1. Machine-learning-based hospital discharge predictions can support multidisciplinary rounds and decrease hospital length-of-stay
- Author
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Arnaud Debraine, Matthew F. Toerper, Jeremiah S. Hinson, Anthony DeAngelo, Erik H. Hoyer, Scott Levin, Eric E Howell, Trushar Dungarani, Sean Barnes, and Eric Hamrock
- Subjects
Patient discharge ,medicine.medical_specialty ,Financial performance ,Receiver operating characteristic ,business.industry ,010102 general mathematics ,Length of hospitalization ,General Medicine ,01 natural sciences ,Patient flow ,03 medical and health sciences ,0302 clinical medicine ,Multidisciplinary approach ,Emergency medicine ,Hospital discharge ,medicine ,030212 general & internal medicine ,0101 mathematics ,business ,Predictive modelling - Abstract
BackgroundPatient flow directly affects quality of care, access and financial performance for hospitals. Multidisciplinary discharge-focused rounds have proven to minimise avoidable delays experienced by patients near discharge. The study objective was to support discharge-focused rounds by implementing a machine-learning-based discharge prediction model using real-time electronic health record (EHR) data. We aimed to evaluate model predictive performance and impact on hospital length-of-stay.MethodsDischarge prediction models were developed from hospitalised patients on four inpatient units between April 2016 and September 2018. Unit-specific models were implemented to make individual patient predictions viewable with the EHR patient track board. Predictive performance was measured prospectively for 12 470 patients (120 780 patient-predictions) across all units. A pre/poststudy design applying interrupted time series methods was used to assess the impact of the discharge prediction model on hospital length-of-stay.ResultsProspective discharge prediction performance ranged in area under the receiver operating characteristic curve from 0.70 to 0.80 for same-day and next-day predictions; sensitivity was between 0.63 and 0.83 and specificity between 0.48 and 0.80. Elapsed length-of-stay, counts of labs and medications, mobility assessments and measures of acute kidney injury were model features providing the most predictive value. Implementing the discharge predictions resulted in a reduction in hospital length-of-stay of over 12 hours on a medicine unit (pConclusionsIncorporating automated patient discharge predictions into multidisciplinary rounds can support decreases in hospital length-of-stay. Variation in execution and impact across inpatient units existed.
- Published
- 2020