JAMIA Open最新文献

{"title":"Understanding pediatric long COVID using a tree-based scan statistic approach: an EHR-based cohort study from the RECOVER Program.","authors":"Vitaly Lorman,&nbsp;Suchitra Rao,&nbsp;Ravi Jhaveri,&nbsp;Abigail Case,&nbsp;Asuncion Mejias,&nbsp;Nathan M Pajor,&nbsp;Payal Patel,&nbsp;Deepika Thacker,&nbsp;Seuli Bose-Brill,&nbsp;Jason Block,&nbsp;Patrick C Hanley,&nbsp;Priya Prahalad,&nbsp;Yong Chen,&nbsp;Christopher B Forrest,&nbsp;L Charles Bailey,&nbsp;Grace M Lee,&nbsp;Hanieh Razzaghi","doi":"10.1093/jamiaopen/ooad016","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooad016","url":null,"abstract":"<p><strong>Objectives: </strong>Post-acute sequalae of SARS-CoV-2 infection (PASC) is not well defined in pediatrics given its heterogeneity of presentation and severity in this population. The aim of this study is to use novel methods that rely on data mining approaches rather than clinical experience to detect conditions and symptoms associated with pediatric PASC.</p><p><strong>Materials and methods: </strong>We used a propensity-matched cohort design comparing children identified using the new PASC ICD10CM diagnosis code (U09.9) (<i>N</i> = 1309) to children with (<i>N</i> = 6545) and without (<i>N</i> = 6545) SARS-CoV-2 infection. We used a tree-based scan statistic to identify potential condition clusters co-occurring more frequently in cases than controls.</p><p><strong>Results: </strong>We found significant enrichment among children with PASC in cardiac, respiratory, neurologic, psychological, endocrine, gastrointestinal, and musculoskeletal systems, the most significant related to circulatory and respiratory such as dyspnea, difficulty breathing, and fatigue and malaise.</p><p><strong>Discussion: </strong>Our study addresses methodological limitations of prior studies that rely on prespecified clusters of potential PASC-associated diagnoses driven by clinician experience. Future studies are needed to identify patterns of diagnoses and their associations to derive clinical phenotypes.</p><p><strong>Conclusion: </strong>We identified multiple conditions and body systems associated with pediatric PASC. Because we rely on a data-driven approach, several new or under-reported conditions and symptoms were detected that warrant further investigation.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 1","pages":"ooad016"},"PeriodicalIF":2.1,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/ce/35/ooad016.PMC10013630.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9132702","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":"Adopting a metaverse-based workspace to support research team collaboration: a pilot study from an academic health informatics laboratory.","authors":"Siyi Zhu,&nbsp;Scott Vennemeyer,&nbsp;Catherine Xu,&nbsp;Danny T Y Wu","doi":"10.1093/jamiaopen/ooad010","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooad010","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to understand how a metaverse-based (virtual) workspace can be used to support the communication and collaboration in an academic health informatics lab.</p><p><strong>Materials and methods: </strong>A survey of lab members (n = 14) was analyzed according to a concurrent triangulation mixed methods design. The qualitative survey data were organized according to the Capability, Opportunity, Motivation, Behavior (COM-B) model and combined to generate personas that represent the overall types of lab members. Additionally, scheduled work hours were analyzed quantitatively to complement the findings of the survey feedback.</p><p><strong>Results: </strong>Four personas, representative of different types of virtual workers, were developed using the survey responses. These personas reflected the wide variety of opinions about virtual work among the participants and helped to categorize the most common feedback. The Work Hours Schedule Sheet analysis showed the low number of possible collaboration opportunities that were utilized compared to the number available.</p><p><strong>Discussion: </strong>We found that informal communication and co-location were not supported by the virtual workplace as we had originally planned. To solve this issue, we offer 3 design recommendations for those looking to implement their own virtual informatics lab. First, labs should establish common goals and norms for virtual workplace interactions. Second, labs should carefully plan the virtual space layout to maximize communication opportunities. Finally, labs should work with their platform of choice to address technical limitations for their lab members to improve user experience. Future work includes a formal, theory-guided experiment with consideration on ethical and behavioral impact.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 1","pages":"ooad010"},"PeriodicalIF":2.1,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/7c/58/ooad010.PMC9969827.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9385334","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":"Integrating health disparities content into health informatics courses: a cross-sectional survey study and recommendations.","authors":"Annie T Chen,&nbsp;Uba Backonja,&nbsp;Kenrick Cato","doi":"10.1093/jamiaopen/ooac101","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooac101","url":null,"abstract":"<p><strong>Objective: </strong>To assess the extent to which health disparities content is integrated in multidisciplinary health informatics training programs and examine instructor perspectives surrounding teaching strategies and challenges, including student engagement with course material.