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Purpose To quantify the effect of a comprehensive, long-term, provider-led utilization management (UM) program on high-cost imaging (computed tomography, magnetic resonance imaging, nuclear imaging, and positron emission tomography) performed on an outpatient basis. Materials and Methods This retrospective, 7-year cohort study included all patients regularly seen by primary care physicians (PCPs) at an urban academic medical center. The main outcome was the number of outpatient high-cost imaging examinations per patient per year ordered by the patient's PCP or by any specialist. The authors determined the probability of a patient undergoing any high-cost imaging procedure during a study year and the number of examinations per patient per year (intensity) in patients who underwent high-cost imaging. Risk-adjusted hierarchical models were used to directly quantify the physician component of variation in probability and intensity of high-cost imaging use, and clinicians were provided with regular comparative feedback on the basis of the results. Observed trends in high-cost imaging use and provider variation were compared with the same measures for outpatient laboratory studies because laboratory use was not subject to UM during this period. Finally, per-member per-year high-cost imaging use data were compared with statewide high-cost imaging use data from a major private payer on the basis of the same claim set. Results The patient cohort steadily increased in size from 88 959 in 2007 to 109 823 in 2013. Overall high-cost imaging utilization went from 0.43 examinations per year in 2007 to 0.34 examinations per year in 2013, a decrease of 21.33% (P < .0001). At the same time, similarly adjusted routine laboratory study utilization decreased by less than half that rate (9.4%, P < .0001). On the basis of unadjusted data, outpatient high-cost imaging utilization in this cohort decreased 28%, compared with a 20% decrease in statewide utilization (P = .0023). Conclusion Analysis of high-cost imaging utilization in a stable cohort of patients cared for by PCPs during a 7-year period showed that comprehensive UM can produce a significant and sustained reduction in risk-adjusted per-patient year outpatient high-cost imaging volume. © RSNA, 2017.

Purpose: To determine the relevant physician- and practice-related factors that jointly affect the rate of low-utility imaging examinations (score of 1-3 out of 9) ordered by means of an order entry system that provides normative appropriateness feedback., Materials and Methods: This HIPAA-compliant study was approved by the institutional review board under an expedited protocol for analyzing anonymous aggregated administrative data. This is a retrospective study of approximately 250 000 consecutive scheduled outpatient advanced imaging examinations (computed tomography, magnetic resonance imaging, nuclear medicine) ordered by 164 primary care and 379 medical specialty physicians from 2008 to 2012. A hierarchical logistic regression model was used to identify multiple predictors of the probability that an examination received a low utility score. Physician- and practice-specific random effects were estimated to articulate (odds ratio) and quantify (intraclass correlation) interphysician variation., Results: Fixed effects found to be statistically significant predictors of low-utility imaging included examination type, whether the examination was cancelled, status of the person entering the order, and the total number of examinations ordered by the clinician. Neither patient age nor sex had any effect, and there were no secular trends (year of study). The remaining amount of interphysician variation was moderate (intraclass correlation, 22%), whereas the variation between medical specialties and primary care practices was low (intraclass correlation, 5%). The estimated physician-specific effects had reliability of 70%, which makes them just suitable for identifying outliers., Conclusion: The authors found that 22% of the variation in the rate of low-utility examinations is attributable to ordering providers and 5% to their specialty or clinic., ((©) RSNA, 2014.)

