• Efrat Shadmi, Natalie Flaks-Manov, Moshe Hoshen, Orit Goldman, Haim Bitterman, and Ran D Balicer.
• *Faculty of Social Welfare and Health Sciences, University of Haifa, Mount Carmel †Clalit Research Institute, Chief Physician's Office, Clalit Health Services, Tel Aviv ‡Faculty of Medicine, Technion Institute of Technology, Haifa §Epidemiology Department, Faculty of Health Sciences, Ben-Gurion University, Beer-Sheva, Israel.
• Med Care. 2015 Mar 1; 53 (3): 283-9.

BackgroundReadmission prevention should begin as early as possible during the index admission. Early identification may help target patients for within-hospital readmission prevention interventions.ObjectivesTo develop and validate a 30-day readmission prediction model using data from electronic health records available before the index admission.Research DesignRetrospective cohort study of admissions between January 1 and March 31, 2010.SubjectsAdult enrollees of Clalit Health Services, an integrated delivery system, admitted to an internal medicine ward in any hospital in Israel.MeasuresAll-cause 30-day emergency readmissions. A prediction score based on before admission electronic health record and administrative data (the Preadmission Readmission Detection Model-PREADM) was developed using a preprocessing variable selection step with decision trees and neural network algorithms. Admissions with a recent prior hospitalization were excluded and automatically flagged as "high-risk." Selected variables were entered into multivariable logistic regression, with a derivation (two-thirds) and a validation cohort (one-third).ResultsThe derivation dataset comprised 17,334 admissions, of which 2913 (16.8%) resulted in a 30-day readmission. The PREADM includes 11 variables: chronic conditions, prior health services use, body mass index, and geographical location. The c-statistic was 0.70 in the derivation set and of 0.69 in the validation set. Adding length of stay did not change the discriminatory power of the model.ConclusionsThe PREADM is designed for use by health plans for early high-risk case identification, presenting discriminatory power better than or similar to that of previously reported models, most of which include data available only upon discharge.

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