Producing Quality Adjusted Hospital Price Indexes

Tuesday, June 25, 2019: 8:00 AM
Truman - Mezzanine Level (Marriott Wardman Park Hotel)

Presenter: Brett Matsumoto

Discussant: John Graves

This paper develops a feasible methodology for quality adjusting the hospital price indexes using the hospital quality measures published by the Centers for Medicare and Medicaid Services (CMS). The Department of Health and Human Services collects quality information from hospitals and produces a number of quality measures that are publicly available through its Hospital Compare project. The purpose of providing these quality measures is to increase transparency and accountability in the healthcare system. If a hospital increases its quality and prices, the value of the change in quality must be removed from the change in price to get the pure price change. Failing to fully account for generally improving quality in the medical sector means that the official price indexes likely overstate the true level of medical inflation.

One method of quality adjusting uses the cost to the producer of the change in quality. The cost-based quality adjustment method is the preferred method of the Producer Price Index (PPI). I implement a cost-based quality adjustment procedure to the hospital PPI. First, I estimate the causal relationship between hospital costs and the quality measures. The causal relationship is identified using the instrumental variable technique of Doyle, Graves, Gruber, and Kleiner (JPE 2015). They develop an instrument for hospital selection based on plausibly exogenous assignment to different ambulance companies (which have different preferences for hospitals). Then, I construct a quality adjusted hospital PPI using the historical microdata using the estimated relationship between costs and the hospital’s quality scores.

This paper uses Medicare claims data for the years 2010-2015 to estimate the relationship between costs and quality measures. The data include inpatient hospitalizations linked to ambulance billing data. The claims data are linked to hospital specific cost (hospital cost reports) and quality data (Hospital Compare). Preliminary results suggest a positive relationship between cost and quality. The effect is largest and most significant for the measures of patient satisfaction. A one standard deviation increase in the patient satisfaction measures is associated with 6 percent higher costs. Since the quality measures are increasing over this period, the quality adjustment leads to a lower level of hospital inflation.