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I partner closely with architecture, product, and engineering to create intelligent systems that simplify workflows, surface actionable insights, and maintain user trust. My work spans internal productivity tools and member-facing healthcare products, always prioritizing clarity, usability, and responsible AI integration.
These examples show how I’ve applied AI to reduce friction, accelerate decision-making, and deliver measurable impact.
Challenge: Manual documentation consumed valuable time and diverted clinician focus from patient care.
Solution: Designed a context-aware ambient intelligence assistant that quietly drafts structured notes in real time, validated through rapid, iterative prototyping.
Impact: Reduced documentation time, maintained accuracy, and allowed clinicians to spend more time with patients.
Challenge: Critical decisions were slowed by insights buried in complex analytics tools.
Solution: Applied conversational AI to enable natural language queries, surfacing targeted, evidence-based recommendations directly within workflows.
Impact: Improved decision-making speed, increased confidence, and kept users focused on high-priority work.
Challenge: High-stakes medication decisions required parsing large volumes of complex information under time pressure.
Solution: Designed an AI-driven workflow to summarize and prioritize relevant medication details, enabling faster, more informed decisions.
Impact: Reduced cognitive load, lowered risk of error, and improved clinician confidence.
Challenge: Coordinating across teams and disciplines in healthcare was inefficient and often lacked structure.
Solution: Created an AI-assisted tool that suggests task lists, plans, and summaries to align teams in real time.
Impact: Improved team alignment, sped up coordination, and supported better patient outcomes.
Challenge: Population health planning was reactive, with risks and trends often identified too late.
Solution: Integrated predictive analytics to anticipate risks, analyze patterns, and surface actionable foresight for earlier interventions.
Impact: Enabled proactive planning, smarter resource allocation, and earlier patient engagement.
Challenge: Revenue cycle workflows were time-consuming, error-prone, and costly.
Solution: Designed AI-assisted features for automated verifications, denial pattern analysis, and coding assistance, with a focus on transparency and trust.
Impact: Reduced processing time, improved accuracy, and lowered administrative burden.