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Predicting 30-Day Readmissions With Preadmission Electronic Health Record Data

Shadmi, Efrat, PhD*,†; Flaks-Manov, Natalie, MPH; Hoshen, Moshe, PhD; Goldman, Orit, PhD; Bitterman, Haim, MD†,‡; Balicer, Ran D., MD, PhD†,§

doi: 10.1097/MLR.0000000000000315
Original Articles

Background: Readmission prevention should begin as early as possible during the index admission. Early identification may help target patients for within-hospital readmission prevention interventions.

Objectives: To develop and validate a 30-day readmission prediction model using data from electronic health records available before the index admission.

Research Design: Retrospective cohort study of admissions between January 1 and March 31, 2010.

Subjects: Adult enrollees of Clalit Health Services, an integrated delivery system, admitted to an internal medicine ward in any hospital in Israel.

Measures: All-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).

Results: The 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.

Conclusions: The 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.

*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

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A portion of the study’s findings were presented at the Annual Research Meeting of Academy Health; Baltimore, MD; June 23, 2013.

The study was supported in part by a grant from the Israel National Institute for Health Policy Research (NIHP) (10/127).

The authors declare no conflict of interest.

Reprints: Efrat Shadmi, PhD, Faculty of Social Welfare and Health Sciences, University of Haifa, Eshkol Tower Room 2104, Haifa 31905, Israel. E-mail:

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