Timothy P Copeland1, John M Hillman2, Benjamin L Franc3. 1. Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California. 2. Decision Support Services, UCSF Medical Center, San Francisco, California. 3. Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California. Electronic address: Benjamin.Franc@ucsf.edu.
Abstract
PURPOSE: The aim of this study was to assess the effect of spending patterns during the final year of life on high-cost imaging utilization in the final 3 months of life. METHODS: An academic comprehensive cancer center's radiology, cancer registry, and claims records were matched to identify decedents with dates of death from April 2013 through June 2014. Spending patterns in the final year of life were identified using group-based trajectory modeling. Descriptive analysis of CT, MRI, and PET utilization across trajectories was conducted. Multivariate logistic regressions modeled the likelihood of imaging utilization in the final 3 months of life, and a sensitivity analysis assessed the impact of spending trajectories on model fit. RESULTS: Six spending trajectories were identified. Membership in the late rising trajectory was the strongest predictor of high-cost imaging in the final 3 months of life (odds ratio, 11.61; P = .000), followed by diagnosis 12 to 6 months premortem (odds ratio, 7.49; P = .000). The likelihood of imaging the final 3 months of life was no different between high persistent and low persistent trajectory patients, despite the heterogeneity between the two patient groups. Sensitivity analysis indicated that spending trajectory improved the prediction of imaging in the final 3 months of life to a greater extent than temporal proximity to death at the time of diagnosis, which may serve as a proxy for severity and/or complexity. CONCLUSIONS: Clinical measures of severity and patients' utilization histories should be considered by hospital administrators in estimations of aggregate and individual oncologic imaging utilization. This analytic approach may aid in evaluating participation in advanced payment models.
PURPOSE: The aim of this study was to assess the effect of spending patterns during the final year of life on high-cost imaging utilization in the final 3 months of life. METHODS: An academic comprehensive cancer center's radiology, cancer registry, and claims records were matched to identify decedents with dates of death from April 2013 through June 2014. Spending patterns in the final year of life were identified using group-based trajectory modeling. Descriptive analysis of CT, MRI, and PET utilization across trajectories was conducted. Multivariate logistic regressions modeled the likelihood of imaging utilization in the final 3 months of life, and a sensitivity analysis assessed the impact of spending trajectories on model fit. RESULTS: Six spending trajectories were identified. Membership in the late rising trajectory was the strongest predictor of high-cost imaging in the final 3 months of life (odds ratio, 11.61; P = .000), followed by diagnosis 12 to 6 months premortem (odds ratio, 7.49; P = .000). The likelihood of imaging the final 3 months of life was no different between high persistent and low persistent trajectory patients, despite the heterogeneity between the two patient groups. Sensitivity analysis indicated that spending trajectory improved the prediction of imaging in the final 3 months of life to a greater extent than temporal proximity to death at the time of diagnosis, which may serve as a proxy for severity and/or complexity. CONCLUSIONS: Clinical measures of severity and patients' utilization histories should be considered by hospital administrators in estimations of aggregate and individual oncologic imaging utilization. This analytic approach may aid in evaluating participation in advanced payment models.
Authors: Kesav Raghavan; Timothy P Copeland; Michael Rabow; Maya Ladenheim; Angela Marks; Steven Z Pantilat; David O'Riordan; David Seidenwurm; Benjamin Franc Journal: BMJ Support Palliat Care Date: 2019-03-01 Impact factor: 3.568