{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T04:51:19Z","timestamp":1783227079739,"version":"3.54.6"},"reference-count":21,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2015,9,15]],"date-time":"2015-09-15T00:00:00Z","timestamp":1442275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,5,1]]},"abstract":"<jats:p>Objective To develop a predictive model for real-time predictions of length of stay, mortality, and readmission for hospitalized patients using electronic health records (EHRs).<\/jats:p>\n               <jats:p>Materials and Methods A Bayesian Network model was built to estimate the probability of a hospitalized patient being \u201cat home,\u201d in the hospital, or dead for each of the next 7 days. The network utilizes patient-specific administrative and laboratory data and is updated each time a new pathology test result becomes available. Electronic health records from 32\u2009634 patients admitted to a Sydney metropolitan hospital via the emergency department from July 2008 through December 2011 were used. The model was tested on 2011 data and trained on the data of earlier years.<\/jats:p>\n               <jats:p>Results The model achieved an average daily accuracy of 80% and area under the receiving operating characteristic curve (AUROC) of 0.82. The model\u2019s predictive ability was highest within 24 hours from prediction (AUROC\u2009=\u20090.83) and decreased slightly with time. Death was the most predictable outcome with a daily average accuracy of 93% and AUROC of 0.84.<\/jats:p>\n               <jats:p>Discussion We developed the first non\u2013disease-specific model that simultaneously predicts remaining days of hospitalization, death, and readmission as part of the same outcome. By providing a future daily probability for each outcome class, we enable the visualization of future patient trajectories. Among these, it is possible to identify trajectories indicating expected discharge, expected continuing hospitalization, expected death, and possible readmission.<\/jats:p>\n               <jats:p>Conclusions Bayesian Networks can model EHRs to provide real-time forecasts for patient outcomes, which provide richer information than traditional independent point predictions of length of stay, death, or readmission, and can thus better support decision making.<\/jats:p>","DOI":"10.1093\/jamia\/ocv110","type":"journal-article","created":{"date-parts":[[2015,9,16]],"date-time":"2015-09-16T01:00:36Z","timestamp":1442365236000},"page":"553-561","source":"Crossref","is-referenced-by-count":101,"title":["Real-time prediction of mortality, readmission, and length of stay using electronic health record data"],"prefix":"10.1093","volume":"23","author":[{"given":"Xiongcai","family":"Cai","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, The University of New South Wales, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oscar","family":"Perez-Concha","sequence":"additional","affiliation":[{"name":"Centre of Health Informatics, AIHI, Macquarie University, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enrico","family":"Coiera","sequence":"additional","affiliation":[{"name":"Centre of Health Informatics, AIHI, Macquarie University, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fernando","family":"Martin-Sanchez","sequence":"additional","affiliation":[{"name":"Melbourne School of Information, The University of Melbourne, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Day","sequence":"additional","affiliation":[{"name":"School of Medical Sciences, The University of New South Wales, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Roffe","sequence":"additional","affiliation":[{"name":"Information Technology Service Centre, St Vincent\u2019s Hospital, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Blanca","family":"Gallego","sequence":"additional","affiliation":[{"name":"Centre of Health Informatics, AIHI, Macquarie University, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2015,9,15]]},"reference":[{"issue":"3","key":"2021010611501034400_ocv110-B1","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.resuscitation.2004.01.021","article-title":"Evaluation of a medical emergency team one year after implementation","volume":"61","author":"Kenward","year":"2004","journal-title":"Resuscitation"},{"key":"2021010611501034400_ocv110-B2","doi-asserted-by":"crossref","DOI":"10.1155\/2012\/486516","article-title":"A literature review on care at the end-of-life in the emergency department","author":"Forero","year":"2012","journal-title":"Emerg Med Int"},{"key":"2021010611501034400_ocv110-B3","doi-asserted-by":"crossref","first-page":"f5913","DOI":"10.1136\/bmj.f5913","article-title":"Including post-discharge mortality in calculation of hospital standardised mortality ratios: retrospective analysis of hospital episode statistics","volume":"347","author":"Pouw","year":"2013","journal-title":"BMJ"},{"issue":"18","key":"2021010611501034400_ocv110-B4","doi-asserted-by":"crossref","first-page":"2539","DOI":"10.1111\/j.1365-2702.2008.02685.x","article-title":"Hospital discharge planning for frail older people and their family. 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