The Dark Side of AI | Implementation Pitfalls Exposed

Corliss
5 days ago
2 min read

We see the same mistakes over and over again in Artificial Intelligence (AI) and Revenue Cycle Management (RCM) projects: data drift, misaligned KPIs, and weak governance.

An example of issues that shrink projected revenue and create quality and compliance risks on implementation projects is provided below:

Drift and Decay: The AI Model Gets Worse Silently

The Pitfalls:

Teams launch new AI models and forget about them. The environments begin to change over time—policies, procedures, patient mix, documentation habits, medical coding, payer rules, behavior, seasonality, etc.

Black Box Issues with no accountability
AI Bias, a business quality and a compliance risk
Reactive vs Proactive security and privacy protocols
AI Workflow Mismatches: the model is right, but not a good operational fit

What It Looks Like:

“This AI Model used to work.” It changed! No one can explain why performance has become flawed.

The Fix:

Put quality checks, balances, and monitoring in place (data drift, concept drift, performance, fairness, and alert thresholds), and define retraining and rollback procedures for implementation processes.

At P3 Quality, we audit AI models early, identify drift, align KPIs with revenue cycle management goals, and establish governance that scales pilot gains into sustainable performance improvements.

Want to protect revenue, enhance quality, and compliance before small implementation problems become big issues and costly failures?

Visit https://wix.to/N2dHZRG 🔍📈 #HealthcareAI #RevenueCycleManagement #HealthTech

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 company specializing in Epic and Cerner AI in Revenue Cycle research, development, and issue resolution management. 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 Dark Side of AI | Implementation Pitfalls Exposed. This analysis draws on research, trends, and innovations in the AI in Revenue Cycle Management (RCM) industry. Some of the blog content is 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 appropriately acknowledged, use the contact page to notify us so we can make the necessary updates.

P3 Quality™