title: Real-time prediction of inpatient length of stay for discharge prioritization.
abstract: OBJECTIVE
  Hospitals are challenged to provide timely patient care while maintaining high resource utilization. This has prompted hospital initiatives to increase patient flow and minimize nonvalue added care time. Real-time demand capacity management (RTDC) is one such initiative whereby clinicians convene each morning to predict patients able to leave the same day and prioritize their remaining tasks for early discharge. Our objective is to automate and improve these discharge predictions by applying supervised machine learning methods to readily available health information.
  
  
  METHODS
  The authors use supervised machine learning methods to predict patients' likelihood of discharge by 2 p.m. and by midnight each day for an inpatient medical unit. Using data collected over 8000 patient stays and 20 000 patient days, the predictive performance of the model is compared to clinicians using sensitivity, specificity, Youden's Index (i.e., sensitivity + specificity - 1), and aggregate accuracy measures.
  
  
  RESULTS
  The model compared to clinician predictions demonstrated significantly higher sensitivity (P < .01), lower specificity (P < .01), and a comparable Youden Index (P > .10). Early discharges were less predictable than midnight discharges. The model was more accurate than clinicians in predicting the total number of daily discharges and capable of ranking patients closest to future discharge.
  
  
  CONCLUSIONS
  There is potential to use readily available health information to predict daily patient discharges with accuracies comparable to clinician predictions. This approach may be used to automate and support daily RTDC predictions aimed at improving patient flow.
authors.affiliation: Department of Decision, Operations & Information Technologies, Robert H. Smith School of Business, 4352 Van Munching Hall, University of Maryland, College Park, MD 20742, USA sbarnes@rhsmith.umd.edu.
authors.forename: Sean
authors.initials: S
authors.lastname: Barnes
authors.affiliation: Department of Operations Integration, Johns Hopkins Health System, Baltimore, MD, USA.
authors.forename: Eric
authors.initials: E
authors.lastname: Hamrock
authors.affiliation: Department of Emergency Medicine, Johns Hopkins Hospital, Baltimore, MD, USA.
authors.forename: Matthew
authors.initials: M
authors.lastname: Toerper
authors.affiliation: Departments of Civil Engineering and Applied Mathematics & Statistics, Johns Hopkins Systems Institute, Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Sauleh
authors.initials: S
authors.lastname: Siddiqui
authors.affiliation: Department of Emergency Medicine and Civil Engineering, Johns Hopkins Systems Institute, Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Scott
authors.initials: S
authors.lastname: Levin
mesh_terms: D000046:Academic Medical Centers; D000328:Adult; D000368:Aged; D005260:Female; D006739:Hospital Administration; D006801:Humans; D007902:Length of Stay; D000069550:Machine Learning; D008297:Male; D008396:Maryland; D008875:Middle Aged; D010351:Patient Discharge; D057188:Workflow; D016526:Workload
keywords: length of stay; machine learning; operational forecasting; patient flow