Interviews remain one of the most powerful ways to understand what patients, caregivers, and healthcare profiessionals truly think and feel. But as therapeutic areas grow more complex, time-pressured, and geographically dispersed, it has become harder to collect the level of depth and detail needed to guide meaningful decisions.
AI-supported interviewing methods can help bridge that gap, not by replacing the human element, but by extending what researchers are able hear, understand and analyze.
Why Interviews Still Matter In Healthcare
Healthcare decisions are shaped by emotion, lived experience, clinical nuance, and trust. These are dimensions that can only be uncovered through thoughtful conversations. Patients may struggle to articulate their fears. HCPs may hesitate to express uncertainty. Caregivers often hold insights no one else sees.
Traditional intveriews reveal these truths, but they also require significant time, coordination, and resources. AI-supported methods allows us to scale qualitative understanding in a responsible way while keeping empathy and expertise at the center.
Where AI Can Help – Responsibly
AI can enhance interview-based research by:
- Identifying themes across large sets of transcripts and open-ends
- Detecting emotional cues, sentiment, and hesitation patterns
- Helping summarize complex qualitive inputs
- Highlighting differences across stakeholder or segments
- Reducing manual coding time so researchers can focus on interpretation
These capabilities help teams explore more questions, analyze more data, and move faster, without sacrificing depth or human nuance.
Maintaining Clinical and Human Context
AI does not replace the need for experienced healthcare researchers. Every insight generated must be interpreted through:
- Therapeutic area understanding
- Clinical relevance
- Patient Burden
- Regulatory and compliance expectations
- Cultural and emotional context
At KJT, AI-supported interview outputs are always reviewed, validated, and refined by experts who understand the realities of patient journeys and healthcare decision-making. This ensures that insights remain accurate, responsible, and meaningful.
Scaling Qualitative Learning Across the Lifecycle
AI-supported interviews can be particularly valuable in:
- Early discovery work
- Message or concept refinement
- Voice-of-customer insights
- Rare disease or low-incidence populations
- Post-launching monitoring
- Patient supported and adherence studies
By increasing efficiency and depth, these tools help teams stay closer to the needs of their audiences throughout the product lifecycle.
A Thoughtful Future for Interview-Based Insights
AI’s role in healthcare research should not be about automation, it should be about amplification. When used with care, oversight, and clinical understanding, AI-supported interviews help researchers hear more clearly, see emerging patterns earlier, and make decisions with greater confidence.
Human conversations will always be at the heart of good research. AI simply helps us honor those conversations with more clarity, depth, and responsibility.
Author
Dan Wasserman
Chief Operating Officer and Head of AI Solutions
Dan Wasserman is KJT’s COO, leading the integration of generative AI into client solutions to drive innovation as part of Sparq Intelligence. With expertise in compliance, operations, and analytics, he transforms complex data into actionable insights. Dan values collaboration with KJT’s teams to help clients design better products and services.
