How can we explain the sudden 30% increase in processing time for patient journey analyses using Komodo Health's Sentinel tool this quarter?

Introduction

The sudden 30% increase in processing time for patient journey analyses using Komodo Health's Sentinel tool this quarter is a critical issue that demands immediate attention. This performance degradation could significantly impact our ability to deliver timely insights to healthcare providers and researchers, potentially affecting patient care outcomes. I'll approach this problem systematically, focusing on identifying the root cause, validating hypotheses, and developing both short-term fixes and long-term solutions.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be a correlation with recent system updates. Have there been any significant changes to the Sentinel tool or its underlying infrastructure in the past quarter?

Why it matters: Recent changes could be directly responsible for the performance issue. Expected answer: Yes, there was a major update to the data processing pipeline. Impact on approach: If confirmed, we'd focus on the update's impact on processing algorithms.

• Considering the scale of the increase, I'm wondering about data volume changes. Has there been a substantial increase in the amount or complexity of patient data being processed?

Why it matters: Data volume changes could explain increased processing time. Expected answer: Patient data volume has increased by 20% this quarter. Impact on approach: We'd need to assess if our current infrastructure can handle the increased load.

• Given the specificity of the 30% figure, I'm curious about our measurement methods. Has there been any change in how we measure or define processing time for patient journey analyses?

Why it matters: Ensures we're comparing apples to apples in our metrics. Expected answer: No changes in measurement methodology. Impact on approach: If changed, we'd need to recalibrate our baseline for comparison.

• Thinking about user behavior, I'm wondering if there's been a shift in how the tool is being used. Have we seen any changes in the types or complexity of analyses being run by users?

Why it matters: User behavior changes could be straining the system in unexpected ways. Expected answer: Users are running more complex, multi-variable analyses. Impact on approach: We might need to optimize for these new usage patterns.