Targeted Quality Measurement of Health Care Providers

Measuring quality of cancer care delivered by US health providers is challenging. Patients receiving oncology care greatly vary in disease presentation among other key characteristics. In this paper we discuss a framework for institutional quality measurement which addresses the heterogeneity of patient populations. For this, we follow recent statistical developments on health outcomes research and conceptualize the task of quality measurement as a causal inference problem, helping to target flexible covariate profiles that can represent specific populations of interest. To our knowledge, such covariate profiles have not been used in the quality measurement literature. We use different clinically relevant covariate profiles and evaluate methods for layered case-mix adjustments that combine weighting and regression modeling approaches in a sequential manner in order to reduce model extrapolation and allow for provider effect modification. We appraise these methods in an extensive simulation study and highlight the practical utility of weighting methods that warn the investigator when case-mix adjustments are infeasible without some form of extrapolation that goes beyond the support of the data. In a study of cancer-care outcomes, we assess the performance of oncology practices for different profiles that correspond to the types of patients who may receive cancer care. We describe how the methods examined may be particularly important for high-stakes quality measurement, such as public reporting or performance-based payments. These methods may also be applied to support the health care decisions of individual patients and provide a path to personalized quality measurement.