THE AI In RCM | DRIFT GRIFT

Corliss
3 days ago
2 min read

Updated: 1 day ago

Is the AI Algorithms Drift issue a Grift?

No. Artificial Intelligence (AI) Algorithmic Drift itself is NOT a grift — but the way some vendors handle the issues associated with AI Drift, (or hide it) absolutely can be.

Algorithm drift is a real, well-documented technical phenomenon in machine learning. Drift itself is a legitimate science.

For those who might be pondering what a Grift is? An AI in RCM Grift refers to a kind of hustle that exaggerates AI in RCM system capabilities, while concealing its flaws (i.e., drift that causes a decline in AI coding accuracy, validity, or compliance)

Drift happens naturally as:

Data Patterns Change
Clinical Documentation Evolves
Payer Rules Shift
Provider Behavior Changes
Workflows Change
Model Inputs Age

Example:

1) Data Drift Problem: AI Models degrade as inputs change:

Solution:

Measure and monitor to detect data drift, retrain AI Models, validate samples, and enforce data lineage to control predictable performance.

2) Misaligned Objectives Problem: Focusing on the wrong KPIs minimizes Optimization yields and delivers perverse outcomes:

Solution:

Developing frameworks, methods, and processes will help you to implement guardrail metrics that keep AI focused on improving actual return on investment (ROI).

3) Compliance Gaps Problem: Improperly documented workflows invite risk:

Solution:

Design a customized quality-by-design playbook that combines automated audit trails, role‑based controls, and tailored policy templates to help your team close these AI governance gaps. Fixing the AI in RCM drift and data degradation issues requires not just algorithms but also includes human-in-the-loop rigor, which will make the operational success you seek possible. Never underestimate People Powered Intelligence (PPI)™

When AI is used compliantly, it can transform revenue cycle management (RCM) if implementation failures are addressed. As a Certified AI Professional and Certified AI Medical Coder, I have seen three recurring AI Drift pitfalls that should be analyzed and addressed promptly.

Need support? Our proprietary frameworks, methods, and processes can help guide you through. Ready to scale or audit your AI in RCM? Let’s talk: https://wix.to/gdEJQRD #HealthcareAI #RCM #AIGovernance

About the Author

Corliss Collins, BSHIM, RHIT, CRCR, CCA, CAIMC, CAIP, CSM, CBCS, CPDC, serves as the Principal and Managing AI Consultant of P3 Quality, a Healthcare Tech and Consulting Company. Ms. Collins stays very busy working on AI projects in RCM Epic and Cerner 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). She is a Member of the Professional Women's Network Board (PWN).

Disclosures/Disclaimers:

The AI in RCM | Drift Grift. This analysis draws on research, trends, and innovations in the AI in Revenue Cycle Management (RCM) industry. AI generates some of the blog content and details. 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 appropriately acknowledged, use the contact page to notify us so we can make the necessary updates.

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