Leveraging Insurer-Provided Health Care Price Data to Inform State Policy Solutions

Driven mainly by high and variable prices, United States’ health care spending is among the highest in the world. Yet, higher prices make health care less affordable and are often not linked to higher quality. Because commercial prices for health care services are determined through an opaque negotiation process between providers and insurers, there is currently a lack of health care price transparency. This hinders price comparison by patients, benefit design by employers and purchasers, and policy reform by policymakers and regulators.

Historically, price transparency data have existed in aggregate formats through medical claims data, which are costly and difficult to obtain. Moreover, these data do not allow for provider or insurer identification, limiting direct price comparisons. A novel dataset, Transparency in Coverage (TiC) data, may help fill these gaps. The TiC data was mandated by the 2020 TiC Executive Order, which required most health plans and insurers to disclose negotiated prices for covered items and services in machine-readable file formats. However, file size and format have commonly prevented use of these files, leading to policy efforts aimed at simplifying the data.

Analysis Finds Price Variability

In a recent study, researchers at the Center for Advancing Health Policy through Research (CAHPR) at the Brown University School of Public Health, along with researchers from Cornell University, Indiana University, and Bentley University, linked TiC data with claims-based data from approximately 270 million patients to enable price comparison analysis at the level of specific providers and insurers.

By using third-party data from Clarify Health, which aggregates and cleans up errors in TiC data, our team was able to demonstrate the utility of TiC data, publishing our findings earlier this year in Health Affairs Scholar. We found substantial price variation across insurers and geographies as well as across types of services, illustrating that substantive commercial price variation is not restricted to a certain specialty.

Variation across insurers and inpatient and outpatient facilities. Our team restricted TiC prices to providers who billed for each applicable procedure to five insurers: Blue Cross Blue Shield, UnitedHealth Group, Cigna, Aetna, and Humana. Confirming previous findings by other researchers, the analysis found significant price variation across different insurers, with prices for common procedures varying by 50% to over 200%, and even larger price variation for outpatient services. Price variation was also found within insurers for inpatient and outpatient facility services, with only a 22% correlation between prices for inpatient and outpatient settings within the same insurer. Prices also varied dramatically by state, even when accounting for differences in insurance and procedures.

Greater variation in facility than in professional prices for specialty services. Using TiC data, CAHPR researchers have also explored commercial price variation across a number of specialties. Studying prices for 10 common services in general surgery from four large insurers (BlueCross BlueShield, UnitedHealthcare, Cigna, and Aetna), we found price variation was lower for professional prices (prices charged by providers for their work during a procedure) than facility prices (prices charged by the facility itself for its use). Facility prices were roughly nine times higher than professional prices for surgical procedures. We also found substantial geographic and insurer price variation.

Variation by state and payer for emergency department (ED) services. Likewise, using January 2025 TiC data for the same four insurers as above, we found significant price variation among states and payers for ED evaluation and management services. Certain states (like Wisconsin or California) had higher than average professional prices—a finding that may be due to physician practices with more leverage or Medicare payment adjustments based on geographic marketplace.

Variation for imaging. In another study, utilizing 2023 contract year TiC data for 30 common imaging studies (broadly categorized as either CT scan, mammogram, MRI, ultrasound, or X-ray studies), CAHPR researchers analyzed commercial prices (excluding Medicare Advantage and Medicaid managed care plans) for providers with billed claims. Despite the standardization of imaging procedures, which should correlate with more consistent pricing structures in theory, we still observed substantial commercial insurance price variation. Greater variation was again found among facility fees than professional fees. Significant price variation was also found across states and payers. For example, Blue Cross Blue Shield had higher payments across all imaging studies, while Cigna and Aetna mostly had payments lower than the mean.

Though sources of price variation were not identified in these studies, previous work by CAHPR has linked hospital and private equity affiliation to price variation. For example, compared to prices for independent primary care physicians, negotiated prices were 10.7% higher for office visits at hospital-affiliated primary care physicians, and 7.8% higher for private equity-affiliated primary care physicians. Private equity acquisition of physician practices has also been linked to increased prices in specialties such as dermatology, gastroenterology, and ophthalmology. Apart from consolidation, price variation may also reflect clinical or perceived quality variation.

Key Takeaways for State Regulators and Purchasers

Findings from TiC data can inform the design of more robust consumer-facing price transparency tools to reduce costs and improve quality, as well as guide their health care purchaser strategy.

Transparency tools such as those in Wisconsin (PricePoint) and Massachusetts (MyHealthCareOptions) have also helped consumers to compare cost and/or quality among providers. Patient uptake of these tools has been modest, however. In addition, these tools require aggregation of complex and incomplete medical claims data. Publicly available and comprehensive TiC data can enhance these patient-facing tools.

In addition to such patient-facing tools, dozens of robust transparency policies have been implemented by states. For example, Colorado recently enacted a bill requiring health insurance carriers to biannually report to state regulators the TiC data they are already required to submit to the federal government. Meanwhile, Texas has adopted transparency requirements for health plans not required to report under federal rules (such as short-term limited duration health plans), alongside issuing standardized reporting guidance to improve data usability.

Transparency is also of high importance to state employee health plans, which have utilized data to create innovative programs to control health care spending. For example, The California Public Employees’ Retirement System (CalPERS) incentivized patients to choose cost-effective providers, improving quality and reducing spending. In addition, Oregon capped reimbursements by its state employee health plan to hospitals at 200% of Medicare rates, reducing annual spending for the state employee plan by 4%. Washington adopted a similar cap this year, and CAHPR researchers have estimated that this policy would save the average state $150 million per year. For states seeking to establish such reference-based price caps, TiC data offers an additional source of information to guide the decision of where to establish these price caps.

While TiC data is already being used to study variation in health care prices, it has the potential to be an even more impactful tool for policymakers and health care purchasers. State policymakers can adopt policies to strengthen transparency data and use it to make meaningful market reforms to control health care spending.