Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/314114 
Year of Publication: 
2020
Citation: 
[Journal:] Journal of Applied Economics [ISSN:] 1667-6726 [Volume:] 23 [Issue:] 1 [Year:] 2020 [Pages:] 729-745
Publisher: 
Taylor & Francis, Abingdon
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.
Subjects: 
Healthcare
hospital readmissions
predictive modeling
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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