Physician cost profiling--reliability and risk of misclassification

Abstract

Background: Insurance products with incentives for patients to choose physicians classified as offering lower-cost care on the basis of cost-profiling tools are increasingly common. However, no rigorous evaluation has been undertaken to determine whether these tools can accurately distinguish higher-cost physicians from lower-cost physicians.

Methods: We aggregated claims data for the years 2004 and 2005 from four health plans in Massachusetts. We used commercial software to construct clinically homogeneous episodes of care (e.g., treatment of diabetes, heart attack, or urinary tract infection), assigned each episode to a physician, and created a summary profile of resource use (i.e., cost) for each physician on the basis of all assigned episodes. We estimated the reliability (signal-to-noise ratio) of each physician's cost-profile score on a scale of 0 to 1, with 0 indicating that all differences in physicians' cost profiles are due to a lack of precision in the measure (noise) and 1 indicating that all differences are due to real variation in costs of services (signal). We used the reliability results to estimate the proportion of physicians in each specialty whose cost performance would be classified inaccurately in a two-tiered insurance product in which the physicians with cost profiles in the lowest quartile were labeled as "lower cost."

Results: Median reliabilities ranged from 0.05 for vascular surgery to 0.79 for gastroenterology and otolaryngology. Overall, 59% of physicians had cost-profile scores with reliabilities of less than 0.70, a commonly used marker of suboptimal reliability. Using our reliability results, we estimated that 22% of physicians would be misclassified in a two-tiered system.

Conclusions: Current methods for profiling physicians with respect to costs of services may produce misleading results.

Figure 1. Relationship between the Number of Episodes Assigned and the Reliability of a Physician’s Cost-Profile Score

Each data point represents an individual physician.