</p><p><strong>Materials and methods: </strong>Data for this cross-sectional, descriptive study were collected between April and October 2019. Instructors of informatics courses taught in the United States were recruited via listservs and email. Eligibility was contingent on course inclusion of disparities content. Participants completed an online survey with open- and closed-ended questions to capture administrative- and teaching-related aspects of disparities education within informatics. Quantitative data were analyzed using descriptive statistics; qualitative data were analyzed using inductive coding.</p><p><strong>Results: </strong>Invitations were sent to 141 individuals and 11 listservs. We obtained data from 23 instructors about 24 informatics courses containing health disparities content. Courses were taught primarily in graduate-level programs (<i>n</i> = 21, 87.5%) in informatics (<i>n</i> = 9, 33.3%), nursing (<i>n</i> = 7, 25.9%), and information science (<i>n</i> = 6, 22.2%). The average course covered 6.5 (range 2-13) social determinants of health; socioeconomic status and race/ethnicity (both <i>n</i> = 21, 87.5%) were most frequently addressed. Instructors described multiple obstacles, including lack of resources and time to cover disparities topics adequately, topic sensitivity, and student-related challenges (eg, lack of prior understanding about disparities).</p><p><strong>Discussion: </strong>A foundational and translational knowledge in health disparities is critical to a student's ability to develop future equitable informatics solutions. Based on our findings, we provide recommendations for the intentional and required integration of health disparities-specific content in informatics curricula and competencies.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 1","pages":"ooac101"},"PeriodicalIF":2.1,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/92/0d/ooac101.PMC10027111.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9159704","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":"Correction to: Designing and implementing smart glass technology for emergency medical services: a sociotechnical perspective.","authors":"","doi":"10.1093/jamiaopen/ooad008","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooad008","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1093/jamiaopen/ooac113.].</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 1","pages":"ooad008"},"PeriodicalIF":2.1,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9891239/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9221480","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":"Design and development of a digital shared decision-making tool for stroke prevention in atrial fibrillation.","authors":"Julio C Nunes,&nbsp;Tina Baykaner,&nbsp;Krishna Pundi,&nbsp;Katie DeSutter,&nbsp;Mellanie True Hills,&nbsp;Kenneth W Mahaffey,&nbsp;Samuel F Sears,&nbsp;Daniel P Morin,&nbsp;Bryant Lin,&nbsp;Paul J Wang,&nbsp;Randall S Stafford","doi":"10.1093/jamiaopen/ooad003","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooad003","url":null,"abstract":"<p><strong>Background: </strong>Shared decision-making (SDM) is an approach in which patients and clinicians act as partners in making medical decisions. Patients receive the information needed to decide and are encouraged to balance risks, benefits, and preferences. Informative materials are vital to SDM. Atrial fibrillation (AF) is the most common cardiac arrhythmia and responsible for 10% of ischemic strokes, however 1/3 of patients are not on appropriate anticoagulation. Decision sharing may facilitate treatment acceptance, improving outcomes.</p><p><strong>Aims: </strong>To develop a framework of the components needed to create novel SDM tools and to provide practical examples through a case-study of stroke prevention in AF.</p><p><strong>Methods: </strong>We analyze the design values of a web-based SDM tool created to better inform AF patients about anticoagulation. The tool was developed in partnership with patient advocates, multi-disciplinary investigators, and private design firms. It was refined through iterative, recursive testing in patients with AF. Its effectiveness is being evaluated in a multisite clinical trial led by Stanford University and sponsored by the American Heart Association.</p><p><strong>Findings: </strong>The main components considered when creating the Stanford AFib tool included: design and software; content identification; information delivery; inclusive communication, user engagement; patient feedback; clinician experience; and anticipation of implementation and dissemination. We also highlight the ethical principles underlying SDM; matters of diversity and inclusion, linguistic variety, accessibility, and health literacy. The Stanford AFib Guide patient tool is available at: https://afibguide.com and the clinician tool at https://afibguide.com/clinician.</p><p><strong>Conclusion: </strong>Attention to a range of vital development and design factors can facilitate tool adoption and information acquisition by diverse cultural, educational, and socioeconomic subpopulations. With thoughtful design, digital tools may decrease decision regret and improve treatment outcomes across many decision-making situations in healthcare.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 1","pages":"ooad003"},"PeriodicalIF":2.1,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/75/c1/ooad003.PMC9893868.