Title

Abstract

Document Type

Poster

Publication Date

2016

Disciplines

Medicine and Health Sciences

Abstract

Under the Hospital Readmission Reduction Program (HRRP), hospitals with high readmission rates are penalized by reductions in Medicare reimbursements. In particular, Parkview Noble hospital has experienced high readmission rates for chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), and acute myocardial infarction (AMI). Parkview Noble’s combined 30-day readmission rates for these conditions is 18.19% compared to the national average of 15.86%. Objective: In order to effectively reduce the readmission rate for COPD and CHF patients at Parkview Noble, this study examined the socioeconomic and clinical factors that can be used to target this patient population for healthcare driven interventions. Design/Methods: Data was collected in the form of a retrospective chart review and phone interview of 260 patients. The data obtained was then analyzed using student t-test, chi-square test, and multi-variable regression to determine statistically promising variables. A linear regression using statistically promising variables was performed to obtained preliminary predictive algorithms for COPD and CHF readmission. The sensitivity and specificity of these equations was then plotted using various cut-off values to determine their practical effectiveness. Results: The statistical analysis determined four factors with strong predictive value including: past ED visits in the last 6 months (p=1.4E-6), past ED admissions in the last year (p=0.03), heart failure with coronary artery disease (p=0.05), and stage of COPD (p=0.06). The sensitivity and specificity plots suggest that it is possible to target 20% of readmission patients while still maintaining over 85% specificity. Conclusions: From this preliminary data it is seen that several variables have value in determining a patient’s likelihood of readmission. Using this data as a benchmark, this study will be expanded to include up to 1,028 more patients before a final predictive algorithm is computed and tested.

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