Physician profiling methods are envisioned as a means of promoting healthcare quality by recognizing the contributions of individual physicians. Developing methods that can reliably distinguish among physicians' performance is challenging because of small sample sizes, incomplete data, and physician panel differences. In this study, we developed a hierarchical, weighted composite model to reliably compare primary care physicians across domains of care, and we demonstrated its use within a clinical system. We evaluated 199 primary care physicians from a large integrated healthcare delivery system using 19 quality and two efficiency measures taken from the Healthcare Effectiveness Data and Information Set and existing pay-for-performance programs. Individual measures were calculated, compared to benchmarks, and grouped into two composites: one focused on quality and one on efficiency. Each composite was fitted to the model, assessed for reliability (signal-to-noise ratio), and weighted to create a single summary score for each primary care physician. The quality-of-care composite had a median reliability of .98, with 99.5% of all physician reliability estimates exceeding threshold. The efficiency composite had a median reliability of .97, with 94.9% of all physician reliability estimates exceeding threshold. Our results demonstrate that reliable physician profiling is possible across care domains using a hierarchical composite model based on multiple data. The model was used to distribute incentive payouts among primary care physicians but is adaptable to many settings.
Available at: http://works.bepress.com/sharon_johnson/10/