Machine-learning-based hospital discharge predictions can support multidisciplinary rounds and decrease hospital length-of-stay. Issue 2 (21st December 2020)

• Record Type: Journal Article
• Authors: Levin, Scott; Barnes, Sean; Toerper, Matthew; Debraine, Arnaud; DeAngelo, Anthony; Hamrock, Eric; Hinson, Jeremiah; Hoyer, Erik; Dungarani, Trushar; Howell, Eric
• Is Part Of: BMJ innovations. Volume 7:Issue 2(2021)
• Page Start: 414
• Page End: 421
• Publication Date: 2020-12-21
• Subjects: assistive technology -- delivery -- medical apps; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjinnov-2020-000420
• Languages: English
• ISSNs: 2055-8074

Abstract

Abstract : 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.