What factors are contributing to the sudden 30% increase in no-show rates for Cityblock's behavioral health appointments this quarter?

Introduction

The sudden 30% increase in no-show rates for Cityblock's behavioral health 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 our product and users.

I'll approach this problem by first clarifying key details, ruling out external factors, and then diving deep into our 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 time of year or event?

Why it matters: Seasonal patterns could indicate external factors rather than product issues. Expected answer: No clear seasonal pattern identified. Impact on approach: If seasonal, we'd focus on anticipating and mitigating cyclical factors.

• Considering user segments, I'm curious if this increase is uniform across all patient groups. Are we seeing higher no-show rates in specific demographics or appointment types?

Why it matters: Segmentation could reveal targeted issues affecting particular user groups. Expected answer: The increase is more pronounced in younger patients and for first-time appointments. Impact on approach: We'd tailor our solutions to address the needs of specific user segments.

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

Why it matters: Recent changes could directly impact user behavior and appointment attendance. Expected answer: A new automated reminder system was implemented two months ago. Impact on approach: We'd focus on analyzing the effectiveness and user experience of the new system.

• Regarding data integrity, I'm wondering if there have been any changes in how we track or define no-shows. Has the definition or measurement of no-shows remained consistent?

Why it matters: Ensures we're comparing apples to apples and not facing a data anomaly. Expected answer: No changes in definition or measurement. Impact on approach: Confirms the issue is real and not a result of measurement discrepancies.