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Published on: September 22, 2023
Interpreting Oncology Care Model Data to Drive Value-Based Care: A Prostate Cancer Analysis
Ronald D Ennis1, Anish B Parikh2, Mark Sanderson3
11 Rutgers Cancer Institute of New Jersey, New Brunswick, NJ.
Purpose::
The Oncology Care Model (OCM) must be clinically relevant, accurate, and comprehensible to drive value-based care.
Methods::
We studied OCM data detailing observed and expected expenses for 6-month-long episodes of care for patients with prostate cancer. We constructed seven disease state-treatment dyads into which we grouped each episode on the bases of diagnoses, procedures, and medications in OCM claims data. We used this clinical-administrative stratification model to facilitate a comparative cost analysis, and we evaluated emergency department and hospital utilization and drug therapy as potential drivers of cost.
Results::
We examined 377 episodes of care, pertaining to 210 patients, that took place within our health system from January 2012 to June 2015. Ninety-six percent of episodes were assigned to clinically meaningful dyads. Excessive expenses were seen in metastatic, castration-resistant dyads containing second-line hormone therapy (ratio of observed to expected expenses [O/E], 2.66), chemotherapy (O/E, 2.09), and radium-223/sipuleucel-T (O/E, 3.01). An OCM update correcting for castration-resistant prostate cancer led to small differences in observed expenses (0% to +2%) but large changes in expected expenses (-17% to -27% for hormone-sensitive dyads and +136% to +141% for castration-resistant dyads). O/E increased up to 38% for hormone-sensitive dyads and decreased up to 58% for castration-resistant dyads. Emergency department and hospital utilization seems to drive cost for castration-resistant dyads but not for hormone-sensitive dyads. In the revised OCM model, overall O/E for all episodes improved by 22%, from 1.48 to 1.15.
Conclusion::
Our experience with OCM highlights the limitations of administrative claims data within this model and illustrates a method of translating these data into clinically meaningful information to improve value.
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