{"doi":"10.1093/jamiaopen/ooag116","title":"Patterns and predictors of clinician use of interoperability tools","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives</jats:title>\n                    <jats:p>Clinicians’ adoption of interoperability tools influences care quality, but evidence of actual use is limited. We analyze clinicians’ use of outside records delivered via Epic Care Everywhere (CE), focusing on use frequency and predictors such as gender, experience, specialty, and role. Differences between pre-pandemic (2018-2019) and pandemic (2020-2021) periods are also examined to see how COVID-19 affected use of outside records.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods</jats:title>\n                    <jats:p>De-identified EHR metadata from UCSF clinicians (n = 1442) during pre-pandemic and pandemic periods, totaling 686 797 clinician-day observations, were analyzed. We measured usage intensity (mean CE lookups per appointment) and breadth (percentage of appointments with ≥1 lookup). Generalized linear models (GLMs) with a negative binomial distribution for overdispersion were used to estimate predictors of intensity.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Results</jats:title>\n                    <jats:p>CE usage intensity rose by 43.0% post-COVID-19 onset compared with the pre-pandemic. Clinician specialty most strongly predicted use, with Nephrology and Cardiology showing the highest breadth (56.0% and 53.0% of visits, respectively), while Dermatology (13.2%) and Pediatrics (23.4%) were lowest. Residents used CE at 23.0% greater intensity than attendings, and each additional year of experience was linked to a 0.57% decrease in intensity.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion</jats:title>\n                    <jats:p>Clinician use of interoperability tools was higher in specialties such as Nephrology and Cardiology that require more care coordination, and among less experienced clinicians including resident physicians. Use increased after the pandemic began, likely due to ongoing adoption trends and increased clinical demands during system strain and uncertainty.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion</jats:title>\n                    <jats:p>These findings underscore the critical importance of considering clinician behavior and contextual factors, such as specialty care needs, in addition to technical capabilities, when promoting the adoption and use of interoperability tools.</jats:p>\n                  </jats:sec>","journal":"JAMIA Open","year":2026,"id":662591,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":907152,"name":"A Jay Holmgren","orcid":"0000-0002-7939-6831","position":1,"is_corresponding":false},{"id":1729777,"name":"Hector P Rodriguez","orcid":null,"position":2,"is_corresponding":false},{"id":1729776,"name":"Sadaf Ashtari","orcid":"0000-0002-5841-9934","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Patterns and predictors of clinician use of interoperability tools","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives</jats:title>\n                    <jats:p>Clinicians’ adoption of interoperability tools influences care quality, but evidence of actual use is limited. We analyze clinicians’ use of outside records delivered via Epic Care Everywhere (CE), focusing on use frequency and predictors such as gender, experience, specialty, and role. Differences between pre-pandemic (2018-2019) and pandemic (2020-2021) periods are also examined to see how COVID-19 affected use of outside records.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods</jats:title>\n                    <jats:p>De-identified EHR metadata from UCSF clinicians (n = 1442) during pre-pandemic and pandemic periods, totaling 686 797 clinician-day observations, were analyzed. We measured usage intensity (mean CE lookups per appointment) and breadth (percentage of appointments with ≥1 lookup). Generalized linear models (GLMs) with a negative binomial distribution for overdispersion were used to estimate predictors of intensity.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Results</jats:title>\n                    <jats:p>CE usage intensity rose by 43.0% post-COVID-19 onset compared with the pre-pandemic. Clinician specialty most strongly predicted use, with Nephrology and Cardiology showing the highest breadth (56.0% and 53.0% of visits, respectively), while Dermatology (13.2%) and Pediatrics (23.4%) were lowest. Residents used CE at 23.0% greater intensity than attendings, and each additional year of experience was linked to a 0.57% decrease in intensity.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion</jats:title>\n                    <jats:p>Clinician use of interoperability tools was higher in specialties such as Nephrology and Cardiology that require more care coordination, and among less experienced clinicians including resident physicians. Use increased after the pandemic began, likely due to ongoing adoption trends and increased clinical demands during system strain and uncertainty.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion</jats:title>\n                    <jats:p>These findings underscore the critical importance of considering clinician behavior and contextual factors, such as specialty care needs, in addition to technical capabilities, when promoting the adoption and use of interoperability tools.</jats:p>\n                  </jats:sec>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"42428528","pmcid":null,"openalex_id":"https://openalex.org/W7164389363","authors":[],"funders":[{"funder_name":"American Medical Association (AMA) Foundation","grant_id":"","title":null},{"funder_name":"AMA EHR Use Metrics Grant","grant_id":"","title":null}],"total_grants":2,"fwci":0.0,"citation_percentile":0.75985908,"influential_citations":0,"citation_trend":[],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://doi.org/10.1093/jamiaopen/ooag116","host_type":"journal"},{"url":"https://doi.org/10.1093/jamiaopen/ooag116","host_type":"publisher"},{"url":"https://academic.oup.com/jamiaopen/advance-article-pdf/doi/10.1093/jamiaopen/ooag116/68513743/ooag116.pdf","host_type":"publisher"},{"url":"https://academic.oup.com/jamiaopen/article-pdf/9/4/ooag116/68513743/ooag116.pdf","host_type":"publisher"},{"url":"https://pubmed.ncbi.nlm.nih.gov/42428528","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13348719/","host_type":"repository"}],"fields_of_study":["Telemedicine and Telehealth Implementation","Electronic Health Records Systems","Mobile Health and mHealth Applications"],"mesh_terms":[],"keywords":["Specialty","Interoperability","Pandemic","MEDLINE","Coronavirus disease 2019 (COVID-19)","Patient care","Electronic Health Records","Health Information Exchange","Clinician Behavior","Care Everywhere"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-12T16:12:07.183289Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}