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9469821","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":"Remote physiologic monitoring for hypertension in primary care: a prospective pragmatic pilot study in electronic health records using propensity score matching.","authors":"Lucia C Petito,&nbsp;Lauren Anthony,&nbsp;Yaw Amofa Peprah,&nbsp;Ji Young Lee,&nbsp;Jim Li,&nbsp;Hironori Sato,&nbsp;Stephen D Persell","doi":"10.1093/jamiaopen/ooac111","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooac111","url":null,"abstract":"<p><strong>Objectives: </strong>Since 2019, the Centers for Medicare and Medicaid Services covers remote physiologic monitoring (RPM) for blood pressure (BP) per hypertension diagnosis and treatment guidelines. Here, we integrated Omron VitalSight RPM into the health system's electronic health record to transmit BP and pulse without manual entry, assessed feasibility, and used pragmatic prospective matched cohort studies to assess initial effects in (1) uncontrolled (last two office BP ≥140/90 mmHg) and (2) general (diagnosed hypertension or last office BP ≥140/90 mmHg) hypertension patient populations.</p><p><strong>Materials and methods: </strong>Seventeen clinicians at two internal medicine practices were oriented. Eligible patients were aged 65-85 years had Medicare insurance with ≥1 office visit in the previous year. We prospectively identified matched controls (age, sex, BP, and number of office visits in previous year) from other primary care practices within the health system and estimated the association between RPM availability (clinic-level) and patient BP outcomes after 6 months. ClinicalTrials.gov: NCT04604925.</p><p><strong>Results: </strong><i>Feasibility.</i> Uptake was low at pilot clinics: 10 physicians prescribed RPM to 118 patients during the 6-month pilot. This included 7% (14/207) of the prespecified uncontrolled hypertension cohort and 3.3% (78/2356) of the general hypertension cohort. Surveyed clinicians (<i>n </i>= 4) reported changing their patients' medical treatment in response to RPM BPs, although they recommended having a dedicated RN or LPN to review BP readings. <i>Effectiveness.</i> At 6 months, BP control was greater at pilot practices than among matched controls (uncontrolled: 31.4% vs 22.8%; <i>P</i> = .007; general: 64.0% vs 59.7%; <i>P</i> < .001). Systolic BP at last office visit did not differ (mean [SD] 146.0 [15.7] vs 147.1 [15.6]; <i>P</i> = .48) in the uncontrolled population, and was lower in the general population (131.8 [15.7] vs 132.8 [15.9]; <i>P</i> = .04).The frequency of antihypertensive medication changes was similar in both groups (uncontrolled <i>P</i> = .986; general <i>P</i> = .218).</p><p><strong>Discussion and conclusions: </strong>Uptake notwithstanding, RPM may have improved BP control. A potential mechanism is increased physician awareness of and attention to uncontrolled hypertension. Barriers to RPM use among physicians require further study.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 1","pages":"ooac111"},"PeriodicalIF":2.1,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9890085/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10667306","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 of a keyword library for capturing PRO-CTCAE-focused \"symptom talk\" in oncology conversations.","authors":"Brigitte N Durieux,&nbsp;Samuel R Zverev,&nbsp;Elise C Tarbi,&nbsp;Anne Kwok,&nbsp;Kate Sciacca,&nbsp;Kathryn I Pollak,&nbsp;James A Tulsky,&nbsp;Charlotta Lindvall","doi":"10.1093/jamiaopen/ooad009","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooad009","url":null,"abstract":"<p><strong>Objectives: </strong>As computational methods for detecting symptoms can help us better attend to patient suffering, the objectives of this study were to develop and evaluate the performance of a natural language processing keyword library for detecting symptom talk, and to describe symptom communication within our dataset to generate insights for future model building.</p><p><strong>Materials and methods: </strong>This was a secondary analysis of 121 transcribed outpatient oncology conversations from the Communication in Oncologist-Patient Encounters trial. Through an iterative process of identifying symptom expressions via inductive and deductive techniques, we generated a library of keywords relevant to the Patient-Reported Outcome version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) framework from 90 conversations, and tested the library on 31 additional transcripts. To contextualize symptom expressions and the nature of misclassifications, we qualitatively analyzed 450 mislabeled and properly labeled symptom-positive turns.</p><p><strong>Results: </strong>The final library, comprising 1320 terms, identified symptom talk among conversation turns with an F1 of 0.82 against a PRO-CTCAE-focused gold standard, and an F1 of 0.61 against a broad gold standard. Qualitative observations suggest that physical symptoms are more easily detected than psychological symptoms (eg, anxiety), and ambiguity persists throughout symptom communication.</p><p><strong>Discussion: </strong>This rudimentary keyword library captures most PRO-CTCAE-focused symptom talk, but the ambiguity of symptom speech limits the utility of rule-based methods alone, and limits to generalizability must be considered.</p><p><strong>Conclusion: </strong>Our findings highlight opportunities for more advanced computational models to detect symptom expressions from transcribed clinical conversations. Future improvements in speech-to-text could enable real-time detection at scale.