I went straight to solutions which was probably the wrong move.
Start by clarifying the problem scope and impact, then systematically diagnose root causes using data and user feedback. Prioritize solutions based on impact vs effort, and propose a phased roadmap with clear success metrics.
Pro tip: In medical software, speed improvements must never compromise diagnostic accuracy or regulatory compliance—frame trade-offs explicitly to show domain maturity.
Ask clarifying questions to understand which user segments are affected, what 'slow' means (e.g., seconds vs minutes), and how it impacts clinical workflows. Define baseline metrics like average processing time and user satisfaction scores.
Analyze the processing pipeline to identify bottlenecks (e.g., algorithm inefficiency, hardware constraints, network latency, data volume). Use logs, user interviews, and A/B tests to pinpoint the biggest contributors.
Brainstorm potential fixes (e.g., algorithm optimization, cloud offloading, caching, UI feedback). Evaluate each on impact, effort, risk, and regulatory constraints. Prioritize quick wins and high-impact changes.
Set clear metrics (e.g., reduce processing time by 50%, increase user satisfaction by 20%). Propose a pilot or A/B test to validate improvements before full rollout, ensuring no degradation in accuracy.
Present a phased roadmap to stakeholders, highlighting trade-offs and dependencies. Commit to continuous monitoring and iteration based on user feedback and performance data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.