Identifying patients at highest-risk: The best timing to apply a readmission predictive model

Natalie Flaks-Manov, Maxim Topaz, Moshe Hoshen, Ran D. Balicer, Efrat Shadmi

Research output: Contribution to journalArticlepeer-review


Background: Most of readmission prediction models are implemented at the time of patient discharge. However, interventions which include an early in-hospital component are critical in reducing readmissions and improving patient outcomes. Thus, at-discharge high-risk identification may be too late for effective intervention. Nonetheless, the tradeoff between early versus at-discharge prediction and the optimal timing of the risk prediction model application remains to be determined. We examined a high-risk patient selection process with readmission prediction models using data available at two time points: at admission and at the time of hospital discharge. Methods: An historical prospective study of hospitalized adults (≥65 years) discharged alive from internal medicine units in Clalit's (the largest integrated payer-provider health fund in Israel) general hospitals in 2015. The outcome was all-cause 30-day emergency readmissions to any internal medicine ward at any hospital. We used the previously validated Preadmission Readmission Detection Model (PREADM) and developed a new model incorporating PREADM with hospital data (PREADM-H). We compared the percentage of overlap between the models and calculated the positive predictive value (PPV) for the subgroups identified by each model separately and by both models. Results: The final cohort included 35,156 index hospital admissions. The PREADM-H model included 17 variables with a C-statistic of 0.68 (95% CI: 0.67-0.70) and PPV of 43.0% in the highest-risk categories. Of patients categorized by the PREADM-H in the highest-risk decile, 78% were classified similarly by the PREADM. The 22% (n = 229) classified by the PREADM-H at the highest decile, but not by the PREADM, had a PPV of 37%. Conversely, those classified by the PREADM into the highest decile but not by the PREADM-H (n = 218) had a PPV of 31%. Conclusions: The timing of readmission risk prediction makes a difference in terms of the population identified at each prediction time point - at-admission or at-discharge. Our findings suggest that readmission risk identification should incorporate a two time-point approach in which preadmission data is used to identify high-risk patients as early as possible during the index admission and an "all-hospital" model is applied at discharge to identify those that incur risk during the hospital stay.

Original languageEnglish
Article number118
JournalBMC Medical Informatics and Decision Making
Issue number1
StatePublished - 26 Jun 2019

Bibliographical note

Publisher Copyright:
© 2019 The Author(s).


  • All-cause readmission
  • Electronic medical records
  • Timing of readmission predictive model

ASJC Scopus subject areas

  • Health Policy
  • Health Informatics


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