The RCM Supply Chain | AI Hallucination Vulnerabilities

Corliss
Oct 23
6 min read

Updated: Oct 24

In the world of revenue cycle management (RCM), artificial intelligence (AI) is no longer a futuristic experiment—it is baked into everything from prior-authorization checks to claims, denial prediction, operational dashboards, clinical documentation integrity (CDI), coding assistance, and audit support. When AI supply chains collide with hallucinations, they confidently produce incorrect or fabricated outputs. In RCM, mistakes can cost money, damage reputation, and lead to costly legal implications. “AI hallucinations” are not just a nuisance—they are potential liabilities Healthcare IT News (2025).

This article breaks down how the AI supply chain applies to RCM, how hallucinations can slip into it, the vulnerabilities they create, and what organizations can do to tame them.

The RCM AI Supply Chain: A Quick Overview

Before diving into hallucinations, let us map out how AI enters RCM workflows—and thus where the supply chain starts and ends.

In RCM, the AI supply chain consists of:

Data Ingestion: clinical documentation, claims information, payer rules, audit outcomes, denials data, and provider metadata.
Data Preparation & Pipelines: cleaning, labeling, structuring, mapping codes (CPT/ICD), linking outcomes.
Model Development & Training: developing predictive models (e.g., risk of denial), NLP models (e.g., coding assistance), or audit summary generative models.
Validation & Governance: Human review, quality control, regulatory compliance (HIPAA, payer regulations, audit trail).
Deployment & Integration: embedding the model into RCM workflows (e.g., automated coding suggestions, claims-scrubbing, denial triage).
Monitoring & Maintenance: watching for model drift, errors, compliance deviations, changes in payer policies, updating, training, and retraining.
Human-in-the-Loop (HITL) Oversight: coding auditors, clinical documentation integrity (CDI) professionals, claims specialists, quality professionals, and legal/compliance reviewers, U.S. CRS Report (2024).

When all these steps flow smoothly, AI can accelerate RCM, reduce errors, improve cash flow, and free up human experts for strategic work. But whenever one link in this chain is weak or overlooked, risk creeps in.

What Are AI Hallucinations?

At its core, an AI hallucination is an output from a model that appears plausible but is factually incorrect, fabricated, or misleading.

Some examples of hallucinations in RCM may be:

A model that recommends a billing code that does not exist or is invalid for the patient's scenario.
An NLP assistant that summarizes documentation incorrectly and is missing key denial justifications.
A pretend dependency or component within the AI infrastructure that introduces risk or mistakes into the pipeline.

These hallucinations matter because in RCM, errors translate into financial losses (denials, underpayment), compliance violations, audit exposure, workflow bottlenecks, and liability.

Why Hallucinations Are a Supply Chain Vulnerability

In the AI supply chain, hallucinations are not just model output errors—they reflect weaknesses across multiple supply-chain links.

Here’s How:

Data lineage & integrity: If the data used for training or inference is flawed or lacks provenance, the model may hallucinate. In RCM, if historical coding data, payer rules, or documentation lack proper context or are incomplete, hallucinations become more likely.
Model development & dependencies: Research shows that code-generation models and large language models (LLMs) introduce fictitious dependencies or incorrect suggestions—this is a “supply chain” risk when the components or dependencies of AI are compromised.
Vendor/third-party risk: Many RCM AI tools plug into third-party platforms or models. If the upstream model or module introduces hallucinations, your supply chain downstream suffers.
Deployment & integration gap: If humans trust model output without review (especially in high-stakes workflows like coding or billing), hallucinations pass straight into production.
Monitoring & governance failure: If there is no continuous feedback, drift detection, or audit trail validation, hallucinations accumulate unchecked, compounding over time.

One specific phenomenon worth noting: “package hallucination” in software supply chains—where AI coding tools suggest libraries or modules that do not exist, enabling attackers to register and exploit them. While this example comes from software development, the same pattern matters in RCM. If an AI subsystem relies on an unvetted component or dataset, it introduces risk into the chain, Slopsquatting: AI Hallucinations, A Drukarev (2025)..

