Empirical research
The Effect of the Hospital Readmission Reduction Program on the Duration of Observation Stays: Using Regression Discontinuity to Estimate Causal Effects
Authors:
Jordan Albritton ,
Intermountain Healthcare, US
Thomas Belnap,
Intermountain Healthcare, US
Lucy Savitz
Kaiser Permanente Center for Health Research, US
Abstract
Research Objective: Determine whether hospitals are increasing the duration of observation stays following index admission for heart failure to avoid potential payment penalties from the Hospital Readmission Reduction Program.
Study Design: The Hospital Readmission Reduction Program applies a 30-day cutoff after which readmissions are no longer penalized. Given this seemingly arbitrary cutoff, we use regression discontinuity design, a quasi-experimental research design that can be used to make causal inferences.
Population Studied: The High Value Healthcare Collaborative includes member healthcare systems covering 57% of the nation’s hospital referral regions. We used Medicare claims data including all patients residing within these regions. The study included patients with index admissions for heart failure from January 1, 2012 to June 30, 2015 and a subsequent observation stay within 60 days. We excluded hospitals with fewer than 25 heart failure readmissions in a year or fewer than 5 observation stays in a year and patients with subsequent observation stays at a different hospital.
Principal Findings: Overall, there was no discontinuity at the 30-day cutoff in the duration of observation stays, the percent of observation stays over 12 hours, or the percent of observation stays over 24 hours. In the sub-analysis, the discontinuity was significant for non-penalized.
Conclusion: The findings reveal evidence that the HRRP has resulted in an increase in the duration of observation stays for some non-penalized hospitals.
How to Cite:
Albritton J, Belnap T, Savitz L. The Effect of the Hospital Readmission Reduction Program on the Duration of Observation Stays: Using Regression Discontinuity to Estimate Causal Effects. eGEMs (Generating Evidence & Methods to improve patient outcomes). 2017;5(3):6. DOI: http://doi.org/10.5334/egems.197
