PREDICTING EPISODE-BASED CARE COSTS FOLLOWING CORONARY INTERVENTION
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Quality of Care and Outcomes Assessment
E1503JACC March 12, 2013Volume 61, Issue 10
predicTing episode-Based care cosTs following coronary inTervenTion
Moderated Poster ContributionsPoster Sessions, Expo NorthSaturday, March 09, 2013, 3:45 p.m.-4:30 p.m.
Session Title: Enhancing Value in Coronary Intervention and Myocardial Infarction CareAbstract Category: 28. Quality of Care and Outcomes AssessmentPresentation Number: 1157M-94
Authors: Matthew Bunte, Matthew Cavender, Rory Hachamovitch, Robert Mackenzie, Pamela Goepfarth, Julianne Nichols, Dhanesh Shah, Kenneth Kissel, Shaheen Bhanji, Jacob Miller, Matthew Pohlman, Jason Gilder, Anil Jain, Joseph Cacchione, Cleveland Clinic, Cleveland, OH, USA
Background: Bundled payment models for healthcare offer cost reduction opportunities, although such models have yet to be applied to percutaneous coronary intervention (PCI)-related treatment. Episode-based cost prediction may advantage providers taking on additional financial risk in an emerging landscape of accountable care.
methods: Data from 300 Medicare patients undergoing PCI at the Cleveland Clinic from 2008-2009 were used to generate a model of bundled-care cost. Clinical data was obtained from the electronic medical record and was matched to data within the National Cardiovascular Data Registry and administrative claims. The Prometheus Payment Model was used to define an episode of care. Preoperative clinical variables from 200 patients were considered within a hierarchy of three statistical models to estimate a weighted episode price. Regression models were validated with data from the remaining 100 patients.
results: Cost bundling allowed categorization of patients into low, medium, and high price classes. Relevant acute care costs determined the full episode price for 72% of patients at a median of $10,946. Ten percent had high post-procedural costs. Full episode price was predicted within $5000 for 75% of patients.
conclusion: As proof of concept, bundled payment modeling can predict relevant costs based on pre-procedural data. This cost bundling framework may be used to identify resource-intensive patients early in the course of care, prompting strategies to reduce overall cost.