JAMIA Open最新文献

{"title":"Designing and testing clinical simulations of an early warning system for implementation in acute care settings.","authors":"Min-Jeoung Kang, Sarah C Rossetti, Graham Lowenthal, Christopher Knaplund, Li Zhou, Kumiko O Schnock, Kenrick D Cato, Patricia C Dykes","doi":"10.1093/jamiaopen/ooae092","DOIUrl":"10.1093/jamiaopen/ooae092","url":null,"abstract":"<p><strong>Objectives: </strong>Conducting simulation testing with end-users is essential for facilitating successful implementation of new health information technologies. This study designed a standardized simulation testing process with a system prototype prior to implementation to help study teams identify the system's interpretability and feasibility from the end-user perspective and to effectively integrate new innovations into real-world clinical settings and workflows.</p><p><strong>Materials and methods: </strong>A clinical simulation model was developed to test a new Clinical Decision Support (CDS) system outside of the clinical environment while maintaining high fidelity. A web-based CDS prototype, the \"CONCERN Smart Application,\" which leverages clinical data to measure and express a patient's risk of deterioration on a 3-level scale (\"low,\" \"moderate,\" or \"high\"), and audiovisual-integrated materials, were used to lead simulation sessions.</p><p><strong>Results: </strong>A total of 6 simulation sessions with 17 nurses were held to investigate how nurses interact with the CONCERN Smart application and how it influences their critical thinking, and clinical responses. Four themes were extracted from the simulation debriefing sessions and used to inform implementation strategies. The strategies include how the CDS should be improved for practical real-world use.</p><p><strong>Discussion and conclusions: </strong>Standardized simulation testing procedures identified and informed the necessary CDS improvements, the enhancements needed for real-world use, and the training requirements to effectively prepare end-users for system go-live.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 4","pages":"ooae092"},"PeriodicalIF":2.5,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11483109/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142476671","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":"Tree-based classification model for Long-COVID infection prediction with age stratification using data from the National COVID Cohort Collaborative.","authors":"Will Ke Wang, Hayoung Jeong, Leeor Hershkovich, Peter Cho, Karnika Singh, Lauren Lederer, Ali R Roghanizad, Md Mobashir Hasan Shandhi, Warren Kibbe, Jessilyn Dunn","doi":"10.1093/jamiaopen/ooae111","DOIUrl":"10.1093/jamiaopen/ooae111","url":null,"abstract":"<p><strong>Objectives: </strong>We propose and validate a domain knowledge-driven classification model for diagnosing post-acute sequelae of SARS-CoV-2 infection (PASC), also known as Long COVID, using Electronic Health Records (EHRs) data.</p><p><strong>Materials and methods: </strong>We developed a robust model that incorporates features strongly indicative of PASC or associated with the severity of COVID-19 symptoms as identified in our literature review. The XGBoost tree-based architecture was chosen for its ability to handle class-imbalanced data and its potential for high interpretability. Using the training data provided by the Long COVID Computation Challenge (L3C), which was a sample of the National COVID Cohort Collaborative (N3C), our models were fine-tuned and calibrated to optimize Area Under the Receiver Operating characteristic curve (AUROC) and the F1 score, following best practices for the class-imbalanced N3C data.</p><p><strong>Results: </strong>Our age-stratified classification model demonstrated strong performance with an average 5-fold cross-validated AUROC of 0.844 and F1 score of 0.539 across the young adult, mid-aged, and older-aged populations in the training data. In an independent testing dataset, which was made available after the challenge was over, we achieved an overall AUROC score of 0.814 and F1 score of 0.545.</p><p><strong>Discussion: </strong>The results demonstrated the utility of knowledge-driven feature engineering in a sparse EHR data and demographic stratification in model development to diagnose a complex and heterogeneously presenting condition like PASC. The model's architecture, mirroring natural clinician decision-making processes, contributed to its robustness and interpretability, which are crucial for clinical translatability. Further, the model's generalizability was evaluated over a new cross-sectional data as provided in the later stages of the L3C challenge.</p><p><strong>Conclusion: </strong>The study proposed and validated the effectiveness of age-stratified, tree-based classification models to diagnose PASC. Our approach highlights the potential of machine learning in addressing the diagnostic challenges posed by the heterogeneity of Long-COVID symptoms.