PURPOSE: To quantify interphysician variation in imaging use during emergency department (ED) visits and examine the contribution of factors to this variation at the patient, visit, and physician level. MATERIALS AND METHODS: This study was HIPAA compliant and approved by the institutional review board of Partners Healthcare System (Boston, Mass), with waiver of informed consent. In this retrospective study of 88 851 consecutive ED visits during 2011 at a large urban teaching hospital, a hierarchical logistic regression model was used to identify multiple predictors for the probability that low- or high-cost imaging would be ordered during a given visit. Physician-specific random effects were estimated to articulate (by odds ratio) and quantify (by intraclass correlation coefficient [ICC]) interphysician variation. RESULTS: Patient- and visit-level factors found to be statistically significant predictors of imaging use included measures of ED busyness, prior ED visit, referral source to the ED, and ED arrival mode. Physician-level factors (eg, sex, years since graduation, annual workload, and residency training) did not correlate with imaging use. The remaining amount of interphysician variation was very low (ICC, 0.97% for low-cost imaging; ICC, 1.07% for high-cost imaging). These physician-specific odds ratios of imaging estimates were moderately reliable at 0.78 (95% confidence interval [CI]: 0.77, 0.79) for low-cost imaging and 0.76 (95% CI: 0.74, 0.78) for high-cost imaging. CONCLUSION: After careful and comprehensive case-mix adjustment by using hierarchical logistic regression, only about 1% of the variability in ED imaging utilization was attributable to physicians.
PURPOSE: To quantify interphysician variation in imaging use during emergency department (ED) visits and examine the contribution of factors to this variation at the patient, visit, and physician level. MATERIALS AND METHODS: This study was HIPAA compliant and approved by the institutional review board of Partners Healthcare System (Boston, Mass), with waiver of informed consent. In this retrospective study of 88 851 consecutive ED visits during 2011 at a large urban teaching hospital, a hierarchical logistic regression model was used to identify multiple predictors for the probability that low- or high-cost imaging would be ordered during a given visit. Physician-specific random effects were estimated to articulate (by odds ratio) and quantify (by intraclass correlation coefficient [ICC]) interphysician variation. RESULTS:Patient- and visit-level factors found to be statistically significant predictors of imaging use included measures of ED busyness, prior ED visit, referral source to the ED, and ED arrival mode. Physician-level factors (eg, sex, years since graduation, annual workload, and residency training) did not correlate with imaging use. The remaining amount of interphysician variation was very low (ICC, 0.97% for low-cost imaging; ICC, 1.07% for high-cost imaging). These physician-specific odds ratios of imaging estimates were moderately reliable at 0.78 (95% confidence interval [CI]: 0.77, 0.79) for low-cost imaging and 0.76 (95% CI: 0.74, 0.78) for high-cost imaging. CONCLUSION: After careful and comprehensive case-mix adjustment by using hierarchical logistic regression, only about 1% of the variability in ED imaging utilization was attributable to physicians.
Authors: Tyler W Barrett; Kristin L Rising; M Fernanda Bellolio; M Kennedy Hall; Aaron Brody; Kenneth W Dodd; Mira Grieser; Phillip D Levy; Ali S Raja; Wesley H Self; Gail Weingarten; Erik P Hess; Judd E Hollander Journal: Acad Emerg Med Date: 2016-11-25 Impact factor: 3.451
Authors: Edward R Melnick; Elizabeth G J O'Brien; Olga Kovalerchik; William Fleischman; Arjun K Venkatesh; R Andrew Taylor Journal: Acad Emerg Med Date: 2016-08-01 Impact factor: 3.451
Authors: Jason H Wasfy; Michael K Hidrue; Robert W Yeh; Katrina Armstrong; G William Dec; Eugene V Pomerantsev; Michael A Fifer; Timothy G Ferris Journal: J Am Heart Assoc Date: 2015-10-16 Impact factor: 5.501
Authors: M Fernanda Bellolio; Shawna D Bellew; Lindsey R Sangaralingham; Ronna L Campbell; Daniel Cabrera; Molly M Jeffery; Nilay D Shah; Erik P Hess Journal: BMC Health Serv Res Date: 2018-03-02 Impact factor: 2.655
Authors: Nóra Kovács; Anita Pálinkás; Valéria Sipos; Attila Nagy; Nouh Harsha; László Kőrösi; Magor Papp; Róza Ádány; Orsolya Varga; János Sándor Journal: Int J Environ Res Public Health Date: 2019-08-29 Impact factor: 3.390
Authors: Nóra Kovács; Orsolya Varga; Attila Nagy; Anita Pálinkás; Valéria Sipos; László Kőrösi; Róza Ádány; János Sándor Journal: BMJ Open Date: 2019-09-06 Impact factor: 2.692