{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:23:01Z","timestamp":1782778981255,"version":"3.54.5"},"reference-count":58,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T00:00:00Z","timestamp":1632960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"The National Institutes of Health","doi-asserted-by":"crossref","award":["GM120484"],"award-info":[{"award-number":["GM120484"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"The National Institutes of Health","doi-asserted-by":"crossref","award":["HL111111"],"award-info":[{"award-number":["HL111111"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"The National Institutes of Health","doi-asserted-by":"crossref","award":["R01AG058639"],"award-info":[{"award-number":["R01AG058639"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"The National Institutes of Health","doi-asserted-by":"crossref","award":["R01LM012854"],"award-info":[{"award-number":["R01LM012854"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>Hospital capacity management depends on accurate real-time estimates of hospital-wide discharges. Estimation by a clinician requires an excessively large amount of effort and, even when attempted, accuracy in forecasting next-day patient-level discharge is poor. This study aims to support next-day discharge predictions with machine learning by incorporating electronic health record (EHR) audit log data, a resource that captures EHR users\u2019 granular interactions with patients\u2019 records by communicating various semantics and has been neglected in outcome predictions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>This study focused on the EHR data for all adults admitted to Vanderbilt University Medical Center in 2019. We learned multiple advanced models to assess the value that EHR audit log data adds to the daily prediction of discharge likelihood within 24 h and to compare different representation strategies. We applied Shapley additive explanations to identify the most influential types of user-EHR interactions for discharge prediction.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The data include 26 283 inpatient stays, 133 398 patient-day observations, and 819 types of user-EHR interactions. The model using the count of each type of interaction in the recent 24 h and other commonly used features, including demographics and admission diagnoses, achieved the highest area under the receiver operating characteristics (AUROC) curve of 0.921 (95% CI: 0.919\u20130.923). By contrast, the model lacking user-EHR interactions achieved a worse AUROC of 0.862 (0.860\u20130.865). In addition, 10 of the 20 (50%) most influential factors were user-EHR interaction features.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>EHR audit log data contain rich information such that it can improve hospital-wide discharge predictions.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocab211","type":"journal-article","created":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T19:19:40Z","timestamp":1631733580000},"page":"2670-2680","source":"Crossref","is-referenced-by-count":27,"title":["Predicting next-day discharge via electronic health record access logs"],"prefix":"10.1093","volume":"28","author":[{"given":"Xinmeng","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6719-1388","authenticated-orcid":false,"given":"Chao","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bradley A","family":"Malin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA"},{"name":"Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA"},{"name":"Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mayur B","family":"Patel","sequence":"additional","affiliation":[{"name":"Section of Surgical Sciences, Departments of Surgery & Neurosurgery, Division of Trauma, Surgical Critical Care, and Emergency General Surgery, Nashville, Tennessee, USA"},{"name":"Geriatric Research and Education Clinical Center, Surgical Services, Veteran Affairs Tennessee Valley Healthcare System, Nashville, Tennessee, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8232-8840","authenticated-orcid":false,"given":"You","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA"},{"name":"Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"2021120106305638800_ocab211-B1","first-page":"298","article-title":"Systematic review of operations research and simulation methods for bed