Administrative waste costs the U.S. health care system an estimated $350 billion each year. This includes $266 billion that is attributed to administrative complexity and between $59 and $84 billion caused by fraud and abuse.
This is why health systems, health plans and policymakers are enthusiastic about the potential of emerging artificial intelligence tools to address administrative waste. New capabilities that enable activities such as documentation using ambient scribes, chart abstraction, billing and parsing of complex health plan policies have generated high expectations that technology will drive meaningful administrative efficiency gains, according to the Peterson Health Technology Institute.
Earlier this year, the institute convened senior leaders from health systems, health plans, technology developers, investment firms and federal agencies to discuss how technology and policy can enable AI to reduce administrative costs, accelerate payment cycles and promote appropriate high-value care.
For example, in prior authorization, providers are using AI tools to automate submissions, while plans use AI to triage and evaluate prior authorization requests. In medical billing, providers use ambient scribing and AI-assisted coding tools to capture increasing clinical complexity and automate billing, while health plans use AI to assist reviewing and processing claims.
"Though we are still in the early stages of administrative AI adoption, it has become clear that rapid AI deployment by both providers and health plans to support prior authorization and medical billing transactions risks increasing levels of system activity without reducing costs," the report said. "Under existing incentive structures, AI automation could increase the volume of prior authorization back-and-forth, rather than making the process more efficient. AI-assisted coding tools could accelerate coding intensity and charge capture, which -- even if accurate -- would have an inflationary impact on health care costs."
Participants reached these conclusions about the use of AI:
- AI may reduce the cost for individual organizations to execute prior authorizations, but it has not reduced overall system-level costs.
- Real-time prior authorization at the point of care is an emerging model, but current proofs of concept are narrow and not yet scalable.
- Data standards and digitization of medical policies can reduce information asymmetry, but AI's impact is limited by variation across medical policies.
- AI is exposing and exacerbating fundamental issues within the underlying prior authorization process.
- Provider deployment of AI is increasing billing intensity and inflating medical spending.
- Health plans are beginning to respond to AI-driven increases in billing intensity with across-the board downcoding and other reimbursement reductions, but the impact of these cuts is not yet known.
- Reimbursement policy is the strongest lever to drive administrative efficiencies and system-level cost savings.
"As currently deployed, AI in health care administrative processes is likely to achieve only some of its goals while simultaneously increasing health care costs," the report concluded. "When applied on top of flawed administrative workflows, data complexity and incentive structures, AI exacerbates the underlying issues. Realizing the potential for AI to reduce administrative waste will require redesigning the processes on which the technology is being deployed."
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