RAC to the Future: AI in Healthcare Audits

Healthcare providers are starting to see the first claims audits based on analysis and determinations made by artificial intelligence (AI). Although the technology is new, many of the issues remain the same. Especially where the companies that develop AI-based audit tools sell these tools and services to commercial insurance companies, AI-driven audits increasingly resemble audits of Medicare providers and suppliers performed by the Recovery Audit Contractors, or RACs.

RACs are Medicare contractors charged by the Centers for Medicare & Medicaid Services (CMS) to identify overpayments and underpayments made to providers and to facilitate return of overpayments to the Medicare Trust Fund. Primarily, RACs accomplish this by conducting audits and issuing repayment demands. RACs are different from other types of Medicare contractors that conduct audits because RACs are paid on a contingency fee. That is, RACs received a percentage of any funds they extract from providers, making them significantly incentivized to deny claims and demand repayment even where there is no clinical or legal basis to do so.

Similarly, because few insurance carriers have developed sophisticated AI tools in house, they often contract outside technology companies to provide the AI audit tools, and often to conduct the audits themselves. These outside contractors are motivated to deny claims and identify alleged overpayments in order to retain the business of the insurance carrier. This motivation is further enhanced where the outside contractor is paid a percentage of the alleged overpayments that their AI tool identifies. Therefore, any provider should carefully scrutinize any such audit findings, much as they would scrutinize the findings of a similarly motivated RAC.

Further, AI-driven audits also shared concerns about the competence of the reviewer. RAC audits are often criticized for utilizing under-qualified coders, nurses, or others to attempt to review the documentation and complex decisions of physicians and specialists. Any AI tool is only as good as the underlying data on which it is built and trained. It is difficult to know how any AI tool has been trained because technology companies generally consider this information proprietary. However, an AI tool that reviews physician records may make the same mistake or misunderstanding over and over again because, like a nurse reviewer, it simply does not understand the content, context, or the decision making that it is attempting to review. Providers should carefully review all audit findings, especially where any questions exist regarding the qualifications or competence of the reviewer.

For over 35 years, Wachler & Associates has represented healthcare providers and suppliers nationwide in a variety of health law matters, and our attorneys can assist providers and suppliers in understanding new developments in healthcare law and regulation. If you or your healthcare entity has any questions pertaining to audits or healthcare compliance, please contact an experienced healthcare attorney at 248-544-0888 or wapc@wachler.com.