management","author":"Baru","year":"2015"},{"issue":"6","key":"2021120106305638800_ocab211-B2","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1097\/01.CCM.0000266585.74905.5A","article-title":"Impact of delayed transfer of critically ill patients from the emergency department to the intensive care unit","volume":"35","author":"Chalfin","year":"2007","journal-title":"Crit Care Med"},{"issue":"1","key":"2021120106305638800_ocab211-B3","doi-asserted-by":"crossref","first-page":"23","DOI":"10.4103\/1735-1995.200277","article-title":"Overcrowding in emergency departments: a review of strategies to decrease future challenges","volume":"22","author":"Rezaei","year":"2017","journal-title":"J Res Med Sci"},{"key":"2021120106305638800_ocab211-B4","author":"Al Taleb","year":"2017"},{"issue":"10","key":"2021120106305638800_ocab211-B5","doi-asserted-by":"crossref","first-page":"e010466","DOI":"10.1136\/bmjopen-2015-010466","article-title":"What factors predict length of stay in a neonatal unit: a systematic review","volume":"6","author":"Seaton","year":"2016","journal-title":"BMJ Open"},{"issue":"1","key":"2021120106305638800_ocab211-B6","doi-asserted-by":"crossref","first-page":"110","DOI":"10.5614\/j.eng.technol.sci.2018.50.1.8","article-title":"Healthcare data mining: predicting hospital length of stay of dengue patients","volume":"50","author":"Wiratmadja","year":"2018","journal-title":"J Eng Technol Sci"},{"issue":"2","key":"2021120106305638800_ocab211-B7","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1136\/bmjinnov-2020-000420","article-title":"Machine-learning-based hospital discharge predictions can support multidisciplinary rounds and decrease hospital length-of-stay","volume":"7","author":"Levin","year":"2021","journal-title":"BMJ Innov"},{"issue":"2","key":"2021120106305638800_ocab211-B8","doi-asserted-by":"crossref","first-page":"e395","DOI":"10.1542\/peds.2015-0456","article-title":"Predicting discharge dates from the neonatal intensive care unit using progress note data","volume":"136","author":"Temple","year":"2015","journal-title":"Pediatrics"},{"issue":"3","key":"2021120106305638800_ocab211-B9","doi-asserted-by":"crossref","first-page":"166","DOI":"10.4258\/hir.2020.26.3.166","article-title":"Application of predictive modelling to improve the discharge process in hospitals","volume":"26","author":"Hisham","year":"2020","journal-title":"Healthc Inform Res"},{"key":"2021120106305638800_ocab211-B10","article-title":"Patient discharge classification using machine learning techniques","author":"Gramaje","year":"2019","journal-title":"Ann Data Sci"},{"issue":"1","key":"2021120106305638800_ocab211-B11","doi-asserted-by":"crossref","first-page":"e146","DOI":"10.1542\/peds.2009-0810","article-title":"Predicting time to hospital discharge for extremely preterm infants","volume":"125","author":"Hintz","year":"2010","journal-title":"Pediatrics"},{"issue":"12","key":"2021120106305638800_ocab211-B12","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1002\/jhm.2439","article-title":"An evaluation of physician predictions of discharge on a general medicine service: physician predictions of discharge","volume":"10","author":"Sullivan","year":"2015","journal-title":"J Hosp Med"},{"issue":"3","key":"2021120106305638800_ocab211-B13","doi-asserted-by":"crossref","first-page":"e000174","DOI":"10.1136\/bmjoq-2017-000174","article-title":"Introducing an electronic tracking tool into daily multidisciplinary discharge rounds on a medicine service: a quality improvement project to reduce length of stay","volume":"7","author":"Meo","year":"2018","journal-title":"BMJ Open Qual"},{"issue":"2","key":"2021120106305638800_ocab211-B14","first-page":"105","article-title":"A review of discharge-prediction processes in acute care hospitals","volume":"12","author":"De Grood","year":"2016","journal-title":"Healthc Policy"},{"issue":"2","key":"2021120106305638800_ocab211-B15","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1097\/00005110-200602000-00006","article-title":"The effect of a multidisciplinary hospitalist\/physician and advanced practice nurse collaboration on hospital costs","volume":"36","author":"Cowan","year":"2006","journal-title":"J Nurs Adm"},{"issue":"5","key":"2021120106305638800_ocab211-B16","first-page":"217","article-title":"Using real-time demand capacity management to improve hospitalwide patient flow","volume":"37","author":"Resar","year":"2011","journal-title":"Jt