Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease (Dec 2023)

Physician Variation and the Impact of Payment Model in Cardiac Imaging

  • Amity E. Quinn,
  • Derek S. Chew,
  • Peter Faris,
  • Flora Au,
  • Matthew T. James,
  • Marcello Tonelli,
  • Braden J. Manns

DOI
https://doi.org/10.1161/JAHA.122.029149
Journal volume & issue
Vol. 12, no. 24

Abstract

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Background The influence of fee‐for‐service reimbursement on cardiac imaging has not been compared with other payment models. Furthermore, variation in ordering practices is not well understood. Methods and Results This retrospective, population‐based cohort study using linked administrative data from Alberta, Canada included adults with chronic heart disease (atrial fibrillation, coronary artery disease, and heart failure) seen by cardiac specialists for a new outpatient consultation April 2012 to December 2018. Generalized linear mixed‐effects models estimated the association of payment model (including the ability to bill to interpret imaging tests) and the use of cardiac imaging and quantified variation in cardiac imaging. Among 31 685 adults seen by 308 physicians at 136 sites, patients received an observed mean of 0.67 (95% CI, 0.67–0.68) imaging tests per consultation. After adjustment, patients seeing fee‐for‐service physicians had 2.07 (95% CI, 1.68–2.54) and fee‐for‐service physicians with ability to interpret had 2.87 (95% CI, 2.16–3.81) times the rate of receiving a test than those seeing salaried physicians. Measured patient, physician, and site effects accounted for 31% of imaging variation and, following adjustment, reduced unexplained site‐level variation 40% and physician‐level variation 29%. Conclusions We identified substantial variation in the use of outpatient cardiac imaging related to physician and site factors. Physician payment models have a significant association with imaging use. Our results raise concern that payment models may influence cardiac imaging practice. Similar methods could be applied to identify the source and magnitude of variation in other health care processes and outcomes.

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