What factors are contributing to the increased wait times for Kaiser Permanente's telehealth video visits compared to last quarter?

Introduction

Kaiser Permanente's increased wait times for telehealth video visits compared to last quarter is a critical issue that requires immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term fixes and long-term strategic implications.

I'll approach this problem by first clarifying the context, then ruling out external factors before diving deep into the product ecosystem, user journey, and potential internal causes. We'll generate data-driven hypotheses, conduct root cause analysis, and develop a comprehensive plan for validation and resolution.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be a seasonal impact. Has there been a significant increase in overall telehealth demand this quarter compared to last?

Why it matters: Helps distinguish between capacity issues and increased demand. Expected answer: Yes, there's been a 20% increase in demand. Impact on approach: If true, we'd focus on scaling capacity; if not, we'd look deeper into internal factors.

• Considering user segments, I'm wondering if the increased wait times are uniform across all patient types. Are certain demographics or medical specialties experiencing longer waits than others?

Why it matters: Identifies if the issue is systemic or localized to specific user groups. Expected answer: Longer waits are primarily affecting non-urgent care visits. Impact on approach: If true, we'd prioritize optimizing non-urgent care processes; if not, we'd look at system-wide bottlenecks.

• Thinking about recent changes, have there been any significant updates to the telehealth platform or scheduling system in the past quarter?

Why it matters: Helps identify if recent changes could be contributing to the issue. Expected answer: A new scheduling algorithm was implemented last month. Impact on approach: If true, we'd focus on the new algorithm's impact; if not, we'd explore other internal factors.

• Considering performance metrics, has the definition of "wait time" remained consistent, and are the systems measuring it functioning correctly?

Why it matters: Ensures we're comparing apples to apples and that the data is reliable. Expected answer: The definition and measurement systems haven't changed. Impact on approach: If true, we'd proceed with current data; if not, we'd need to reassess our metrics and measurement tools.