Predicting 30-day readmissions with preadmission electronic health record data

Med Care. 2015 Mar;53(3):283-9. doi: 10.1097/MLR.0000000000000315.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Cohort Studies
  • Decision Support Techniques
  • Electronic Health Records / statistics & numerical data*
  • Forecasting
  • Humans
  • Inpatients / statistics & numerical data
  • Israel / epidemiology
  • Middle Aged
  • Multivariate Analysis
  • Outcome Assessment, Health Care
  • Patient Admission / statistics & numerical data*
  • Patient Readmission / statistics & numerical data*
  • Retrospective Studies
  • Risk Assessment / methods