Why has AKASA's AI-powered medical coding solution seen a 15% drop in accuracy rates over the past month?

Introduction

AKASA's AI-powered medical coding solution has experienced a concerning 15% drop in accuracy rates over the past month. This decline in performance is a critical issue that requires immediate attention and a thorough root cause analysis. I'll approach this problem systematically, examining both internal and external factors that could be contributing to the decreased accuracy.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might have been a recent update to the AI model. Has there been any significant change to the AI algorithm or training data in the last 1-2 months?

Why it matters: Changes in the AI model could directly impact accuracy rates. Expected answer: Yes, there was a model update about 6 weeks ago. Impact on approach: If confirmed, we'd focus on the model update as a primary factor.

• Considering the nature of medical coding, I'm wondering about any recent changes in healthcare regulations or coding standards. Have there been any major updates to ICD-10 or CPT codes in the past quarter?

Why it matters: New or changed codes could affect the AI's accuracy if not properly incorporated. Expected answer: No significant changes to coding standards recently. Impact on approach: If no changes, we'd shift focus to internal factors or data quality issues.

• Given the specificity of the 15% drop, I'm curious about our measurement methodology. Has there been any change in how we're calculating or measuring accuracy rates?

Why it matters: Changes in measurement could create a false perception of decreased accuracy. Expected answer: No changes to measurement methodology. Impact on approach: If confirmed, we'd focus on actual performance issues rather than measurement discrepancies.

• Thinking about potential data quality issues, have we seen any changes in the types or sources of medical records being processed by the AI in the last month?

Why it matters: New or different data sources could introduce unfamiliar patterns or errors. Expected answer: Some new healthcare providers were onboarded recently. Impact on approach: If confirmed, we'd investigate the impact of new data sources on accuracy.