Health Care Price Transparency Data Is Needed To Enable Consumer Health Care Shopping

The recent Transparency in Coverage Rule marks a monumental shift in health care by requiring health insurance companies to disclose prices to the public. This pricing data is provided through machine-readable files containing the costs of different items and services for providers across the United States. Despite this significant development, the data is still not practically usable by consumers.

The first obstacle to actionable price transparency is the significant inconsistencies in the price transparency data. Despite the Centers for Medicare & Medicaid Services’ (CMS) standardized reporting formats, the price transparency data from insurance companies frequently contains vastly different negotiated rates for the exact same procedure, insurance plan, and provider. These discrepancies can be thousands of dollars for even the simplest billing codes with no clear reason. For example, an analysis by Certainly Health revealed that United Healthcare data contains different prices for a standard 30-minute physician visit (billing code 99213) for 46 percent of providers in New York.

Analysts have deduced that some rates implicitly vary based on different factors such as the patient’s diagnosis, or the type of provider rendering the service. But any context to explain the differences is completely opaque and undermines the reliability of the information. In the meantime, any application that leverages this data must resort to analyzing claims data to guess which rate is correct before surfacing it to the consumer. One simple proposal to solve this issue would be to mandate unique prices for every combination of provider and procedure code along with the necessary context to explain any variations.

Another significant challenge in leveraging this data is the lack of accessibility to detailed insurance information for patients. Insurance companies deploy black-box algorithms to automatically determine patient responsibility for a large segment of claims as part of a process called adjudication. In some cases, this process can incorrectly deny claims. Regardless, it’s extremely cumbersome, or not possible for even health care providers to confidently determine how insurance companies will calculate the patient’s out-of-pocket cost before submitting a claim.

To enable consumers to compare prices across providers, insurance companies should be required to provide public APIs to retrieve patient responsibility and eligibility rules given specific procedure codes. To minimize complexity and maximize confidence in the output, these requirements could start with simpler procedures and services where auto-adjudication is feasible. With access to such APIs, price transparency data could be applied to individual patients’ specific insurance, so consumers can compare providers and understand which services are covered along with corresponding out-of-pocket.

Finally, consider that the average patient has no idea how potential treatments translate to billing codes, which are necessary to compare expected out-of-pocket costs across providers. Even if they could guess the likely billing codes ahead of time, the doctor may uncover other issues and diagnose different treatments during the visit that affect the cost. This is where AI models trained on claims data could play a transformative role by predicting potential treatment paths with procedures and costs that are most likely to be applicable for a patient’s condition.

There is bipartisan, nationwide regulatory momentum and widespread support among Americans for health care price transparency. With advancements in AI, we are closer than ever before to empowering consumers to actually shop for health care with upfront prices. However, policymakers and consumers must recognize that realizing the full potential of price transparency hinges upon improving health insurance data accuracy and accessibility. Only then can price transparency data become actionable for consumers to make informed choices about their health care.

Kevin Chiu is the co-founder and CEO of Certainly Health. He worked at Uber for seven years where he was a founding engineer on Uber Health after graduating from Stanford University.

The views expressed in this article are the writer’s own.