Specific Hallucination Risks in RCM

Here is how hallucinations might play out in RCM—and what they could cost you:

Invalid Billing/Coding Suggestions: An AI-assisted coding tool recommends a code that does not apply, resulting in claim denials or audits. Loss of revenue, increased appeals, and damaged provider trust.
Mis-classified Clinical Documentation: A model misinterprets a note and recommends incorrect documentation changes, raising compliance or audit exposure.
Incorrect Denial Prediction: A denial-triage model is fed faulty data (or hallucinates) and misprioritizes cases, wasting time and leading to missed recoveries.
Unvetted Model Dependencies: An RCM solution uses a third-party model or library that contains a hidden vulnerability or a fabricated component—compromising data security, regulatory compliance (HIPAA/PII), or producing downstream erroneous outputs.
Invisible Contagion Effect: Because RCM systems often feed into each other (e.g., documentation → coding → claims → analytics), a hallucination early in the chain propagates. Just as software dependencies can carry risk downstream, the RCM AI supply chain can as well.
Audit/Regulatory Exposure: In a regulated environment, using AI outputs that contain hallucinations exposes the organization to fines, penalties, loss of accreditation, or payer contracts, NAIC Survey Reveals (2025).

Mitigation: What You Should Do About It!

The risks are clear; here is your action plan:

Implement Human Review Across the Supply Chain
- Every AI recommendation in RCM must pass a human checkpoint (coding, audit, documentation).
- Use HITL roles (CDI, coding auditors, quality reviewers) explicitly within the supply chain.
- Treat AI output as a “draft,” not a final product.
Establish An Audit Trail Validation Process with Traceability
- Maintain data lineage: where did the input data come from? What version/model?
- Track model versioning, dependencies, training data snapshots, and output logs.
- For each critical model (coding suggestion, denial forecast), record who reviewed what and when.
Govern Supply Chain Components & Model Dependencies
- Vet third-party models, libraries, and services.
- Supply Chain decision-makers should require contracts that address data governance, audit rights, and liability.
- For internal models, conduct supply-chain risk assessments: which upstream components are involved? Are any weak?
- Use something akin to a “Software Bill of Materials” (SBOM) but for AI components, to know what you are deploying.
Measure, & Monitor to Detect Drift
- Measure to identify unforeseen outputs (e.g., unusual recommendations, implausible codes, excessive human override rates).
- Monitor Dashboards for model performance, alerting when output varies from expectations.
- Implement routine retraining, review logic modifications, and decommission outdated models.
Governance, Policies & Training
- Establish AI governance: roles, responsibilities, escalation paths.
- Create policies requiring human oversight and documentation for AI decisions that impact revenue, quality, and compliance audits.
- Train staff (documenters, coders, claims specialists) about hallucination risk, model limitations, and the “AI suggestion vs decision” distinction.
Scenario Testing and Audit Outputs
- Simulate real-world scenarios, including edge cases and adversarial conditions, before deploying a model.
- Audit model outputs regularly (especially in high-impact workflow areas) to reveal concealed biases or hallucinations.
Design "fail-safe" Strategies
- If model confidence is low or an out-of-the-ordinary output is generated, route it for manual review, processing, and validation analysis.

Conclusion

The AI supply chain is not solely about automating mundane processes or lowering costs. In Revenue Cycle Management (RCM), establishing and maintaining trust and ensuring ongoing quality and compliance are the key reliability drivers for mitigating risk. Hallucinations are one of the weakest links in the RCM chain. This is not hypothetical—these are real risks that can ripple through your data, models, and workflows, impacting your financial outcomes.

If one part of the chain is ignored—such as when human-in-the-loop review is skipped or a third-party model is used without governance—then you are handing the keys to your RCM value stream over to uncertainty.

To Be Clear: AI does not replace human accountability in RCM. It should amplify it!

The organizational leaders who will succeed in building a robust, traceable, auditable, governed, and resilient AI supply chain will be deemed the winners for ensuring humans remain in the loop—not out of it.

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 RCM Research projects. 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:

AI in RCM Supply Chain | Hallucination Vulnerabilities. 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 properly acknowledged, use the contact page to notify us so we can make the necessary updates.