Comm J Qual Patient Saf"},{"issue":"e1","key":"2021120106305638800_ocab211-B17","doi-asserted-by":"crossref","first-page":"e2","DOI":"10.1093\/jamia\/ocv106","article-title":"Real-time prediction of inpatient length of stay for discharge prioritization","volume":"23","author":"Barnes","year":"2016","journal-title":"J Am Med Inform Assoc"},{"issue":"4","key":"2021120106305638800_ocab211-B18","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1007\/s00134-003-2139-7","article-title":"Can the experienced ICU physician predict ICU length of stay and outcome better than less experienced colleagues?","volume":"30","author":"Gusm\u00e3o Vicente","year":"2004","journal-title":"Intensive Care Med"},{"issue":"11","key":"2021120106305638800_ocab211-B19","doi-asserted-by":"crossref","first-page":"3058","DOI":"10.1097\/CCM.0b013e31825bc399","article-title":"Real-time forecasting of pediatric intensive care unit length of stay using computerized provider orders","volume":"40","author":"Levin","year":"2012","journal-title":"Crit Care Med"},{"issue":"1","key":"2021120106305638800_ocab211-B20","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1186\/1472-6947-11-64","article-title":"Computerized prediction of intensive care unit discharge after cardiac surgery: development and validation of a Gaussian processes model","volume":"11","author":"Meyfroidt","year":"2011","journal-title":"BMC Med Inform Decis Mak"},{"issue":"2","key":"2021120106305638800_ocab211-B21","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1177\/0951484817696212","article-title":"Patient length of stay and mortality prediction: a survey","volume":"30","author":"Awad","year":"2017","journal-title":"Health Serv Manage Res"},{"key":"2021120106305638800_ocab211-B22","doi-asserted-by":"crossref","DOI":"10.1109\/HEALTHCOM49281.2021.9399025","article-title":"Operationally-informed hospital-wide discharge prediction using machine learning","author":"Ward","year":"2021"},{"key":"2021120106305638800_ocab211-B23","volume-title":"Predicting Surgical Inpatients\u2019 Discharges at Massachusetts General Hospital","author":"Zanger","year":"2018"},{"key":"2021120106305638800_ocab211-B24","first-page":"1651","article-title":"Hospital bed management support using regression data mining models","author":"Oliveira","year":"2014"},{"issue":"2","key":"2021120106305638800_ocab211-B25","doi-asserted-by":"crossref","first-page":"121","DOI":"10.4258\/hir.2013.19.2.121","article-title":"Use of data mining techniques to determine and predict length of stay of cardiac patients","volume":"19","author":"Hachesu","year":"2013","journal-title":"Healthc Inform Res"},{"issue":"12","key":"2021120106305638800_ocab211-B26","doi-asserted-by":"crossref","first-page":"e1917221","DOI":"10.1001\/jamanetworkopen.2019.17221","article-title":"Development and validation of a machine learning model to aid discharge processes for inpatient surgical care","volume":"2","author":"Safavi","year":"2019","journal-title":"JAMA Netw Open"},{"key":"2021120106305638800_ocab211-B27","doi-asserted-by":"crossref","first-page":"103343","DOI":"10.1016\/j.jbi.2019.103343","article-title":"EHR audit logs: a new goldmine for health services research?","volume":"101","author":"Adler-Milstein","year":"2020","journal-title":"J Biomed Inform"},{"issue":"5","key":"2021120106305638800_ocab211-B28","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1370\/afm.2121","article-title":"Tethered to the EHR: primary care physician workload assessment using EHR event log data and time-motion observations","volume":"15","author":"Arndt","year":"2017","journal-title":"Ann Fam Med"},{"issue":"1","key":"2021120106305638800_ocab211-B29","doi-asserted-by":"crossref","first-page":"e12650","DOI":"10.2196\/12650","article-title":"Use of electronic health record access and audit logs to identify physician actions following noninterruptive alert opening: descriptive study","volume":"7","author":"Amroze","year":"2019","journal-title":"JMIR Med Inform"},{"issue":"4","key":"2021120106305638800_ocab211-B30","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1093\/jamia\/ocz223","article-title":"Metrics for assessing physician activity using electronic health record log data","volume":"27","author":"Sinsky","year":"2020","journal-title":"J Am Med Inform Assoc"},{"issue":"3","key":"2021120106305638800_ocab211-B31","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1055\/s-0040-1713133","article-title":"Attributing