Prevalence of "One-Off Events" in Radiology: Implications for Radiology in Episode-Based Alternative Payment Models.

Curr Probl Diagn Radiol

American College of Radiology Board of Chancellors, Reston, VA; Hackensack Radiology Group, PA, River Edge, NJ; American College of Radiology Commission on Economics, Reston, VA.

Published: December 2023

Objective: As reimbursement mechanisms become more value-based, there are questions about the applicability of these mechanisms for nonepisodic care, particularly care provided by nonpatient-facing specialists, for example, radiologists. Accordingly, this study examined the prevalence of nonepisodic care-one-off events-in diagnostic radiology.

Methods: We conducted a multiyear (2015-2019) retrospective study of diagnostic imaging using a large commercial payer database including commercial insurance and Medicare Advantage. Using a 12-month evaluation period starting with the day of the initial imaging study/studies, we categorized imaging studies as one-off events if there were no additional studies (beyond the first day of the evaluation period) for the next 12 months in the same body region. We also evaluated an alternative, more stringent definition of a one-off event: the only imaging study during the 12-month evaluation period. We computed the percentage of one-off events overall and by body region.

Results: We found that one-off events comprised 33.2%-45.8% of imaging studies depending on whether one-off events are defined as the only study in the evaluation period or imaging only on the first day of the evaluation period, respectively. This share varied widely by body region: highest for cardiac (80.9%-87.7%) and lower for chest (26.8%-35.2%). By place-of-service, the proportion was lowest for the inpatient (12.9%-29.1%) and long-term care settings (18.6%-30%).

Discussion: Given the sizeable share of imaging studies categorized as one-off events, much of radiologists' workload falls outside of the framework of episodic measurement tools and value-based payment models.

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Source
http://dx.doi.org/10.1067/j.cpradiol.2023.08.009DOI Listing

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