Clinical Documentation Integrity (CDI) Automation Issues
- Corliss
- Aug 18
- 2 min read
🚨 One Minute Read:
The Hidden Dangers of Untrustworthy Clinical Documentation Integrity (CDI) Automation.
According to an IBM | Read in Apple News (2025), CEOs report that only 25% of Artificial Intelligence (AI) initiatives delivered the expected ROI over the past three years. Why?
While AI is transforming healthcare, it is not always for the better. Hundreds of thousands, millions, and possibly more are being invested in AI in Revenue Cycle Management (RCM) automation solutions.
Behind the buzzwords and automation lies a growing, yet unspoken, risk: inaccurate and untrustworthy AI-driven CDI systems that quietly compromise data quality, medical coding integrity, reimbursement accuracy, and patient safety. These systems often generate suggestions based on incomplete context, outdated coding logic, or biased training models. Yet, many organizations adopt these technologies without auditing their inputs, outputs, or establishing quality and compliance standards.
The result? Misleading clinical records, financial leakage, increased audit risk, and potential harm to patients.
🔍 Where is the Oversight?
Despite the critical role CDI plays in revenue cycle management, quality, and compliance, most health systems lack a standardized method to evaluate the accuracy, transparency, or bias of AI-generated documentation suggestions.
📢 It is time to act.
Healthcare leaders, patients, and consumers must demand independent AI audits, enforce CDI governance frameworks, and prioritize human-led AI collaboration—not relying solely on blind trust when it comes to automation.
✅ Do not let bad CDI Automation become your liability.
✅ With predictability, adaptability, and data being the new currency driving most AI implementation and deployment decision-making.
✅ Start auditing. Start questioning. Start protecting your patients, the consumers, and revenue now.
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 Consulting Company. Ms. Collins stays very busy working on Epic and Cerner RCM projects. She also serves as a subject matter expert and Volunteer Education Committee for the American Institute of Healthcare Compliance (AIHC) and is a Member of the Professional Woman Network Board (PWN).
Disclosures/Disclaimers:
This CDI automation 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 can identify Responsible AI in RCM Governance and Stewardship gaps, address the issues, and recommend results-driven solutions.
References:
CEOs report that only 25% of AI initiatives delivered the expected ROI over the past three years. (2025).
Read in IBM: https://apple.news/AfPtVk3-WSDOqzhDKKZj5XQ
Improving Clinical Documentation with Artificial Intelligence: A Systematic Review. (2024).