What factors are contributing to the sudden 30% increase in no-show rates for Wheel Health's telehealth appointments this quarter?

Introduction

The sudden 30% increase in no-show rates for Wheel Health's telehealth appointments this quarter is a critical issue that demands 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 business.

I'll approach this problem by first clarifying key details, ruling out external factors, and then diving deep into the product, user journey, and metrics. From there, I'll form 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 increase coincided with any particular season or event?

Why it matters: Seasonal patterns could explain temporary fluctuations. Expected answer: No clear seasonal pattern identified. Impact on approach: If seasonal, we'd focus on temporary mitigations; if not, we'd look deeper into systemic issues.

• Considering user segments, I'm curious if this increase is uniform across all user groups. Have you noticed any particular demographic or user type more affected by this increase?

Why it matters: Helps identify if the issue is global or specific to certain users. Expected answer: The increase is relatively uniform across segments. Impact on approach: If uniform, we'd look at system-wide factors; if not, we'd focus on segment-specific issues.

• Thinking about recent changes, have there been any significant updates to the appointment booking or reminder system in the last quarter?

Why it matters: Recent changes could directly impact no-show rates. Expected answer: A new reminder system was implemented last month. Impact on approach: If changes occurred, we'd scrutinize their impact; if not, we'd look at other factors.

• Considering data integrity, has there been any change in how no-shows are tracked or defined recently?

Why it matters: Ensures we're comparing apples to apples in our metrics. Expected answer: No changes in tracking or definitions. Impact on approach: If changed, we'd need to recalibrate our analysis; if not, we can trust the data as is.