Using Patient-Specific Quality Information to Unlock Hidden Healthcare Capabilities

Problem definition: We address two key questions faced by patients, physicians, and policy makers: (1) How do different types of patients benefit differently from elite surgeons? and (2) How can outcome data be used to achieve better healthcare at both the individual and social levels? Academic/practical relevance: Existing healthcare provider quality information, which is almost exclusively based on population averages, cannot detect differences in the surgeon effect for patients with different characteristics or medical conditions. Consequently, such information may under-state or over-state the benefit to an individual patient of seeking out an elite physician. This paper develops methods to generate patient-specific quality information and analyzes the impact on enabling patients to receive better care. Methodology: Using mitral valve surgery as the clinical setting, we study the quality of 188 cardiac surgeons at 35 hospitals in New York State with respect to different quality metrics, including a new quality-adjusted life expectancy (QALE) metric that combines many of the short-term and long-term effects of traditional metrics. By combining a patient choice model that considers outcome quality, travel distance, and waiting time on the surgical schedule with a queueing model of patient waiting time, we translate our empirical results into estimates of the relative value of population-average and patient-specific quality information for improving overall patient outcomes. We formulate patients' choice of surgeons as a queueing system to compare the value of using population-average and patient-specific quality information to help patients find better care. Results: We observe that patients of different demographics and levels of acuity benefit differently from elite surgeons. We estimate that the societal benefits (i.e., the total patient utility) from using patient-specific information are comparable to those achievable by enabling the best surgeons to treat 10%-20% more patients under population-average information. Managerial implications: Patient-specific quality information, which calibrates outcome statistics by patient demographics and acuity, allows patients to obtain better healthcare, physicians to make better referrals, administrators to better focus improvement initiatives, and payers to better align incentives.