Why has Infinx's Prior Authorization software seen a 15% drop in approval rates over the past month?

Introduction

The recent 15% drop in approval rates for Infinx's Prior Authorization software is a critical issue that demands immediate attention. This decline not only impacts the product's core value proposition but also has potential ripple effects on customer satisfaction, revenue, and market position. To address this complex problem, I'll employ a systematic approach to identify, validate, and resolve the root cause while considering both short-term fixes and long-term strategic implications.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be a recent change in the software or workflow. Has there been any significant update or feature release in the past month?

Why it matters: Recent changes often correlate with performance shifts. Expected answer: Yes, there was a minor update to the authorization algorithm. Impact on approach: If confirmed, we'd focus on the update's impact on approval rates.

• Considering the metric itself, I'm curious about its stability. Has the definition or measurement of "approval rates" remained consistent over the past few months?

Why it matters: Ensures we're comparing apples to apples in our analysis. Expected answer: The definition has remained unchanged. Impact on approach: If changed, we'd need to recalibrate our baseline for comparison.

• Given the nature of prior authorizations, I'm wondering about external factors. Have there been any significant changes in payer policies or regulations in the last month?

Why it matters: External changes could explain the drop without indicating a product issue. Expected answer: No major policy changes noted. Impact on approach: If changes occurred, we'd need to adapt our software to new requirements.

• Thinking about user segments, I'm curious if this drop is uniform across all users. Are we seeing this 15% decrease consistently across different healthcare providers or specialties?

Why it matters: Helps identify if the issue is systemic or specific to certain user groups. Expected answer: The drop varies across specialties, with some more affected than others. Impact on approach: If varied, we'd focus on the most affected segments first.