Identifying the Most Costly Patients in Radiation Oncology and Predicting the Top Spenders.

Abstract

Quality payment programs aim to adjust payments on the basis of quality and cost; however, few quality metrics exist in radiation oncology. This study evaluates and predicts the top spenders (TS) after radiation therapy (RT). Patient characteristics, cancer details, treatments, toxicity, and survival data were collected for patients treated with RT at Mayo Clinic from 2007 to 2016. Standardized costs were obtained and adjusted for inflation. TSs were identified as those with greater than 93rd percentile costs (≥ $120,812). Prediction models were developed to predict TSs using training and validation sets using information available at consultation, after RT, and at last follow-up. A total of 15,131 patients were included and 1,065 TSs identified. Mean cost overall was $55,290 (median, $39,996) for all patients. Prediction models 1, 2, and 3 had concordance statistics of 0.83 to 0.83, 0.85 to 0.85, and 0.87 to 0.88, respectively in training and validation, indicating excellent prediction of TSs. Factors that were most predictive of TSs included stage N/A and stage 4 (v stage 0; odds ratio [OR], 18.23 and 8.44, respectively; P < .001); hematologic, upper GI, skin and lung cancers (v breast; OR, 11.45, 7.69, 3.81, and 2.43, respectively; P < .01); immunotherapy, surgery, and chemotherapy use (OR, 4.36, 2.51, and 1.61, respectively; P < .01); hospitalizations within 90 days of RT (OR, 2.26; P < .01); or death during the episode (OR, 1.56; P < .01). This is the first study of its kind to predict with high accuracy the highest spenders in radiation oncology. These patients may benefit from pre-emptive management to mitigate costs, or may require exclusion or adjustment from quality payment programs.