Guests
- Jennifer Deal – SVP of Product Development, Healio
- Casey Utley – Senior UX Designer, Healio
- Matthew Skepner – VP of Technology, Healio
What we cover in this episode:
- Why physicians need AI at the point of care—and how they actually use it (hint: it's preparation, not bedside)
- The surprising discovery that physicians wanted help with patient communication and empathy, not just clinical answers
- Building a working prototype in a weekend with Cursor after starting with Figma mockups
- How Healio's RAG system combines lexical search, vector search, and semantic search across multiple trusted sources
- Why "just use PubMed" isn't simple—five different ways to access the same data, each with trade-offs
- Designing citations that physicians trust: subscripts, hover states, and progressive disclosure
- Serving contextual ads while the LLM processes queries—a practical monetization approach
- HIPAA compliance and input guardrails for masking personal health information
- Eight LLM judges for evals: safety, medical accuracy, faithfulness, relevancy, completeness, reasoning, clarity, and overall quality
- Why physician feedback trumps LLM-as-judge feedback in high-stakes medical contexts
- The role of the Healio Innovation Partners in ongoing discovery and validation
Resources & Links
- Healio — Medical news, education, and clinical guidance for healthcare professionals
- PubMed — Database of biomedical literature
- Cursor — AI-powered code editor used to build the prototype
Chapters
00:00 Introduction to Healio Team
01:00 Overview of Healio's Services
01:57 Introducing Healio AI
03:39 Addressing Physician Needs with AI
05:45 Building Trust in AI Solutions
13:56 Prototyping and Testing Healio AI
18:02 Refining the AI Product
21:48 Technical Architecture and Advertising Integration
25:16 Balancing Speed and Accuracy in AI Responses
26:30 Ensuring Credible and Trustworthy Content
27:41 Challenges in Data Integration and Web Crawling
29:00 Optimizing Search Strategies for Different Data Types
31:09 User Interface and Trust Building
34:31 Human Feedback and Continuous Improvement
35:41 Guardrails and Evaluations for Reliable AI
39:11 Experimenting with LLM as Judges
45:13 Future Directions and User-Centric Design