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 determine the effect of a computerized radiology order entry system rule that prevented nonclinician support staff from completing orders for outpatient computed tomographic, magnetic resonance imaging, and nuclear medicine examinations that received initial low-yield decision support scores in the order entry system., Materials and Methods: This retrospective HIPAA-compliant study was approved by the institutional review board; the requirement for informed consent was waived. The control group consisted of 42737 consecutive orders for examinations in which decision support was provided that were placed from April to December 2006. The study group consisted of 76238 consecutive orders that were placed from April to December 2007. During the latter time period, a new rule in the order entry system was implemented: Examinations that had low-yield decision support scores could not be scheduled when the orders were placed by nonclinician support staff. To schedule the blocked examinations, the responsible clinician was required to personally log in to complete the process. System event logs and records of outpatient imaging procedures were extracted, counted, and analyzed to determine which ordering sessions resulted in examinations being scheduled and performed and which sessions resulted in modified or cancelled examinations. Results were correlated with user status and decision support scores. The Cochran-Mantel-Haenszel technique was used to control for the status of the order initiator and to allow testing for significance of the effect of the intervention on the "fate" of ordering events., Results: After the intervention, the proportion of total examination requests initiated by clinicians directly logging in almost doubled: from 11,243 (26.31%) of 4,737 to 41,450 (54.37%) of 76238 examinations (P < .001). The fraction of low-yield (decision support score, 1-3) examinations requested through the order entry system that were later scheduled and performed decreased from 2106 (5.43%) of 38,801 to 1261 (1.92%) of 65,765 (P < .001). This is in contrast to requests for examinations with higher initial decision support scores that were not affected by the policy change and were scheduled at the same rate (relative risk, 0.988) before and after the change., Conclusion: A simple change in the business logic of the order entry system resulted in a substantially decreased rate of low-yield imaging examinations and a markedly increased percentage of tests personally ordered by clinicians., (Copyright RSNA, 2010)

Purpose: To quantify the rates of recommendation for additional imaging (RAI) in a large number of radiology reports of different modalities and to estimate the effects of 11 clinically relevant factors., Materials and Methods: This HIPAA compliant research was approved by the institutional review board under an expedited protocol for analyzing anonymous aggregated radiology data. All diagnostic imaging examinations (n = 5 948 342) interpreted by radiologists between 1995 and 2008 were studied. A natural language processing technique specifically designed to extract information about any recommendations from radiology report texts was used. The analytic data set included three quantitative variables: the interpreting radiologist's experience, the year of study, and patient age. Categoric variables described patient location (inpatient, outpatient, emergency department), whether a resident dictated the case, patient sex, modality, body area studied, ordering service, radiologist's specialty division, and whether the examination result was positive. A multivariable logistic regression model was used to determine the effect of each of these factors on likelihood of RAI while holding all others equal., Results: Recommendations increased during the 13 years of study, with the unadjusted rate rising from roughly 6% to 12%. After accounting for all other factors, the odds of any one examination resulting in an RAI increased by 2.16 times (95% confidence interval: 2.12, 2.21) from 1995 to 2008. As radiologist experience increased, the odds of an RAI decreased by about 15% per decade. Studies that had positive findings were more likely (odds ratio = 5.03; 95% confidence interval: 4.98, 5.07) to have an RAI. The remaining factors also had significant effects on the tendency for an RAI., Conclusion: The likelihood of RAI increased by 15% for each decade of radiologist experience and roughly doubled over 13 years of study., ((c) RSNA, 2009.)

Purpose: To determine the effect of a computerized radiology order entry (ROE) and decision support (DS) system on growth rate of outpatient computed tomography (CT), magnetic resonance (MR) imaging, and ultrasonography (US) procedure volumes over time at a large metropolitan academic medical center., Materials and Methods: Institutional review board approval was obtained for this study of deidentified aggregate administrative data. The research was compliant with HIPAA; informed consent was waived. This was a retrospective study of outpatient advanced imaging utilization before, during, and after implementation of a Web-based ROE and DS system. Dependent variables were the quarterly volumes of outpatient CT, MR imaging, and US examinations from quarter 4 of 2000 through quarter 4 of 2007. Outpatient visits during each quarter were included as control variables. These data were analyzed as three separate time series with piecewise linear regression for simultaneous estimation of quarterly examination volume trends before and after ROE and DS system implementation. This procedure was repeated with log-transformed quarterly volumes to estimate percentage growth rates., Results: There was a significant decrease in CT volume growth (274 per quarter) and growth rate (2.75% per quarter) after ROE and DS system implementation (P < .001). For MR imaging, growth rate decreased significantly (1.2%, P = .016) after ROE and DS system implementation; however, there was no significant change in quarterly volume growth. With US, quarterly volume growth (n = 98, P = .014) and growth rate (1.3%, P = .001) decreased significantly after ROE implementation. These changes occurred during a steady growth in clinic visit volumes in the associated referral practices., Conclusion: Substantial decreases in the growth of outpatient CT and US procedure volume coincident with ROE implementation (supplemented by DS for CT) were observed. The utilization of outpatient MR imaging decreased less impressively, with only the rate of growth being significantly lower after interventions were in effect.