A Head Start: Using AI Before the Research Begins
And the gold medal goes to...
Getting up to speed before a project begins is the best use of AI in healthcare market research, in my opinion.
Every project comes with a learning curve. We need to understand the condition, treatment landscape, client’s challenge and decisions the research will inform.
Even when we know the therapy area, we cannot assume that yesterday’s understanding answers today’s questions.
AI can help us prepare more efficiently, giving us a stronger foundation for research.
Building an initial picture
Imagine starting a project exploring treatment decisions in severe asthma.
Before developing the discussion guide, researchers need to understand terminology, treatment pathways and the roles of different healthcare professionals.
AI can help organise an initial briefing around these topics, using relevant source documents.
That gives the team a starting point for discussion and highlights areas requiring closer investigation.
However, an overview should never be mistaken for verified expertise. Researchers must check its sources, clinical accuracy and relevance to the markets being studied.
Making better use of existing knowledge
Clients often have valuable evidence scattered across previous reports, presentations and internal documents.
AI can help retrieve and organise that material, where its use is permitted.
For example, researchers could ask:
“What barriers to treatment initiation appeared in our previous three studies?”
The useful output would identify findings, their sources and differences between studies.
That makes it easier to build on existing knowledge, avoid unnecessary repetition and identify questions that remain unanswered.
Navigating published evidence
A new project might involve guidelines, published studies and information about treatment availability.
AI can help summarise selected documents and compare what they cover.
It might highlight that one publication describes clinical recommendations while another examines patients’ experiences of accessing treatment.
Researchers then need to read the relevant passages themselves, check publication dates and distinguish recommendations from actual practice.
Sources should be traceable. A plausible reference that cannot be found is no reference at all.
Finding gaps and challenging assumptions
Good preparation should do more than confirm what we already believe.
Suppose previous research suggests convenience drives treatment choice. AI can help researchers examine whether the evidence also points to access, confidence or perceived effectiveness.
That can generate hypotheses for fieldwork.
The distinction matters: a hypothesis gives us something to investigate. It does not establish what respondents will say.
Protecting confidential information
Previous reports and client briefs may contain sensitive commercial information.
They should only be processed through approved services, with verified protections covering access, retention and permitted use.
Uploading documents should also be consistent with client agreements and participant commitments.
Arriving better prepared
For me, the benefit is straightforward: less time searching through material and more time deciding what the research needs to uncover.
AI can help us arrive at the first project meeting with a clearer picture, sharper questions and a better understanding of what we still need to learn.
Experienced researchers remain responsible for deciding which evidence deserves confidence and where uncertainty should shape the research.