patients to pediatric residents using electronic health record features augmented with audit logs","volume":"11","author":"Mai","year":"2020","journal-title":"Appl Clin Inform"},{"issue":"5","key":"2021120106305638800_ocab211-B32","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1093\/jamia\/ocaa305","article-title":"Conceptual considerations for using EHR-based activity logs to measure clinician burnout and its effects","volume":"28","author":"Kannampallil","year":"2021","journal-title":"J Am Med Inform Assoc"},{"key":"2021120106305638800_ocab211-B33","doi-asserted-by":"crossref","DOI":"10.1093\/jamia\/ocaa338","article-title":"Mining tasks and task characteristics from electronic health record audit logs with unsupervised machine learning","author":"Chen","year":"2021","journal-title":"J Am Med Inform Assoc"},{"issue":"1","key":"2021120106305638800_ocab211-B34","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1136\/amiajnl-2012-001145","article-title":"Next-generation phenotyping of electronic health records","volume":"20","author":"Hripcsak","year":"2013","journal-title":"J Am Med Inform Assoc"},{"issue":"e2","key":"2021120106305638800_ocab211-B35","doi-asserted-by":"crossref","first-page":"e311","DOI":"10.1136\/amiajnl-2013-001922","article-title":"Correlating electronic health record concepts with healthcare process events","volume":"20","author":"Hripcsak","year":"2013","journal-title":"J Am Med Inform Assoc"},{"issue":"6","key":"2021120106305638800_ocab211-B36","doi-asserted-by":"crossref","first-page":"1242","DOI":"10.1093\/jamia\/ocab006","article-title":"Healthcare process modeling to phenotype clinician behaviors for exploiting the signal gain of clinical expertise (HPM-ExpertSignals): development and evaluation of a conceptual framework","volume":"28","author":"Rossetti","year":"2021","journal-title":"J Am Med Inform Assoc"},{"issue":"1","key":"2021120106305638800_ocab211-B37","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1093\/jamia\/ocx098","article-title":"Secondary use of electronic health record data for clinical workflow analysis","volume":"25","author":"Hribar","year":"2018","journal-title":"J Am Med Inform Assoc"},{"key":"2021120106305638800_ocab211-B38","first-page":"612","article-title":"Learning tasks of pediatric providers from Electronic Health Record audit logs","volume":"2020","author":"Jones","year":"2020","journal-title":"AMIA Annu Symp Proc"},{"issue":"1","key":"2021120106305638800_ocab211-B39","doi-asserted-by":"crossref","first-page":"ooab014","DOI":"10.1093\/jamiaopen\/ooab014","article-title":"An electronic health record (EHR) log analysis shows limited clinician engagement with unsolicited genetic test results","volume":"4","author":"Nestor","year":"2021","journal-title":"JAMIA Open"},{"key":"2021120106305638800_ocab211-B40","first-page":"1820","article-title":"Using EHR audit trail logs to analyze clinical workflow: a case study from community-based ambulatory clinics","volume":"2017","author":"Wu","year":"2017","journal-title":"AMIA Annu Symp Proc"},{"issue":"3","key":"2021120106305638800_ocab211-B41","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1093\/jamia\/ocz196","article-title":"Using electronic health record audit logs to study clinical activity: a systematic review of aims, measures, and methods","volume":"27","author":"Rule","year":"2020","journal-title":"J Am Med Inform Assoc"},{"key":"2021120106305638800_ocab211-B42","first-page":"886","article-title":"Time-motion examination of electronic health record utilization and clinician workflows indicate frequent task switching and documentation burden","volume":"2020","author":"Moy","year":"2020","journal-title":"AMIA Annu Symp Proc"},{"issue":"7","key":"2021120106305638800_ocab211-B43","doi-asserted-by":"crossref","first-page":"e0233004","DOI":"10.1371\/journal.pone.0233004","article-title":"Analyses of electronic health records utilization in a large community hospital","volume":"15","author":"Verma","year":"2020","journal-title":"PLoS One"},{"key":"2021120106305638800_ocab211-B44","first-page":"63","article-title":"Detection of anomalous insiders in collaborative environments via relational analysis of access logs","volume":"2011","author":"Chen","year":"2011","journal-title":"CODASPY"},{"key":"2021120106305638800_ocab211-B45","author":"Yan","year":"2018"},{"issue":"2","key":"2021120106305638800_ocab211-B46","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1111\/imm.12195","article-title":"The