Why has the average time to fill open shifts on CareRev's platform increased by 30 minutes over the past two weeks?

Introduction

The recent 30-minute increase in average time to fill open shifts on CareRev's platform over the past two weeks is a critical issue that demands immediate attention. This metric directly impacts our ability to match healthcare professionals with facilities efficiently, which is the core value proposition of our platform. I'll approach this problem systematically, focusing on identifying the root cause, validating hypotheses, and developing both short-term and long-term solutions.

Step 1

Clarifying Questions (3 minutes)

• Given the sudden change, I'm wondering about recent platform updates. Have there been any significant changes to the shift-matching algorithm or user interface in the past month?

Why it matters: Recent changes could directly impact shift-filling efficiency. Expected answer: Yes, a minor UI update was implemented. Impact on approach: If confirmed, I'd focus on UI-related hypotheses and user feedback analysis.

• Considering potential seasonal factors, has there been any notable change in the types or volume of shifts being posted recently?

Why it matters: Shift characteristics could affect fill times. Expected answer: No significant changes in shift types or volume. Impact on approach: If unchanged, I'd focus more on internal factors or user behavior changes.

• Thinking about user segments, has there been any shift in the ratio of new vs. returning healthcare professionals using the platform in the last month?

Why it matters: New users might take longer to fill shifts due to unfamiliarity. Expected answer: Slight increase in new user registrations. Impact on approach: If confirmed, I'd investigate onboarding processes and new user experience.

• Regarding data integrity, can we confirm that the method for calculating average time to fill shifts hasn't changed in the past month?

Why it matters: Ensures we're comparing apples to apples. Expected answer: No changes in calculation method. Impact on approach: If changed, we'd need to recalibrate our analysis based on the new methodology.