Introduction
Increased error rates in Ad Hoc's Medicare Plan Finder API integrations pose a significant challenge to our product's reliability and user experience. To address this issue, I'll employ a systematic approach to identify, validate, and resolve the root cause while considering both immediate fixes and long-term strategic implications.
This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.
Step 1
Clarifying Questions (3 minutes)
Why it matters: Recent changes often correlate with performance issues. Expected answer: Yes, there was an update. Impact on approach: If yes, we'll focus on the update's impact; if no, we'll look at other factors.
Why it matters: Helps narrow down if it's a data-specific or user-specific issue. Expected answer: Error rates vary across user segments. Impact on approach: If varied, we'll investigate segment-specific factors; if uniform, we'll look at system-wide issues.
Why it matters: Ensures we're comparing apples to apples in our metrics. Expected answer: No change in error definition or measurement. Impact on approach: If changed, we'll reassess our baseline; if not, we'll focus on actual performance issues.
Why it matters: External policy changes can affect our system's ability to handle data. Expected answer: Some minor policy updates, but nothing major. Impact on approach: If major changes, we'll investigate policy impact; if minor, we'll focus more on internal factors.
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