• Eva K Lee, Fan Yuan, Daniel A Hirsh, Michael D Mallory, and Harold K Simon.
• Center for Operations Research in Medicine and HealthCare, Georgia Institute of Technology, Georgia, USA. eva.lee@gatech.edu
• AMIA Annu Symp Proc. 2012 Jan 1; 2012: 495-504.

AbstractThe primary purpose of this study was to develop a clinical tool capable of identifying discriminatory characteristics that can predict patients who will return within 72 hours to the Pediatric emergency department (PED). We studied 66,861 patients who were discharged from the EDs during the period from May 1 2009 to December 31 2009. We used a classification model to predict return visits based on factors extracted from patient demographic information, chief complaint, diagnosis, treatment, and hospital real-time ED statistics census. We began with a large pool of potentially important factors, and used particle swarm optimization techniques for feature selection coupled with an optimization-based discriminant analysis model (DAMIP) to identify a classification rule with relatively small subsets of discriminatory factors that can be used to predict - with 80% accuracy or greater - return within 72 hours. The analysis involves using a subset of the patient cohort for training and establishment of the predictive rule, and blind predicting the return of the remaining patients. Good candidate factors for revisit prediction are obtained where the accuracy of cross validation and blind prediction are over 80%. Among the predictive rules, the most frequent discriminatory factors identified include diagnosis (> 97%), patient complaint (>97%), and provider type (> 57%). There are significant differences in the readmission characteristics among different acuity levels. For Level 1 patients, critical readmission factors include patient complaint (>57%), time when the patient arrived until he/she got an ED bed (> 64%), and type/number of providers (>50%). For Level 4/5 patients, physician diagnosis (100%), patient complaint (99%), disposition type when patient arrives and leaves the ED (>30%), and if patient has lab test (>33%) appear to be significant. The model was demonstrated to be consistent and predictive across multiple PED sites.The resulting tool could enable ED staff and administrators to use patient specific values for each of a small number of discriminatory factors, and in return receive a prediction as to whether the patient will return to the ED within 72 hours. Our prediction accuracy can be as high as over 85%. This provides an opportunity for improving care and offering additional care or guidance to reduce ED readmission.

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