AI Medical Insights Platform

Medical teams decide where to focus based on what physicians tell them, what trials and publications show, what’s changing in regulation and what’s emerging at medical congresses. That information sat in separate places, so pulling it together took hours of manual work every month. By the time it was compiled, the insight was already behind what was happening in the field.

The real cost was capacity, more than speed. Every hour spent compiling data was an hour field medical teams weren’t spending with physicians. It also delayed medical directors in adjusting strategy.

One AI platform that brings together 8 data sources. They include regulatory information, clinical trial intelligence, publications (PubMed), congress data, advisory board reports, and field medical notes from the CRM where Medical Science Liaisons log their physician interactions.

• Conversational assistant Users ask questions in plain language and get answers grounded in the source data, using retrieval-augmented generation (RAG).
• Product insight summaries What’s being heard about each product, in one view.
• Trend comparisons How themes shift from period to period, such as one quarter against the same quarter last year.
• Recurring insight digests Key themes delivered on a schedule, so users don’t have to ask.
• Agentic workflows and a metadata layer Content is tagged by product and context, so answers stay precise as the data grows.
• Contextual filtering Users asked for a separate dashboard for each brand. What they needed was faster access to relevant insights, so I built one platform with filtering instead of a tool per brand.

• Field medical teams (Medical Science Liaisons) Less time compiling, more time with healthcare providers.
• Medical directors Spot shifts sooner and change strategy faster.
• Medical and product leadership Compare trends across periods to see what’s changing and why.
• The organization One shared platform for every product team instead of separate tools for each brand.

68% of users report faster or more confident decisions.

• Turned around weak early adoption. Usage was low after the proof of concept. I raised it with leadership and ran a 2–3 week discovery sprint with medical directors, field medical teams and their leads, mapping how each role actually works.
• Found the real gap. The pain points users described weren’t on the backlog. The team had been shipping features, but users needed faster decisions.
• Reset the OKRs. I set one north-star objective (faster, more confident medical decisions) with key results for each user group. Every backlog item had to map to a key result, or it was deprioritized.
• Changed the rollout. I presented the platform to senior medical leadership across product teams. That led to training requests and more teams onboarding.
• Scaled onboarding. I redesigned and standardized onboarding. Cycle time fell by about 50% and throughput doubled.