Chatbot Demonstrates Moderate Interrater Reliability in Billing for Hand Surgery Clinic Encounters
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View abstract on PubMed
Summary
This summary is machine-generated.Artificial intelligence (AI) chatbots show moderate reliability in medical billing, achieving 68% agreement with a surgeon. This suggests AI could streamline administrative tasks in healthcare, improving efficiency and reducing costs.
Area Of Science
- Medical Informatics
- Artificial Intelligence in Healthcare
- Healthcare Administration
Background
- Artificial intelligence (AI) presents opportunities to alleviate administrative burdens in healthcare.
- Machine learning applications in medical coding and billing may offer significant time and cost savings with minimal risk to patient outcomes.
Purpose Of The Study
- To evaluate the performance of a publicly available AI chatbot, Gemini, in generating Current Procedural Terminology (CPT) codes for surgical encounters.
- To assess the interrater reliability between the AI chatbot and a hand surgeon for medical billing accuracy.
Main Methods
- A large language model chatbot (Gemini) was queried with 139 de-identified patient encounters.
- The AI was asked to assign CPT codes based on encounter type criteria.
- Percent agreement and Cohen's kappa coefficient were calculated to measure reliability.
Main Results
- Gemini achieved 68% overall agreement with the surgeon, with a kappa coefficient of 0.586 (moderate reliability).
- Highest agreement was observed for postoperative encounters (98%), while new encounters showed the lowest agreement (48%).
- The AI suggested higher billing levels 31 times and lower levels 10 times, with 4 incorrect encounter type codes.
Conclusions
- A general-purpose AI chatbot demonstrated moderate interrater reliability in medical billing tasks when compared to a hand surgeon.
- Integrating AI tools into physician workflows could enhance billing accuracy and speed, potentially lowering administrative expenses.
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