Why has Tebra's patient scheduling feature seen a 15% drop in usage over the past month?

Introduction

The recent 15% drop in usage of Tebra's patient scheduling feature over the past month is a concerning trend that requires immediate attention. As we delve into this issue, we'll employ a systematic approach to identify, validate, and address the root cause while considering both short-term fixes and long-term strategic implications.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be seasonal factors at play. Has this drop coincided with any particular time of year or holiday period?

Why it matters: Seasonal patterns could explain temporary fluctuations. Expected answer: No significant seasonal correlation. Impact on approach: If seasonal, we'd focus on annual trends; if not, we'd investigate recent changes.

• Considering user segments, I'm curious about the distribution of the drop. Is the 15% decrease uniform across all user types, or are certain groups more affected?

Why it matters: Helps pinpoint if the issue is global or specific to certain users. Expected answer: The drop is more pronounced among new users. Impact on approach: If specific to new users, we'd focus on onboarding and first-time user experience.

• Thinking about recent changes, have there been any updates to the scheduling feature or related systems in the past 1-2 months?

Why it matters: Recent changes often correlate with performance shifts. Expected answer: A minor UI update was implemented 6 weeks ago. Impact on approach: If changes occurred, we'd scrutinize their impact; if not, we'd look at external factors.

• Regarding data integrity, has there been any change in how we measure or define "usage" for this feature?

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 recalibrate our analysis; if not, we'd focus on actual usage patterns.