AI CDI | EXPLAINABILITY

Corliss
Nov 6
4 min read

The adoption of AI in Clinical Documentation Integrity (CDI) is rapidly changing how health systems capture, interpret, verify, and validate patient information, medical codes, and charges in the modern healthcare ecosystem. However, while AI promises unprecedented levels of efficiency and accuracy, one challenge remains at the forefront: explainability.

Explainability ensures that the logic behind an AI’s decisions, recommendations, and inferences can be clearly understood, verified, validated, and trusted by human experts — particularly physicians, CDI specialists, coders, quality assurance officers, and compliance officers (McCormack, 2024).

1. Defining AI Explainability in CDI

AI explainability refers to the capability of an AI system to justify its output in a manner that is understandable to human users. Translated into CDI, that means understanding why an algorithm flagged a chart for review, how it derived a probable diagnosis or documentation gap, and what data inputs influenced its recommendation.

For example, if an AI model indicates the need to query a physician regarding Acute Kidney Injury (AKI), explainability would mean that the system provides clinical indicators, laboratory trends, and diagnostic context leading to such a decision, rather than just the outcome of the decision itself.

2. Does AI CDI Explainability Really Matter

The answer is, yes! AI-driven CDI tools are utilized to enhance, automate, and improve the entire Clinical Documentation Integrity (CDI) process. These AI CDI tools perform chart reviews, analyze documentation, generate physician queries, and recommend medical code assignment. These actions sometimes require detailed explanations to understand how the AI CDI bridges the gaps between clinical care, coding quality, billing compliance, and optimal reimbursements.

Example No. 1:

Explainability Transparency:

Medical Record Prioritization and Case Identification
- When the Algorithm flags a pneumonia case, AI should indicate whether sepsis or malnutrition indicators are present and should be coded accordingly.
Clinical Concept Extraction
- Natural Language Processing (NLP) and AI CDI tools should extract relevant clinical terms from provider narratives, notes, and reports for accurate coding.
AI-Assisted Physician Query Generation
- The AI CDI platform automatically generates non-leading or un-directed query templates based on recognized documentation gaps. The template should produce quality-driven and compliant queries based on AHIMA, ACDIS, and CMS Query guidelines.

Example No. 2:

Sepsis Documentation Core Elements

Origin of the Infection?
Organism Involved?
Severity of Sepsis?
Etiology, if known?
Associated Major Conditions and Complications?
Clinical Indicators that Support the Severity of Illness?
Treatment Requirements?
Specificity of Condition?
Is it a Perinatal & Pediatric Patient?

How does the AI CDI Technology bridge automation gaps with all of the documentation element requirements and ensure coding accuracy? When this AI system's performance is not measured, monitored, or maintained appropriately. Healthcare organizations could experience unnecessary risk with “black box” systems that produce results that are not verified, validated, or transparent, leaving user uncertainty as it relates to integrity, accuracy, and reliability:

Liability Protections:
- A transparent AI CDI tool requires an audit trail that helps to identify and mitigate ethical and legal risks. It supports defensible documentation and an audit strategy (Molnar, 2022).
Audit Trails:
- Digital logs that are maintained of AI CDI interactions for quality and compliance review (Doshi-Velez & Kim, 2017).

The goal is not just to make AI CDI accurate — it is to make it auditable and accountable.

3. The Future of Explainable AI in CDI

Explainability will soon become a non-negotiable standard in the next generation of CDI technology. As the NAIC AI Model Bulletin (NAIC, 2023) and the EU AI Act (European Commission, 2024) promote risk-based AI governance, U.S. healthcare systems will be required to adopt “Auditable Principles by Design.”

That means embedding AI traceability, performance metrics, and bias checks into the CDI workflow — not as afterthoughts, but as foundational design features.

Tomorrow’s CDI leaders will not just measure success by query turnaround times or financial yield, but they will be judged by how well they can explain, defend, and improve their AI-assisted decisions (European Commission, 2024).

Conclusion

Who are the Voices of Reason Leaders in the rooms when AI CDI Technology decisions are being made, and when Explainability issues are discussed? What gaps are not being bridged between automation and accountability? Physicians and other healthcare providers should be able to trust AI-assisted technologies and the opinions of CDI experts. The integrity and ethical use of AI CDI requires the implementation and enforcement of a quality-driven and compliant governance infrastructure.

In a field where every data point translates to patient diagnoses, treatment protocols, medical codes, and financial integrity, explainability is not a luxury; instead, it is the new standard of quality for responsible AI in healthcare and revenue cycle management. We have work to do!

References

Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. https://arxiv.org/

European Commission. (2024). EU Artificial Intelligence Act: Risk-based governance of AI systems. Publications Office of the European Union. https://commission.europa.eu/

Molnar, C. (2022). Interpretable machine learning: A guide for making black box models explainable. https://www.academia.edu/

McCormack, J. (2024). Ethical issues loom as artificial intelligence shows promise for health information. https://journal.ahima.org/

National Association of Insurance Commissioners (2023). AI Model Bulletin: Responsible AI governance for regulated entities. NAIC Publications.

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 Health Tech and Consulting Company. Ms. Collins stays very busy working on AI in RCM Epic and Cerner 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 CDI | Explainability. 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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