What factors are contributing to the sudden 50% increase in wait times for 98point6's text-based medical consultations during peak hours?

Introduction

The sudden 50% increase in wait times for 98point6's text-based medical consultations during peak hours is a critical issue that demands immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term fixes and long-term strategic implications.

To tackle this problem, I'll follow a structured approach:

1. Clarify the situation with targeted questions
2. Rule out basic external factors
3. Analyze the product and user journey
4. Break down the metric
5. Gather and prioritize relevant data
6. Form data-driven hypotheses
7. Conduct root cause analysis
8. Propose validation methods and next steps
9. Present a decision framework
10. Outline a comprehensive resolution plan

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking this might be a recent change. When exactly did you first notice this increase in wait times?

Why it matters: Pinpointing the timeframe helps isolate potential causes. Expected answer: Within the last week or two. Impact on approach: A sudden change suggests a specific trigger rather than gradual degradation.

• Given the focus on peak hours, I'm wondering about capacity. Has there been any recent change in the number of available medical professionals during these times?

Why it matters: Staffing directly impacts wait times. Expected answer: No significant changes in staffing. Impact on approach: If staffing is stable, we'll focus more on technical or user behavior factors.

• Considering the nature of medical consultations, I'm curious about the types of cases. Has there been a shift in the complexity or duration of consultations recently?

Why it matters: Changes in consultation nature could affect overall throughput. Expected answer: No notable change in case types. Impact on approach: If case types are consistent, we'll look more at system performance or user influx.

• Given the specificity of the increase, I'm thinking about measurement accuracy. Have there been any recent changes to how wait times are calculated or reported?

Why it matters: Ensures we're addressing a real issue, not a reporting anomaly. Expected answer: No changes in measurement methodology. Impact on approach: Confirms we should focus on actual wait time causes rather than data issues.