What factors are contributing to the increased error rate in Commure's clinical decision support tool since the latest update?

Introduction

The increased error rate in Commure's clinical decision support tool since the latest update is a critical issue that demands immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term fixes and long-term implications for the product.

I'll approach this problem by first clarifying key details, ruling out external factors, and then diving deep into the product's functionality and user journey. From there, I'll break down the metric, gather relevant data, form hypotheses, and conduct a thorough root cause analysis. Finally, I'll propose validation methods and outline a comprehensive resolution plan.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking this might be related to the recent update. Can you confirm when exactly the latest update was rolled out?

Why it matters: Pinpoints the timeframe for investigation. Expected answer: A specific date within the last month. Impact on approach: Narrows down the scope of data analysis.

• Considering user segments, I'm curious if the increased error rate is uniform across all user types. Have you noticed any patterns in terms of which users or healthcare specialties are experiencing more errors?

Why it matters: Helps identify if the issue is systemic or specific to certain user groups. Expected answer: Variation in error rates across different user segments. Impact on approach: May lead to focusing on specific user journeys or use cases.

• Given the nature of clinical decision support, I'm wondering about the severity of these errors. Are we seeing an increase in minor discrepancies or potentially harmful misdiagnoses?

Why it matters: Assesses the urgency and potential impact on patient care. Expected answer: A mix of error severities with some concerning cases. Impact on approach: Influences prioritization of fixes and communication strategy.

• Thinking about system changes, has there been any recent integration with new data sources or changes in the underlying algorithms?

Why it matters: Could point to technical issues or data inconsistencies. Expected answer: Information about recent backend changes or data source updates. Impact on approach: May shift focus to data integrity or algorithm refinement.

• Considering user feedback, have there been any changes in how users are interacting with the tool since the update?

Why it matters: Could indicate usability issues or unintended changes in user behavior. Expected answer: Reports of confusion or changes in usage patterns. Impact on approach: Might lead to a deeper examination of the user interface and experience.