton","doi":"10.1093/jamiaopen/ooae095","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooae095","url":null,"abstract":"<p><strong>Objective: </strong>The Multiple Chronic Conditions (MCCs) Electronic Care (e-Care) Plan project aims to establish care planning data standards for individuals living with MCCs. This article reports on the portion of the project focused on long COVID and presents the process of identifying and modeling data elements using the HL7 Fast Healthcare Interoperability Resources (FHIR) standard.</p><p><strong>Materials and methods: </strong>Critical data elements for managing long COVID were defined through a consensus-driven approach involving a Technical Expert Panel (TEP). This involved 2 stages: identifying data concepts and establishing electronic exchange standards.</p><p><strong>Results: </strong>The TEP-identified and -approved long COVID data elements were mapped to the HL7 US Core FHIR profiles for syntactic representation, and value sets from standard code systems were developed for semantic representation of the long COVID concepts.</p><p><strong>Discussion: </strong>Establishing common long COVID data standards through this process, and representing them with the HL7 FHIR standard, facilitates interoperable data collection, benefiting care delivery and patient-centered outcomes research (PCOR) for long COVID. These standards may support initiatives including clinical and pragmatic trials, quality improvement, epidemiologic research, and clinical and social interventions.By building standards-based data collection, this effort accelerates the development of evidence to better understand and deliver effective long COVID interventions and patient and caregiver priorities within the context of MCCs and to advance the delivery of coordinated, person-centered care.</p><p><strong>Conclusion: </strong>The open, collaborative, and consensus-based approach used in this project will enable the sharing of long COVID-related health concerns, interventions, and outcomes for patient-centered care coordination across diverse clinical settings and will facilitate the use of real-world data for long COVID research.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 3","pages":"ooae095"},"PeriodicalIF":2.5,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11519022/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142548076","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":"Bridging clinical informatics and implementation science to improve cancer symptom management in ambulatory oncology practices: experiences from the IMPACT consortium.","authors":"Nadine Jackson McCleary, James L Merle, Joshua E Richardson, Michael Bass, Sofia F Garcia, Andrea L Cheville, Sandra A Mitchell, Roxanne Jensen, Sarah Minteer, Jessica D Austin, Nathan Tesch, Lisa DiMartino, Michael J Hassett, Raymond U Osarogiagbon, Sandra Wong, Deborah Schrag, David Cella, Ashley Wilder Smith, Justin D Smith","doi":"10.1093/jamiaopen/ooae081","DOIUrl":"10.1093/jamiaopen/ooae081","url":null,"abstract":"<p><strong>Objectives: </strong>To report lessons from integrating the methods and perspectives of clinical informatics (CI) and implementation science (IS) in the context of Improving the Management of symPtoms during and following Cancer Treatment (IMPACT) Consortium pragmatic trials.</p><p><strong>Materials and methods: </strong>IMPACT informaticists, trialists, and implementation scientists met to identify challenges and solutions by examining robust case examples from 3 Research Centers that are deploying systematic symptom assessment and management interventions via electronic health records (EHRs). Investigators discussed data collection and CI challenges, implementation strategies, and lessons learned.</p><p><strong>Results: </strong>CI implementation strategies and EHRs systems were utilized to collect and act upon symptoms and impairments in functioning via electronic patient-reported outcomes (ePRO) captured in ambulatory oncology settings. Limited EHR functionality and data collection capabilities constrained the ability to address IS questions. Collecting ePRO data required significant planning and organizational champions adept at navigating ambiguity.</p><p><strong>Discussion: </strong>Bringing together CI and IS perspectives offers critical opportunities for monitoring and managing cancer symptoms via ePROs. Discussions between CI and IS researchers identified and addressed gaps between applied informatics implementation and theory-based IS trial and evaluation methods. The use of common terminology may foster shared mental models between CI and IS communities to enhance EHR design to more effectively facilitate ePRO implementation and clinical responses.</p><p><strong>Conclusion: </strong>Implementation of ePROs in ambulatory oncology clinics benefits from common understanding of the concepts, lexicon, and incentives between CI implementers and IS researchers to facilitate and measure the results of implementation efforts.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 3","pages":"ooae081"},"PeriodicalIF":2.5,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11373565/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142134104","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":"Leveraging a global, federated, real-world data network to optimize investigator-initiated pediatric clinical trials: the TriNetX Pediatric Collaboratory Network.","authors":"Jurran L Wilson, Marisol Betensky, Sharda Udassi, Pavithra R Ellison, Richard Lilienthal, Lindsay R Stahl, Matvey B Palchuk, Ayesha Zia, Deborah A Town, Wes Kimble, Neil A Goldenberg, Hiroki Morizono","doi":"10.1093/jamiaopen/ooae077","DOIUrl":"10.1093/jamiaopen/ooae077","url":null,"abstract":"<p><strong>Objective: </strong>Clinical research networks facilitate collaborative research, but data sharing remains a common barrier.</p><p><strong>Materials and methods: </strong>The TriNetX platform provides real-time access to electronic health record (EHR)-derived, anonymized data from 173 healthcare organizations (HCOs) and tools for queries and analysis. In 2022, 4 pediatric HCOs worked with TriNetX leadership to found the Pediatric Collaboratory Network (PCN), facilitated via a multi-institutional data-use agreement (DUA). The DUA enables collaborative study design and execution, with institutional review board-approved transfer of complete datasets for further analyses on a per-protocol basis.