title: A comparison of models for predicting early hospital readmissions.
abstract: Risk sharing arrangements between hospitals and payers together with penalties imposed by the Centers for Medicare and Medicaid (CMS) are driving an interest in decreasing early readmissions. There are a number of published risk models predicting 30day readmissions for particular patient populations, however they often exhibit poor predictive performance and would be unsuitable for use in a clinical setting. In this work we describe and compare several predictive models, some of which have never been applied to this task and which outperform the regression methods that are typically applied in the healthcare literature. In addition, we apply methods from deep learning to the five conditions CMS is using to penalize hospitals, and offer a simple framework for determining which conditions are most cost effective to target.
authors.affiliation: Dept. of Statistical Science, Duke University, Box 90251, Durham, NC 27708, USA. Electronic address: jdf38@stat.duke.edu.
authors.forename: Joseph
authors.initials: J
authors.lastname: Futoma
authors.affiliation: Quintiles, 4820 Emperor Blvd., Durham, NC 27703, USA. Electronic address: jonmorrismd@gmail.com.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Morris
authors.affiliation: Dept. of Statistical Science, Duke University, Box 90251, Durham, NC 27708, USA; Dept. of Electrical and Computer Engineering, Duke University, Box 90291, Durham, NC 27708, USA. Electronic address: joe@stat.duke.edu.
authors.forename: Joseph
authors.initials: J
authors.lastname: Lucas
mesh_terms: D000328:Adult; D000465:Algorithms; D019540:Area Under Curve; D003362:Cost-Benefit Analysis; D003953:Diagnosis-Related Groups; D057286:Electronic Health Records; D005260:Female; D006302:Health Services Research / Q000379:methods*; D006761:Hospitals; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009520:New Zealand; D010359:Patient Readmission / Q000191:economics / Q000706:statistics & numerical data*; D012372:ROC Curve; D012044:Regression Analysis; D018570:Risk Assessment; D012984:Software
keywords: Deep learning; Early readmission; Electronic Health Records; Penalized methods; Predictive models; Random forest