Purpose: Prior authorization (PA) imposes significant time burdens on radiation oncology practices, but its financial impact has not been characterized. We used time-driven activity-based costing (TDABC) to assess the cost burden of treatment-related PA events at an academic radiation oncology practice. We then estimated annual costs for an academic practice and academic practices nationally.
Methods And Materials: Using internal analyses, we created TDABC process maps for treatment-related PA events at an academic radiation oncology practice. Using published compensation data, internal workhour estimates, and supervisory requirements, we calculated the cost of each PA event and annual costs. Using data from the 2017 American Society for Radiation Oncology Workforce Survey and the 2018 American Society for Radiation Oncology Prior Authorization Survey, we estimated annual PA costs for academic medical centers nationally.
Results: We successfully created TDABC process maps for treatment-related PA events at an academic radiation oncology practice. There were significant time and cost burdens for all events (range: 51-95 minutes, $28-$101 US dollars [USD]), with significant increases when peer-to-peer discussion was required (range: 92-95 minutes, $75-$101 USD). Annual treatment-related PA departmental costs were estimated to be $491,989 USD, with approved treatments accounting for the majority (94%; $463,027 USD). Nationally, annual treatment-related PA costs were estimated to be $40,125,848 USD, with approved treatments accounting for the majority (86%; $34,632,620 USD).
Conclusion: TDABC can be used to estimate the cost burden of PA events. These burdens are significant and translate into massive organizational costs. Our national estimates highlight the tremendous cost of PA for academic radiation oncology practices, with the majority of costs related to approved treatments.
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http://dx.doi.org/10.1200/OP.21.00644 | DOI Listing |
Radiat Oncol
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Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
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Shanghai Artificial Intelligence Research Institute Co., Ltd., Shanghai, 200240, China.
Academic data processing is crucial in scientometrics and bibliometrics, such as research trending analysis and citation recommendation. Existing datasets in this domain have predominantly concentrated on textual data, overlooking the importance of visual elements. To bridge this gap, we introduce a multidisciplinary multimodal aligned dataset (MMAD) specifically designed for academic data processing.
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