TY - JOUR T1 - Machine-learning-based hospital discharge predictions can support multidisciplinary rounds and decrease hospital length-of-stay JF - BMJ Innovations JO - BMJ Innov DO - 10.1136/bmjinnov-2020-000420 SP - bmjinnov-2020-000420 AU - Scott Levin AU - Sean Barnes AU - Matthew Toerper AU - Arnaud Debraine AU - Anthony DeAngelo AU - Eric Hamrock AU - Jeremiah Hinson AU - Erik Hoyer AU - Trushar Dungarani AU - Eric Howell Y1 - 2020/12/20 UR - http://innovations.bmj.com/content/early/2020/12/20/bmjinnov-2020-000420.abstract N2 - Background Patient 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.Methods Discharge 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.Results Prospective 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 (p<0.001) and telemetry unit (p=0.002), while no changes were observed for the surgery unit (p=0.190) and second medicine unit (p<0.555).Conclusions Incorporating automated patient discharge predictions into multidisciplinary rounds can support decreases in hospital length-of-stay. Variation in execution and impact across inpatient units existed. ER -