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 4","pages":"ooae111"},"PeriodicalIF":2.5,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11547948/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142629987","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":"VMAP: Vaginal Microbiome Atlas during Pregnancy.","authors":"Antonio Parraga-Leo, Tomiko T Oskotsky, Boris Oskotsky, Camilla Wibrand, Alennie Roldan, Alice S Tang, Connie W Y Ha, Ronald J Wong, Samuel S Minot, Gaia Andreoletti, Idit Kosti, Kevin R Theis, Sherrianne Ng, Yun S Lee, Patricia Diaz-Gimeno, Phillip R Bennett, David A MacIntyre, Susan V Lynch, Roberto Romero, Adi L Tarca, David K Stevenson, Nima Aghaeepour, Jonathan L Golob, Marina Sirota","doi":"10.1093/jamiaopen/ooae099","DOIUrl":"10.1093/jamiaopen/ooae099","url":null,"abstract":"<p><strong>Objectives: </strong>To enable interactive visualization of the vaginal microbiome across the pregnancy and facilitate discovery of novel insights and generation of new hypotheses.</p><p><strong>Material and methods: </strong>Vaginal Microbiome Atlas during Pregnancy (VMAP) was created with R shiny to generate visualizations of structured vaginal microbiome data from multiple studies.</p><p><strong>Results: </strong>VMAP (http://vmapapp.org) visualizes 3880 vaginal microbiome samples of 1402 pregnant individuals from 11 studies, aggregated via open-source tool MaLiAmPi. Visualized features include diversity measures, VALENCIA community state types, and composition (phylotypes, taxonomy) that can be filtered by various categories.</p><p><strong>Discussion: </strong>This work represents one of the largest and most geographically diverse aggregations of the vaginal microbiome in pregnancy to date and serves as a user-friendly resource to further analyze vaginal microbiome data and better understand pregnancies and associated outcomes.</p><p><strong>Conclusion: </strong>VMAP can be obtained from https://github.com/msirota/vmap.git and is currently deployed as an online app for non-R users.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 3","pages":"ooae099"},"PeriodicalIF":2.5,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11430916/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355733","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":"<i>Literature search sandbox</i>: a large language model that generates search queries for systematic reviews.","authors":"Gaelen P Adam, Jay DeYoung, Alice Paul, Ian J Saldanha, Ethan M Balk, Thomas A Trikalinos, Byron C Wallace","doi":"10.1093/jamiaopen/ooae098","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooae098","url":null,"abstract":"<p><strong>Objectives: </strong>Development of search queries for systematic reviews (SRs) is time-consuming. In this work, we capitalize on recent advances in large language models (LLMs) and a relatively large dataset of natural language descriptions of reviews and corresponding Boolean searches to generate Boolean search queries from SR titles and key questions.</p><p><strong>Materials and methods: </strong>We curated a training dataset of 10 346 SR search queries registered in PROSPERO. We used this dataset to fine-tune a set of models to generate search queries based on Mistral-Instruct-7b. We evaluated the models quantitatively using an evaluation dataset of 57 SRs and qualitatively through semi-structured interviews with 8 experienced medical librarians.</p><p><strong>Results: </strong>The model-generated search queries had median sensitivity of 85% (interquartile range [IQR] 40%-100%) and number needed to read of 1206 citations (IQR 205-5810). The interviews suggested that the models lack both the necessary sensitivity and precision to be used without scrutiny but could be useful for topic scoping or as initial queries to be refined.</p><p><strong>Discussion: </strong>Future research should focus on improving the dataset with more high-quality search queries, assessing whether fine-tuning the model on other fields, such as the population and intervention, improves performance, and exploring the addition of interactivity to the interface.</p><p><strong>Conclusions: </strong>The datasets developed for this project can be used to train and evaluate LLMs that map review descriptions to Boolean search queries. The models cannot replace thoughtful search query design but may be useful in providing suggestions for key words and the framework for the query.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 3","pages":"ooae098"},"PeriodicalIF":2.5,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11424077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355731","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":"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":"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}