</p><p><strong>Results and discussion: </strong>Of the 41.2 million children with TriNetX records, the PCN represents nearly 10%. The PCN assisted several early-career investigators to bring study concepts from conception to an international scientific meeting presentation and journal submission.</p><p><strong>Conclusion: </strong>The PCN facilitates EHR vendor-agnostic multicenter pediatric research on the global TriNetX platform. Continued growth of the PCN will advance knowledge in pediatric health.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 3","pages":"ooae077"},"PeriodicalIF":2.5,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11368118/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142120789","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":"Leveraging multi-site electronic health data for characterization of subtypes: a pilot study of dementia in the N3C Clinical Tenant.","authors":"Suchetha Sharma, Jiebei Liu, Amy Caroline Abramowitz, Carol Reynolds Geary, Karen C Johnston, Carol Manning, John Darrell Van Horn, Andrea Zhou, Alfred J Anzalone, Johanna Loomba, Emily Pfaff, Don Brown","doi":"10.1093/jamiaopen/ooae076","DOIUrl":"10.1093/jamiaopen/ooae076","url":null,"abstract":"<p><strong>Objectives: </strong>To provide a foundational methodology for differentiating comorbidity patterns in subphenotypes through investigation of a multi-site dementia patient dataset.</p><p><strong>Materials and methods: </strong>Employing the National Clinical Cohort Collaborative Tenant Pilot (N3C Clinical) dataset, our approach integrates machine learning algorithms-logistic regression and eXtreme Gradient Boosting (XGBoost)-with a diagnostic hierarchical model for nuanced classification of dementia subtypes based on comorbidities and gender. The methodology is enhanced by multi-site EHR data, implementing a hybrid sampling strategy combining 65% Synthetic Minority Over-sampling Technique (SMOTE), 35% Random Under-Sampling (RUS), and Tomek Links for class imbalance. The hierarchical model further refines the analysis, allowing for layered understanding of disease patterns.</p><p><strong>Results: </strong>The study identified significant comorbidity patterns associated with diagnosis of Alzheimer's, Vascular, and Lewy Body dementia subtypes. The classification models achieved accuracies up to 69% for Alzheimer's/Vascular dementia and highlighted challenges in distinguishing Dementia with Lewy Bodies. The hierarchical model elucidates the complexity of diagnosing Dementia with Lewy Bodies and reveals the potential impact of regional clinical practices on dementia classification.</p><p><strong>Conclusion: </strong>Our methodology underscores the importance of leveraging multi-site datasets and tailored sampling techniques for dementia research. This framework holds promise for extending to other disease subtypes, offering a pathway to more nuanced and generalizable insights into dementia and its complex interplay with comorbid conditions.</p><p><strong>Discussion: </strong>This study underscores the critical role of multi-site data analyzes in understanding the relationship between comorbidities and disease subtypes. By utilizing diverse healthcare data, we emphasize the need to consider site-specific differences in clinical practices and patient demographics. Despite challenges like class imbalance and variability in EHR data, our findings highlight the essential contribution of multi-site data to developing accurate and generalizable models for disease classification.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 3","pages":"ooae076"},"PeriodicalIF":2.5,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11316614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141917596","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 application of Breadth-Depth-Context (BDC), a conceptual framework for measuring technology engagement with a qualified clinical data registry.","authors":"Emma Kersey, Jing Li, Julia Kay, Julia Adler-Milstein, Jinoos Yazdany, Gabriela Schmajuk","doi":"10.1093/jamiaopen/ooae061","DOIUrl":"10.1093/jamiaopen/ooae061","url":null,"abstract":"<p><strong>Objectives: </strong>Despite the proliferation of dashboards that display performance data derived from Qualified Clinical Data Registries (QCDR), the degree to which clinicians and practices engage with such dashboards has not been well described. We aimed to develop a conceptual framework for assessing user engagement with dashboard technology and to demonstrate its application to a rheumatology QCDR.</p><p><strong>Materials and methods: </strong>We developed the BDC (Breadth-Depth-Context) framework, which included concepts of breadth (derived from dashboard sessions), depth (derived from dashboard actions), and context (derived from practice characteristics). We demonstrated its application via user log data from the American College of Rheumatology's Rheumatology Informatics System for Effectiveness (RISE) registry to define engagement profiles and characterize practice-level factors associated with different profiles.</p><p><strong>Results: </strong>We applied the BDC framework to 213 ambulatory practices from the RISE registry in 2020-2021, and classified practices into 4 engagement profiles: not engaged (8%), minimally engaged (39%), moderately engaged (34%), and most engaged (19%). Practices with more patients and with specific electronic health record vendors (eClinicalWorks and eMDs) had a higher likelihood of being in the most engaged group, even after adjusting for other factors.</p><p><strong>Discussion: </strong>We developed the BDC framework to characterize user engagement with a registry dashboard and demonstrated its use in a specialty QCDR. The application of the BDC framework revealed a wide range of breadth and depth of use and that specific contextual factors were associated with nature of engagement.</p><p><strong>Conclusion: </strong>Going forward, the BDC framework can be used to study engagement with similar dashboards.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 3","pages":"ooae061"},"PeriodicalIF":2.5,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11278873/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141789317","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}