Recent Coverage rule transparency This represents a ground-breaking change in health care by requiring health insurance companies to disclose prices to the public. This pricing data is provided through machine-readable files containing costs for various goods and services from providers across the United States. Despite these important developments, data is still not really available to consumers.
The first obstacle to practical price transparency is the significant discrepancy in price transparency data. Despite the Centers for Medicare & Medicaid Services (CMS) standardized reporting format, price transparency data from insurance companies often contain vastly different negotiated rates for the exact same procedure, insurance plan, or provider. This is often the case. Such discrepancies can add up to thousands of dollars for even the simplest billing code without a clear reason. For example, an analysis by Indeed Healthcare shows that United Healthcare data shows different prices for his standard 30-minute doctor’s visit (billing code 99213) for his 46% of New York medical providers. It became clear that it was included.
Analysts speculate that some fees implicitly vary based on a variety of factors, such as the patient’s diagnosis and the type of provider providing the service. However, the context explaining the differences is completely opaque, which undermines the credibility of the information. In the meantime, applications that utilize this data must analyze billing data to infer which rate is correct before presenting it to the consumer. One simple suggestion to solve this problem is to mandate that every combination of provider and procedure code has its own price, along with the necessary context to account for variations.
Another major challenge in leveraging this data is that patients do not have access to detailed insurance information. Insurers deploy black-box algorithms to automatically determine patient liability for the majority of claims as part of a process called adjudication. In some cases, this process results in falsely deny a claim. Either way, determining with confidence how an insurance company will calculate a patient’s out-of-pocket costs before submitting a claim is extremely cumbersome and impossible even for medical providers. is.
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To enable consumers to compare prices across providers, insurers will be required to provide a public API to retrieve patient liability and eligibility rules for specific procedure codes. Should. To minimize complexity and maximize output reliability, these requirements can start with simpler procedures and services that can be automatically determined. Access to such APIs allows price transparency data to be applied to an individual patient’s specific insurance, allowing consumers to compare providers and understand which services are covered with corresponding copays. Masu.
Finally, the average patient has no idea how potential treatments translate into billing codes. Billing codes are required to compare expected out-of-pocket costs between providers. Even if you can guess the likely billing code in advance, your doctor may discover other problems during your visit and diagnose other treatments that will impact your cost. Here, AI models trained on claims data can play a transformative role by predicting potential treatment pathways with the most likely steps and costs for a patient’s condition. can.
There is momentum for bipartisan national regulation, and there is widespread support among the American public for health care price transparency. Advances in AI are bringing us closer than ever to the ability for consumers to actually purchase healthcare at upfront prices. However, policymakers and consumers need to recognize that realizing the full potential of price transparency depends on improving the accuracy and accessibility of health insurance data. there is. Only then will 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 healthy. After graduating from Stanford University, he worked for Uber for seven years and was a founding engineer at Uber Health.
The views expressed in this article are the author’s own.
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