AI in RCM | ROI Cracks

Corliss
Sep 29
3 min read

When AI fails to deliver the promised ROI, issues identified within the service line should be audited, stabilized, or claw back value exited.

What Actually Happens (and Why It Hurts)

Financial:

Cost-to-collect creeps up, first-pass yield stalls, DNFB/A/R days rise, labor “savings” disappear as shadow rework.

Operational:

Rework loops explode into (auths, edits, appeals). Staff trust tanks → adoption tanks → AI delivery outcomes worsen.

Compliance/Risk:

Audit exposure (AI/upcoding/medical necessity), 1557 fairness issues, HIPAA/data-use compliance issues, and vendor drift with no validation logs.

Reputation:

“AI is not working as promised.” This story is captured with CFOs, boards, and payers.

Seeing ROI Cracks Through the NAIC Lens

Fairness:

AI coding tools and claims data used to train models can create systemic risks and errors that lead to unfair denials and inequitable patient billing.

o ROI models sometimes overlook the downstream impact.

Accountability:

Vendors rarely provide Auditable logs for the AI decisions made.

o Hospitals/healthcare systems are held accountable for ROI risks. Vendors not so much.

Transparency:

Black-box algorithms fail the NAIC transparency test.

o Leaders are unable to explain why claims are being auto-denied or auto-coded inaccurately.

Compliance:

AI ROI models rarely factor in CMS policy updates, payer-specific requirements, or quality alignment strategies.

o Non-compliance costs (fines, appeals, reputational damage) are often hidden in the vendors' ROI projections.

Evaluate the Cost of Ignoring the Issues

Hospitals and payers face double exposure: expenses associated with unproven AI service delivery, and escalating compliance risk cause ROI to become unstable.

Establish ROI Reinforcements

Leaders can move their organizations from ROI cracks to confidence by ensuring the following guidance is implemented:

Develop a 90-Day ROI-Failure Playbook (RCM-focused)

1. Days 0–14 | Stabilize issues, and Stop the Bleed

2. Days 15–45 | Diagnose ROI leakage and Force a Cure Plan

3. Days 46–90 | Create a Greenlight vs. Red Light recovery plan, Scale, Salvage, or Exit

The NAIC framework is not just a regulatory guidance tool—it is a financial stabilizer. While NAIC accountability does not shift to hospitals, health systems, and third-party vendors. NAIC/CMS has made it clear: algorithms cannot replace human and medical necessity determination reviews. Hospitals, health systems, and third-party vendor policies must adhere to Traditional Medicare requirements and undergo human-in-the-loop review.

Failure to adhere to these recommendations opens the door to AI in RCM ROI gains collapsing under the weight of hidden costs, payer disputes, and compliance failures.

Sources:

National Association of Insurance Commissioners (NAIC). AI Principles: Fair and Ethical Insurance (2020).
Centers for Medicare & Medicaid Services (CMS). Program Integrity and Compliance Guidance.
ISO. ISO 9001:2015 Quality Management Systems — Requirements.

About the Author

Corliss Collins, BSHIM, RHIT, CRCR, CCA, CAIMC, CAIP, CSM, CBCS, CPDC, serves as a Principal and Managing Consultant of P3 Quality, a Health Tech and AI in RCM Consulting Company. Ms. Collins stays very busy working on Epic and Cerner AI projects in RCM research. She also serves as a subject matter expert and a member of the Volunteer Education Committee for the American Institute of Healthcare Compliance (AIHC) and is a Member of the Professional Women's Network Board (PWN).

Disclosures/Disclaimers:

AI in RCM: Foundational Cracks are a Warning Signal. This analysis draws on research, trends, and innovations in the AI in Revenue Cycle Management (RCM) industry. Some of the blog content and details are generated by AI. Reasonable efforts have been made to ensure the validity of all materials and the consequences of their use. If any copyrighted material has not been properly acknowledged, use the contact page to notify us so we can make the necessary updates. P3 Quality is a Responsible AI in RCM Governance and Stewardship leader who identifies gaps, supports addressing the issues, and recommends results-driven solutions.