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 1","pages":"ooad009"},"PeriodicalIF":2.1,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9912707/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9287810","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":"Integrating real-world data to assess cardiac ablation device outcomes in a multicenter study using the OMOP common data model for regulatory decisions: implementation and evaluation.","authors":"Yue Yu,&nbsp;Guoqian Jiang,&nbsp;Eric Brandt,&nbsp;Tom Forsyth,&nbsp;Sanket S Dhruva,&nbsp;Shumin Zhang,&nbsp;Jiajing Chen,&nbsp;Peter A Noseworthy,&nbsp;Amit A Doshi,&nbsp;Kimberly Collison-Farr,&nbsp;Dure Kim,&nbsp;Joseph S Ross,&nbsp;Paul M Coplan,&nbsp;Joseph P Drozda","doi":"10.1093/jamiaopen/ooac108","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooac108","url":null,"abstract":"<p><p>The objective of this study is to describe application of the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to support medical device real-world evaluation in a National Evaluation System for health Technology Coordinating Center (NESTcc) Test-Case involving 2 healthcare systems, Mercy Health and Mayo Clinic. CDM implementation was coordinated across 2 healthcare systems with multiple hospitals to aggregate both medical device data from supply chain databases and patient outcomes and covariates from electronic health record data. Several data quality assurance (QA) analyses were implemented on the OMOP CDM to validate the data extraction, transformation, and load (ETL) process. OMOP CDM-based data of relevant patient encounters were successfully established to support studies for FDA regulatory submissions. QA analyses verified that the data transformation was robust between data sources and OMOP CDM. Our efforts provided useful insights in real-world data integration using OMOP CDM for medical device evaluation coordinated across multiple healthcare systems.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 1","pages":"ooac108"},"PeriodicalIF":2.1,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/28/b6/ooac108.PMC9831049.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9162458","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":"Linking rare and common disease vocabularies by mapping between the human phenotype ontology and phecodes.","authors":"Evonne McArthur,&nbsp;Lisa Bastarache,&nbsp;John A Capra","doi":"10.1093/jamiaopen/ooad007","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooad007","url":null,"abstract":"<p><p>Enabling discovery across the spectrum of rare and common diseases requires the integration of biological knowledge with clinical data; however, differences in terminologies present a major barrier. For example, the Human Phenotype Ontology (HPO) is the primary vocabulary for describing features of rare diseases, while most clinical encounters use International Classification of Diseases (ICD) billing codes. ICD codes are further organized into clinically meaningful phenotypes via phecodes. Despite their prevalence, no robust phenome-wide disease mapping between HPO and phecodes/ICD exists. Here, we synthesize evidence using diverse sources and methods-including text matching, the National Library of Medicine's Unified Medical Language System (UMLS), Wikipedia, SORTA, and PheMap-to define a mapping between phecodes and HPO terms via 38 950 links. We evaluate the precision and recall for each domain of evidence, both individually and jointly. This flexibility permits users to tailor the HPO-phecode links for diverse applications along the spectrum of monogenic to polygenic diseases.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 1","pages":"ooad007"},"PeriodicalIF":2.1,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/df/85/ooad007.PMC9976874.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9464549","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":"Interoperability with multiple Fast Healthcare Interoperability Resources (FHIR^®) profiles and versions.","authors":"Mark A Kramer,&nbsp;Chris Moesel","doi":"10.1093/jamiaopen/ooad001","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooad001","url":null,"abstract":"<p><strong>Objective: </strong>To provide a method for evaluating the interoperability of Fast Healthcare Interoperability Resources (FHIR^®) clients and servers supporting different FHIR resources, profiles, and versions, and to determine the feasibility of FHIR servers supporting multiple FHIR Implementation Guides (IGs).</p><p><strong>Materials and methods: </strong>A method of analysis, the FHIR Interoperability Table (FHIT), is proposed. The FHIT involves the concept of a \"catchment,\" the type or category of data that a profile is intended to represent. The solution first aligns sender and/or receiver profiles according to their catchments, then determines the relationship between the admittances of those profiles, and finally interprets the relationship in terms of the feasibility of data exchange.</p><p><strong>Results: </strong>The FHIT method is demonstrated by analyzing the FHIR-based exchange between the US Core IG and the International Patient Summary IG.</p><p><strong>Discussion: </strong>The last few years have witnessed a significant growth in Fast Healthcare Interoperability Resources (FHIR), resulting in several major versions of FHIR, hundreds of IGs, and thousands of FHIR profiles. Previous work and available tools have not fully addressed the problem of interoperability between clients and servers that support different FHIR resources, profiles, and versions.</p><p><strong>Conclusion: </strong>Application of the proposed methodology allows interoperability problems in FHIR networks to be identified. In some cases, new profiles that resolve those conflicts can be derived, using intersections of the original profiles. There is a need for additional tools that implement the proposed method, as well as structured methods for expressing catchments in FHIR profiles.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 1","pages":"ooad001"},"PeriodicalIF":2.1,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/37/55/ooad001.PMC9904180.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10679762","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}