The AI Coding Assistant — ROI Gaps

Corliss
Sep 22
3 min read

Updated: Sep 23

The AI Coding Assistant promises faster chart reviews, fewer denials, and leaner staffing. On paper, the Return on Investment (ROI) looks irresistible. In practice, the gains leak through gaps that the National Association of Insurance Commissioners (NAIC) expects you to close: governance, transparency, data quality, monitoring, and third-party control. If you don’t build these expectations into your AI solutions platform, your “savings” can turn into rework, payer friction, and regulatory risks.

Where ROI Leaks Happen

1) Governance without Proof:

Most teams can't say who “owns” the AI Coding Assistant; few can show NAIC-aligned governance records: named accountability, defined use cases, risk classification, and approvals before expansion.

→ ROI Hit: scope creep, inconsistent AI usage, and audit findings difficult to explain.

2) Data Quality & Bias Control:

Training and prompts often blend messy EHR notes, legacy encoders, and vendor embeddings. Without defined lineage responsibility, sampling plans, and drift rules, how do you defend quality output?

→ ROI Hit: coding variance by site/provider, payer challenges with “patterned inaccuracies.”

3) Explainability & Documentation:

If your AI Coding Assistant suggests a code, does it provide an explainable path? With source terms, guideline logic, and why competing codes were rejected. Would this model pass payer scrutiny?

→ ROI Hit: appeal losses, prolonged AR, and staff time spent correcting AI decision logic.

4) Continuous Monitoring, Not Launch-and-Leave:

KPIs launched (speed & first-pass yield) look great, but month three reveals denial clusters, seasonality issues, and provider mix shifts. Do you have stability metrics, bias checks, and exception review check-ins—weekly?

→ ROI Hit: silent degradation can erode ROI margins quarter over quarter (QOQ)

5) Third-Party Risk & Change Control:

Model updates, prompt tweaks, and ontology changes arrive fast. If your vendor can change AI Coding Assistant behavior without your validation window, who carries the liability and has the leverage?

→ ROI Hit: sudden performance dips and untraceable defects could lead to corrective-action costs.

6) Human-in-the-loop that Actually Works.

“Coder reviews” are controlled, but overrides are not captured properly, with code change reasons, and not fed back into improvement validation and update reports.

→ ROI Hit: these recurring mistakes; minimize labor “savings” and vanish into rework.

A Simple, Defensible ROI Framework

Define the Guardrails First. Approved encounters, specialties, and code families. Put red-flag exclusions in writing.
Instrument Every Decision. For each suggestion: input snippets used, guideline references, confidence, and alternative codes considered.
Track ROI as Net of Risk. (Time saved + clean claims uplift) − (appeal labor + denial loss + corrective actions). Review and report it monthly.
Run NAIC-style control as Product Features.
- Governance register: owners, approvals, risk class, change log.
- Data lineage: source tables, refresh cadence, drift alarms.
- Model validation pack: accuracy, stability, bias by cohort, with acceptance thresholds.
- Incident playbook: who pauses, who remediates, who reports.
- Vendor SLA: notice for changes, sandbox validation window, and rollback rights.
Close the Loop. Convert coder overrides and payer feedback into labeled cases; require measurable error-rate decline release-over-release.

Bottom-Line

If your AI Coding Assistant cannot show its work properly and prove how it is controlled, the ROI is fragile and at risk. Build NAIC-aligned governance into the workflow, not the policy binder. Do that, and your gains are not just promises of faster chart reviews—verifiable margins could be tracked appropriately, data captured can be used to defend AI logic to CFOs, payers, and regulators.

As the Forvis Mazars and NAIC Use of Artificial Intelligence and the NAIC Review AI Oversight at the OECD references highlight, and the AI Coding Assistant, ROI Gap mitigation strategies suggest:

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 Volunteer Education Committee member for the American Institute of Healthcare Compliance (AIHC) and is a Member of the Professional Women's Network Board (PWN).

Disclosures/Disclaimers:

The AI Coding Assistant, ROI Gaps 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.