Cookbooks · Healthcare · Pharma medical affairs
The literature agent found papers. The evidence team still could not choose the next question.
18 October 2026 · FinAnalytic Editorial · 4 min read
Reference implementation. An illustrative business scenario showing how FinAnalytic would approach the problem. It does not describe a completed client deployment or measured results.
The medical team has a summary of recent insights, a folder of publications and an updated study tracker. The AI assistant has produced a polished account of each. In the planning meeting, the same question returns: does the evidence address the population and outcome the team actually needs to understand?
Retrieval found the topic, but missed the decision
This reference scenario concerns evidence organisation for a pharma medical-affairs team. A general retrieval assistant finds papers sharing the right terminology, but a relevant topic is not necessarily the right population, comparison, endpoint or follow-up period.
Separate insight and study-planning agents can compound the problem if one treats a planned study as completed evidence while another treats an unanswered medical question as an approved strategic priority. Their summaries may be individually plausible and jointly misleading.
Bring scientific and operational owners into discovery
Our Forward Deployed Engineering approach would begin with medical colleagues, evidence-generation specialists and data owners defining the decision the workflow should support. We would trace representative insights to the objectives they may inform and the evidence already available.
The business requirement resolver would identify missing context and ask for clarification rather than assign strategic meaning on its own. A shared ontology could link an insight, objective, population, endpoint, evidence item and planned study. It would preserve the distinction between evidence that exists, work in progress and a proposed option.
Coordinate evidence, interpretation and approval separately
A controlled investigation could gather candidate sources, compare their scope and prepare a review packet. For longer investigations, a deep-agent harness could help maintain unresolved questions and evidence references across steps. Scientific relevance and study suitability would still require qualified review.
The packet should expose an apparent gap and the evidence supporting that interpretation. It should also show uncertainty: an inaccessible source, an incomplete plan or a finding whose applicability has not been established. Document retrieval alone cannot settle these questions.
A study option is the beginning of appraisal
Before any funding decision, specialists would assess scientific validity, data suitability, feasibility, time, cost and governance. For example, the FDA’s Advancing RWE programme explicitly considers data relevance, reliability and key design elements. That is a reminder that having access to data is different from having evidence suited to a question.
This cookbook does not prescribe a study design or claim regulatory acceptability. The proposed system supports internal planning; it does not make clinical decisions or replace medical, statistical or regulatory review.
Value means a clearer decision, not more study proposals
The pilot would measure time to a scientifically reviewed packet, traceability of claims, missed or falsely identified gaps, reviewer corrections and agreement on the question to investigate. Use adjudicated cases and retain unfamiliar examples for evaluation.
Do not claim research-budget savings from a faster summary. A spending reduction would need a documented decision and a defensible comparison. The near-term business objective is better preparation for expert review, with less repeated evidence assembly and fewer hidden assumptions.
Bring one difficult handoff into focus
FinAnalytic’s Applied AI Solutions work begins with the people, process and evidence behind a business problem. For healthcare · pharma medical affairs, a useful starting point is one recurring exception, an accountable business owner and a measurable baseline.
Research basis & further reading
- LangChain: Deep Agents capabilities and execution model
- FDA: Advancing Real-World Evidence Program
- W3C: ontology concepts and relationships (OWL 2 Primer)
Sources checked 18 October 2026. The operating scenario and proposed design are FinAnalytic’s illustrative interpretation; the references do not validate a FinAnalytic deployment or its results. Cover illustration generated for this article.