Assessment of Machine Learning vs Standard Prediction Rules for Predicting Hospital Readmissions

• Published in: JAMA network open Vol. 2; no. 3; p. e190348
• Main Authors: Morgan, Daniel J, Bame, Bill, Zimand, Paul, Dooley, Patrick, Thom, Kerri A, Harris, Anthony D, Bentzen, Soren, Ettinger, Walt, Garrett-Ray, Stacy D, Tracy, J Kathleen, Liang, Yuanyuan
• Format: Journal Article
• Language: English
• Published: United States American Medical Association 01.03.2019
• Subjects: Female; Health Informatics; Hospitalization - economics; Hospitalization - statistics & numerical data; Humans; Length of Stay; Machine Learning; Male; Maryland - epidemiology; Medicare - statistics & numerical data; Middle Aged; Online Only; Original Investigation; Patient Readmission - economics; Patient Readmission - statistics & numerical data; Prognosis; Research Design - standards; Risk Assessment - methods; Risk Assessment - standards; Risk Factors; United States; United States; Maryland
• ISSN: 2574-3805; 2574-3805
• DOI: 10.1001/jamanetworkopen.2019.0348

Hospital readmissions are associated with patient harm and expense. Ways to prevent hospital readmissions have focused on identifying patients at greatest risk using prediction scores. To identify the type of score that best predicts hospital readmissions. This prognostic study included 14 062 consecutive adult hospital patients with 16 649 discharges from a tertiary care center, suburban community hospital, and urban critical access hospital in Maryland from September 1, 2016, through December 31, 2016. Patients not included as eligible discharges by the Centers for Medicare & Medicaid Services or the Chesapeake Regional Information System for Our Patients were excluded. A machine learning rank score, the Baltimore score (B score) developed using a machine learning technique, for each individual hospital using data from the 2 years before September 1, 2016, was compared with standard readmission risk assessment scores to predict 30-day unplanned readmissions. The 30-day readmission rate evaluated using various readmission scores: B score, HOSPITAL score, modified LACE score, and Maxim/RightCare score. Of the 10 732 patients (5605 [52.2%] male; mean [SD] age, 54.56 [22.42] years) deemed to be eligible for the study, 1422 were readmitted. The area under the receiver operating characteristic curve (AUROC) for individual rules was 0.63 (95% CI, 0.61-0.65) for the HOSPITAL score, which was significantly lower than the 0.66 for modified LACE score (95% CI, 0.64-0.68; P < .001). The B score machine learning score was significantly better than all other scores; 48 hours after admission, the AUROC of the B score was 0.72 (95% CI, 0.70-0.73), which increased to 0.78 (95% CI, 0.77-0.79) at discharge (all P < .001). At the hospital using Maxim/RightCare score, the AUROC was 0.63 (95% CI, 0.59-0.69) for HOSPITAL, 0.64 (95% CI, 0.61-0.68) for Maxim/RightCare, and 0.66 (95% CI, 0.62-0.69) for modified LACE score. The B score was 0.72 (95% CI, 0.69-0.75) 48 hours after admission and 0.81 (95% CI, 0.79-0.84) at discharge. In directly comparing the B score with the sensitivity at cutoff values for modified LACE, HOSPITAL, and Maxim/RightCare scores, the B score was able to identify the same number of readmitted patients while flagging 25.5% to 54.9% fewer patients. Among 3 hospitals in different settings, an automated machine learning score better predicted readmissions than commonly used readmission scores. More efficiently targeting patients at higher risk of readmission may be the first step toward potentially preventing readmissions.