Agentic AI in Healthcare
Provide intelligent guidance that empowers members to understand their benefits with ease
With any health insurance, you expect a clear understanding of your medical and drug coverage, out of pocket expenses and your in-network provider availability.
Unfortunately, that is not the experience for MVP members.
- Members have to either call in, wait on hold and rely on verbal confirmations and quotes.
16% of all member calls related to benefit questions.
- Members search through large PDF documents, decode complex industry and healthcare acronyms, and then assume the risk of misunderstanding their coverage.
To address this, I worked with developers and a PM to build the first in-house, AI Chatbot that would allow members to self-service their benefit coverage.
Role
Team
- 1 Product Manager
- AI Council
- 2 Tech Teams
- 1 Designer
Time
skills
Impact
- Researched 100+ member pain points, established UX x AI principles, helped build evaluation guide, and designed final prototype
- Launched a pilot AI to the 1500+ MVP employees for testing and feedback
Discover
Objective: Understand what are the main pain points and challenges for members in interpreting benefits, finding care and reading the formulary
Contribution: Worked directly with 5 call center staff to document pain points, pulled analytics on frequent customer contact reasons, and defined UX problem scope for AI solution development
Result: Co-synthesized problem space, opportunities and risk considerations into a presentation for leadership. Idea was pushed to AI council, receiving green-light
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Define
Objective: Pull data and APIs, tag and normalize data. For UX, set boundaries for the LLM and assist in defining testing cases
Contribution: Developed a set of 50+ scenarios and questions, defined conversational principles and helped validate a grounding glossary of 280+ healthcare terms
Result: Engineers were able to successfully parse PDFs, spreadsheets and pull API data. Integrate UX considerations like tone, escalation path, actionability into model parameters
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Design
Objective: Begin testing prototype in Claude Opus, ranking responses and validate outputs are in line with problem statement
Contribution: Worked to build a UX success measurement plan. Collaborated with customer care to pull a sample of ~500 questions from member chats for evaluation.
Co-prepared scoring guide and walkthrough with 5 agents
Result: Call center agents provided AI response feedback, allowing engineers to fine tune the model
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Synthesize
Objective: Create an entry point for the AI assistant within MVP’s existing member portal experience
Contribution: Collaborated with other designer to define visual annotations eg. “presentation rules” for displaying coverage answers, citations, and escalation options. Partnered cross-functionally to create a backlog of features
Result: Team provided engineers with a ready-to-implement design system with UI rules to add to Google Vertex Agent (text, list, CTA, alert, table, link out, etc.) to match content
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Deliver
Objective: Helped build and present deck for VP level leadership audience
Contribution: Co-presented problem statement, research, UX guidelines and sample responses.
Result: Launched on 1/26 a prototype AI to the 1500+ MVP employees for testing and feedback. This was launched in as a standalone URL for testing.
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