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The application of artificial intelligence technologies to health care delivery, coding and population management may profoundly alter the manner in which clinicians and others interact with patients, and seek reimbursement.
While on one hand, AI may promote better treatment decisions and streamline onerous coding and claims submission, there are risks associated with unintended bias that may be lurking in the algorithms. AI is trained on data.
To the extent that data encodes historical bias, that bias may cause unintended errors when applied to new patients. This can result in errors in utilization management, coding, billing and healthcare delivery.
The following hypothetical illustrates the problem.
A physician practice management service organization (MSO) adopts a third-party software tool to assist its personnel in make treatment decisions for both the fee-for-service population and a Medicare Advantage population for which the MSO is at financial risk.
The tool is used for both pre-authorizations and ICD diagnostic coding for Medicare Advantage patients, without the need of human coders.
The MSO’s compliance officer observes two issues:
> It appears Native American patients seeking substance abuse treatment are being approved by the MSO’s team far more frequently than other cohorts who are seeking the same care, […]