title: Real-time prediction of mortality, readmission, and length of stay using electronic health record data.
abstract: To develop a predictive model for real-time predictions of length of stay, mortality, and readmission for hospitalized patients using electronic health records (EHRs).
  
  
  
  A Bayesian Network model was built to estimate the probability of a hospitalized patient being "at home," 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 32 634 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.
  
  
  
  The model achieved an average daily accuracy of 80% and area under the receiving operating characteristic curve (AUROC) of 0.82. The model's predictive ability was highest within 24 hours from prediction (AUROC = 0.83) and decreased slightly with time. Death was the most predictable outcome with a daily average accuracy of 93% and AUROC of 0.84.
  
  
  
  We developed the first non-disease-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.
  
  
  
  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.
authors.affiliation: School of Computer Science and Engineering, The University of New South Wales, Sydney, Australia.
authors.forename: Xiongcai
authors.initials: X
authors.lastname: Cai
authors.affiliation: Centre of Health Informatics, AIHI, Macquarie University, Sydney, Australia.
authors.forename: Oscar
authors.initials: O
authors.lastname: Perez-Concha
authors.affiliation: Centre of Health Informatics, AIHI, Macquarie University, Sydney, Australia.
authors.forename: Enrico
authors.initials: E
authors.lastname: Coiera
authors.affiliation: Melbourne School of Information, The University of Melbourne, Melbourne, Australia.
authors.forename: Fernando
authors.initials: F
authors.lastname: Martin-Sanchez
authors.affiliation: School of Medical Sciences, The University of New South Wales, Sydney, Australia.
authors.forename: Richard
authors.initials: R
authors.lastname: Day
authors.affiliation: Information Technology Service Centre, St Vincent's Hospital, Sydney, Australia.
authors.forename: David
authors.initials: D
authors.lastname: Roffe
authors.affiliation: Centre of Health Informatics, AIHI, Macquarie University, Sydney, Australia blanca.gallegoluxan@mq.edu.au.
authors.forename: Blanca
authors.initials: B
authors.lastname: Gallego
mesh_terms: D001499:Bayes Theorem; D057286:Electronic Health Records; D017052:Hospital Mortality; D006760:Hospitalization; D006801:Humans; D007902:Length of Stay; D015233:Models, Statistical; D010359:Patient Readmission; D011336:Probability; D011379:Prognosis; D012372:ROC Curve
keywords: length of stay; mortality; patient outcome; prediction; readmission