Coffee & Conversations:

The Truth About AI Coding Automation in Healthcare

AI is making waves in healthcare, but what does AI coding automation really mean for your healthcare organization? Can you trust it to accurately code charges? How does it impact workflows, labor, and efficiencies?

Join Gary Bernklow, Senior Product Director at medaptus, as he unpacks the reality of AI coding automation, separating hype from real-world impact. In this webinar, you’ll learn:

• What AI means for medical coding and healthcare
• The truth about AI hype vs. reality
• The key differences between AI coding automation and traditional coding methods
• Critical factors to consider before adopting an AI-powered solution

Watch On Demand

Watch now to gain expert insights on optimizing your coding processes with AI.

Webinar Transcript

Opening Remarks

Julie: Welcome everyone! Today, Gary will be sharing his expertise on AI coding automation, helping us understand what AI really means for medical coding and healthcare. 

He’ll discuss the reality behind the AI hype, the differences between AI coding and CAC, and key things to consider when evaluating AI solutions. After his presentation, we’ll have a live Q&A session, so please submit your questions in the Q&A window. We’ll also share a recording of this webinar by email for you to rewatch and share with colleagues. Now, over to you, Gary!

Understanding AI in Healthcare Coding

Gary: Thanks, Julie. At medaptus, we’ve been developing innovative software solutions for over 20 years, focusing on underserved but high-value areas in healthcare. Our goal is to help healthcare professionals focus more on patient care rather than administrative burdens.

Recently, an HFMA survey confirmed that AI coding automation is here to stay. Many providers are already incorporating AI into their revenue cycle workflows, with about 20% planning to do so soon, and half of those looking to adopt AI solutions within the next 6–12 months. The reason is simple: AI promises efficiency, workflow streamlining, and improved revenue generation.

Key Terminology: AI, CAC, and Autonomous Coding

One of the biggest challenges in discussing AI coding is the confusion around terminology. Let’s clarify a few key terms:

  • Artificial Intelligence (AI): Any technology that enables machines to simulate human intelligence and problem-solving.
  • Computer-Assisted Coding (CAC): Software that uses natural language processing (NLP) and machine learning to analyze clinical documentation and suggest medical codes (ICD-10, CPT, etc.).
  • Autonomous Coding: AI-driven coding that automatically generates codes based on structured data from EHRs without human intervention.

The Difference Between CAC and Autonomous Coding

Gary: A common question is how CAC differs from autonomous coding. CAC assists coders by recommending codes, which then require human verification. Autonomous coding, on the other hand, fully automates the coding process using structured data, eliminating the need for manual review.

However, while AI coding sounds promising, challenges remain. AI systems must interpret clinical language accurately, and without structured data, there’s a risk of misinterpretation. For example, a sentence like “Cindy’s birthday comes before Stacy’s” could be misread as Cindy being older when she may not be. Data-driven AI, however, would simply compare birthdates to determine age accurately.

Considerations When Evaluating AI Coding Solutions

When looking at AI coding solutions, keep these key factors in mind:

  1. Accuracy Rate: What is the minimum accuracy level required to justify implementation?
  2. Workflow Impact: How much manual work is still required for validation?
  3. Training & Learning Period: How much historical data must be fed into the system for it to learn effectively?
  4. Feedback Mechanism: How does the system handle incorrect coding and refine itself over time?
  5. Compliance & Privacy: How does the solution handle sensitive patient data while ensuring HIPAA compliance?
  6. Scalability: Can the solution support multiple specialties and departments?

AI in Action: The Case of Infusion Coding

One area where medaptus has successfully implemented AI-driven autonomous coding is outpatient infusion coding. Infusion coding is complex due to its time-based nature and hierarchy of drugs. Our AI-powered solution pulls data directly from EHRs, applies coding rules, and produces fully compliant billing codes automatically. This results in an 80–90% increase in coder efficiency and reduced manual workload.

Q&A Session

Julie: Thanks, Gary. That was insightful! Let’s move to some audience questions:

  1. Will AI replace human coders?
    • Gary: No, not in the near future. AI will enhance coder efficiency rather than replace them, allowing coders to focus on oversight and validation.
  2. What does the implementation process involve?
    • Gary: Implementation depends on the solution. AI-driven systems require extensive data integration and training. Natural language processing-based AI takes longer to train, while structured data-driven AI is quicker to deploy.
  3. How does AI ensure patient data privacy and HIPAA compliance?
    • Gary: AI solutions must follow HIPAA regulations, ensuring secure data handling and encrypted storage. Organizations should verify vendor compliance before implementation.
  4. Does medaptus offer AI coding solutions for nephrology?
    • Gary: Not currently, but we’re exploring AI coding solutions for additional specialties, including nephrology, to address underserved areas.

Conclusion & Next Steps

Julie: Thank you, Gary, and thanks to everyone who attended today’s webinar. If you’re interested in implementing AI coding into your operations, reach out to us to schedule a follow-up call. Follow medaptus on LinkedIn to stay updated on our latest innovations.

For direct inquiries, you can contact Gary via email or LinkedIn. Thank you for reading our webinar recap and have a great day!