Can targeting high-risk patients reduce readmission rates? Evidence from Israel

Efrat Shadmi, Dan Zeltzer, Tzvi Shir, Natalie Flaks-Manov, Liran Einav, Ran D. Balicer

Research output: Contribution to journalArticlepeer-review

Abstract

We study a large intervention intended to reduce hospital readmission rates in Israel. Since 2012, readmission risk was calculated for patients aged 65 and older, and high-risk patients were flagged to providers upon admission and after discharge. Analyzing 171,541 admissions during 2009–2016, we find that the intervention reduced 30-day readmission rates by 5.9% among patients aged 65–70 relative to patients aged 60–64, who were not targeted by the intervention and for whom no risk-scores were calculated. The largest reduction, 12.3%, was among high-risk patients, though some of it may reflect substitution of attention away from patients with unknown high-risk at the point of care. Post-discharge follow-up encounters were significantly expedited. Estimated effects declined after incentives to reduce readmission rates were discontinued. The evidence demonstrates that informing providers about patient risk in real-time coupled with incentives to reduce readmissions can improve care continuity and reduce hospital readmissions.

Original languageEnglish
Pages (from-to)729-745
Number of pages17
JournalJournal of Applied Economics
Volume23
Issue number1
DOIs
StatePublished - 1 Jan 2020

Keywords

  • Healthcare
  • hospital readmissions
  • predictive modeling

ASJC Scopus subject areas

  • Economics, Econometrics and Finance (all)

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