challenges, advantages and future of phenome-wide association studies","volume":"141","author":"Hebbring","year":"2014","journal-title":"Immunology"},{"issue":"3","key":"2021120106305638800_ocab211-B47","doi-asserted-by":"crossref","first-page":"169","DOI":"10.7326\/M18-3684","article-title":"Physician time spent using the electronic health record during outpatient encounters: a descriptive study","volume":"172","author":"Overhage","year":"2020","journal-title":"Ann Intern Med"},{"key":"2021120106305638800_ocab211-B48","first-page":"3149","article-title":"LGBM: a highly efficient gradient boosting decision tree","author":"Ke","year":"2017"},{"issue":"1","key":"2021120106305638800_ocab211-B49","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1038\/s41591-019-0724-8","article-title":"Prediction of gestational diabetes based on nationwide electronic health records","volume":"26","author":"Artzi","year":"2020","journal-title":"Nat Med"},{"issue":"7767","key":"2021120106305638800_ocab211-B50","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1038\/s41586-019-1390-1","article-title":"A clinically applicable approach to continuous prediction of future acute kidney injury","volume":"572","author":"Toma\u0161ev","year":"2019","journal-title":"Nature"},{"key":"2021120106305638800_ocab211-B51","article-title":"RETAIN: an interpretable predictive model for healthcare using REverse Time AttentIoN mechanism [Internet]","author":"Choi","year":"2016","journal-title":"arXiv [cs.LG]"},{"issue":"1","key":"2021120106305638800_ocab211-B52","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1002\/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3","article-title":"Index for rating diagnostic tests","volume":"3","author":"Youden","year":"1950","journal-title":"Cancer"},{"issue":"4","key":"2021120106305638800_ocab211-B53","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1093\/jamia\/ocz228","article-title":"A tutorial on calibration measurements and calibration models for clinical prediction models","volume":"27","author":"Huang","year":"2020","journal-title":"J Am Med Inform Assoc"},{"key":"2021120106305638800_ocab211-B54","article-title":"On calibration of modern neural networks [Internet]","author":"Guo","year":"2017","journal-title":"arXiv [cs.LG]"},{"key":"2021120106305638800_ocab211-B55","article-title":"A unified approach to interpreting model predictions [Internet]","author":"Lundberg","year":"2017","journal-title":"arXiv [cs.AI]"},{"issue":"4","key":"2021120106305638800_ocab211-B56","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1055\/s-0041-1733851","article-title":"Measuring electronic health record use in the pediatric ICU using audit-logs and screen recordings","volume":"12","author":"Sinha","year":"2021","journal-title":"Appl Clin Inform"},{"issue":"1","key":"2021120106305638800_ocab211-B57","doi-asserted-by":"crossref","first-page":"e25724","DOI":"10.2196\/25724","article-title":"Collaboration structures in COVID-19 critical care: retrospective network analysis study","volume":"8","author":"Yan","year":"2021","journal-title":"JMIR Hum Factors"},{"issue":"11","key":"2021120106305638800_ocab211-B58","doi-asserted-by":"crossref","first-page":"1606","DOI":"10.1164\/rccm.202008-3114LE","article-title":"Network analysis subtleties in ICU structures and outcomes","volume":"202","author":"Chen","year":"2020","journal-title":"Am J Respir Crit Care Med"}],"container-title":["Journal of the American Medical Informatics Association"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jamia\/article-pdf\/28\/12\/2670\/41325326\/ocab211.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jamia\/article-pdf\/28\/12\/2670\/41325326\/ocab211.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T10:02:20Z","timestamp":1638352940000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jamia\/article\/28\/12\/2670\/6378628"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,30]]},"references-count":58,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,9,30]]},"published-print":{"date-parts":[[2021,11,25]]}},"URL":"https:\/\/doi.org\/10.1093\/jamia\/ocab211","relation":{},"ISSN":["1527-974X"],"issn-type":[{"value":"1527-974X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,12,1]]},"published":{"date-parts":[[2021,9,30]]}}}