Association for Public Policy Analysis & Management

There is growing policy interest in the use of health care price transparency as a tool to address high health care prices. This study examines the results of a randomized experiment on providing market-wide price transparency tool for a select set of procedures. While most other studies on the impact of price transparency utilize a difference-in-difference or quasi-experimental research design, which are susceptible to confounders, this study offers evidence of price transparency from a randomized controlled trial.

In September 2017, FAIR Health released its New York Healthcare Online Shopping Tool (NY HOST), an independent, publicly-accessible state-wide consumer shopping tool. NY HOST displays benchmarks for insurers’ allowed amounts and providers’ billed charges for each 3-digit geozip (areas designated by the first three digits of a zipcode) by procedure for a range of values including benchmarks for the average and the 50th to 95th percentiles’ billed charges. The site also provides educational resources. The rollout was accompanied by an extensive, multi-pronged marketing effort. We embedded a randomized experiment in NY HOST – individual provider billed charges (list prices) were randomized across New York State’s 3-digit geozips for a set of common procedures.

We then used a unique, comprehensive dataset of claims drawn from the FAIR Health database, which includes about 75% of all private insurance claims across the country, to analyze the effects of this randomized experiment on billed charges. The claims dataset encompassed all claims for 104 procedures which were selected from the database based on high frequency of utilization across a segment of specialties in New York State from 2016 through the second quarter of 2019. The dataset encompassed approximately 115 million claims, over 200,000 unique National Provider Identifiers (NPI), and multiple private insurers. We constructed a longitudinal dataset to assess the trends in providers’ billed charges by quarter for the specified time period.

We assessed the impact of the September 2017 release of the transparency tool on providers’ billed charges in the subsequent time period using regression models that controlled for variations over time in different geographic locations for different procedures. We assessed the overall effect of the treatment (the release of provider-level price information) as well as treatment effect heterogeneity by examining the impact of the experiment on billed charges by high vs. low cost providers, by high vs. low volume providers, by out-of-network utilization, by market concentration, by charge dispersion, by procedure category, and across quintiles. Utilizing a difference-in-differences framework, we found that the charge transparency tool increased charges on average in the markets where provider-level billed charges were revealed.

We conducted a longitudinal study to analyze the impact of a market-wide transparency tool on providers’ billed charges over time. The release of provider-level billed charges through the launch of a market-wide transparency tool altered providers’ billed charges, with the impact differentiated by provider and market characteristics.

Recent federal and state efforts to facilitate price transparency by hospitals and other health care providers highlight the need to understand the impact of transparency on pricing and affordability.