The Use of Individual Provider Performance Reports by US Hospitals

Reimbursement for hospitals and physicians is increasingly tied to performance.¹ Bundled payments, for example, require hospitals to share risk for patient outcomes. Medicare bundled payments are becoming mandatory for some surgical and medical conditions, including joint replacement, acute myocardial infarction, and coronary artery bypass graft surgery.² Value-based payment is anticipated to become the norm as Medicare and private payers strive to control costs and improve outcomes. Although value-based reimbursement for hospitals targets hospital-level costs and outcomes, we know that variations at the level of individual providers explain a considerable proportion of variation in utilization and outcomes.³ However, physicians often lack awareness of their own practice patterns and relative costs, and successful participation in new payment models may require an investment by hospitals in the infrastructure needed to measure and provide feedback on performance to individual providers to affect their behavior.^4,5

Electronic health record (EHR)-based reports or “dashboards” have been proposed as one potential tool to provide individualized feedback on provider performance.⁶ Individual provider performance profiles (IPPs) offer the potential to provide peer comparisons that may adjust individual behavior by correcting misperceptions about norms.⁷ Behavioral economic theory suggests that individual performance data, if combined with information on peer behavior and normative goals, may be effective in changing behavior.⁸ Several studies have reported the effects of specific efforts to use IPPs, showing that such reports can improve care in certain clinical areas. For example, individual provider dashboards have been associated with better outcomes for hospitalized patients, such as increased compliance with recommendations for prophylaxis of venous thromboembolism, although evidence in other areas of practice is mixed.^9,10 A randomized controlled trial of peer comparison feedback reduced inappropriate antibiotic prescribing for upper respiratory infections by 11% among internists.¹¹

Despite the promise of individualized feedback to optimize behavior, however, little has been reported on trends in the use of IPPs on a population level. It is unknown whether their use is common or rare, or what hospital characteristics are associated with adoption. Such information would help guide future efforts to promote IPP use and understand its effect on practice. We used data from a nationally representative survey of US hospitals to examine the use of individual provider-level performance profiles.

We used data from the American Hospital Association (AHA) Annual Survey Information Technology (IT) Supplement, which asked respondents to indicate whether they have used electronic clinical data from the EHR or other electronic system in their hospital to create IPPs. The AHA survey is sent annually to all US operating hospitals. Survey results are supplemented by data from the AHA registration database, US Census Bureau, hospital accrediting bodies, and other organizations. The AHA IT supplement is also sent yearly to each hospital’s chief executive officer, who assigns it to the most knowledgeable person in the institution to complete.¹²

We linked data on IPP use to AHA Annual Survey responses on hospital characteristics for all general adult and pediatric hospitals. Multivariable logistic regression was used to model the odds of individual provider performance profile use as a function of hospital characteristics, including ownership (nonprofit, for profit, or government), geographic region, teaching versus nonteaching status, rural versus urban location, size, expenditures per bed, proportion of patient days covered by Medicaid, and risk-sharing models of reimbursement (participation in a health maintenance organization or bundled payments program). Variables were chosen a priori to account for important characteristics of US hospitals (eg, size, teaching status, and geographic location). These were combined with variables representing risk-sharing arrangements based on the hypothesis that hospitals whose payments are at greater risk would be more likely to invest in tracking provider performance. We eliminated any variable with an item nonresponse rate greater than 15%, which resulted in elimination of 2 variables representing hospital revenue from capitated payments and any risk-sharing arrangement, respectively. All other variables had item nonresponse rates of 0%, except for 4.7% item nonresponse for the bundled payments variable.

We also measured the trend in individual provider performance report use between 2013 and 2015 by estimating the linear probability between IPP use and year. A P value less than .05 was considered statistically significant.

Because past work has demonstrated nonresponse bias in the AHA Survey and IT Supplement, we performed additional analyses using nonresponsive weights based on hospital characteristics. Weighting methodology was based on prior work with the AHA and AHA IT surveys.^13,14 Weighting exploits the fact that a number of hospital characteristics are derived from sources outside the survey and thus are available for both respondents and nonrespondents. We created nonresponse weights based on a logistic regression model of survey response as a function of hospital characteristics (ownership, size, teaching status, systems membership, critical access hospital, and geographic region). Our findings were similar for weighted and nonweighted models and nonweighted estimates are presented throughout.

The University of Pennsylvania Institutional Review Board exempted this study from review. Analyses were performed using Stata statistical software, version 14.0 (StataCorp, College Station, TX).