PT - JOURNAL ARTICLE 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 TI - Machine-learning-based hospital discharge predictions can support multidisciplinary rounds and decrease hospital length-of-stay AID - 10.1136/bmjinnov-2020-000420 DP - 2021 Apr 01 TA - BMJ Innovations PG - 414--421 VI - 7 IP - 2 4099 - http://innovations.bmj.com/content/7/2/414.short 4100 - http://innovations.bmj.com/content/7/2/414.full SO - BMJ Innov2021 Apr 01; 7 AB - 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.No data are available. Patient-level hospital data are prohibited for sharing under both federal privacy (HIPAA) and contractual constraints. However, authors are happy to share additional methodological detail and code if requested. Please email slevin33@jhmi.edu