Why has Mayo Clinic's online appointment scheduling system seen a 30% drop in usage over the past month?

Introduction

The 30% drop in usage of Mayo Clinic's online appointment scheduling system over the past month is a significant issue that requires immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term and long-term implications for the product and organization.

I'll approach this problem by first clarifying key details, ruling out external factors, and then diving deep into the product ecosystem, user journey, and relevant metrics. From there, I'll generate data-driven hypotheses, conduct root cause analysis, and propose validation methods and solutions.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be a seasonal component. Has this 30% drop been compared to the same period last year?

Why it matters: Seasonal trends could explain the drop and influence our solution approach. Expected answer: No significant seasonal variation observed in previous years. Impact on approach: If seasonal, we'd focus on optimizing for peak periods; if not, we'd investigate recent changes.

• Considering user segments, I'm curious if this drop is uniform across all patient types. Are we seeing any differences in usage patterns between new and returning patients?

Why it matters: Different user segments may be affected differently, pointing to specific issues. Expected answer: The drop is more pronounced among new patients. Impact on approach: If new patients are more affected, we'd focus on onboarding and first-time user experience.

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

Why it matters: Recent changes could directly correlate with the usage drop. Expected answer: A minor UI update was implemented six weeks ago. Impact on approach: If changes coincide with the drop, we'd prioritize investigating those specific updates.

• Regarding system performance, has there been any increase in error rates or page load times for the scheduling system?

Why it matters: Technical issues could be driving users away from the online system. Expected answer: No significant changes in error rates or load times observed. Impact on approach: If technical issues are present, we'd prioritize fixing those; if not, we'd focus more on user experience and feature-related hypotheses.