No matter where you look, generative AI is becoming commonplace. In the realm of healthcare market research, we are confident it is here to stay. And, it will be instrumental in the coming years to gaining impactful insights.
However, it is continuously evolving, and doing so quickly. Fundamentally, generative AI can feel at odds with patient-centered research, but we believe it will become a substantive component of research meant to incorporate patient voice. When our inherent objective is to get closer to patients, this duality can feel at odds. How could AI really accomplish that?
At its most benign level of impact, generative AI is simply a tool for analyzing data from patients – a means to analyze and synthesize qualitative data at scale. At the other end of the spectrum, it is the synthetic representation of patient data. And whether you like it or not, the latter is gaining significance and continuing to become more of a reality in its reliable use.
We at KJT firmly believe in the value of being on the forefront of discovering the ways we can leverage generative AI to extend the limits of healthcare market research. However, it is equally important to continue to elevate the (real) patient voice while integrating generative AI tools and approaches into primary market research. To this end, we’ve developed our BRAIN FrameworkTM (Balanced and Responsible Artificial INtelligence).
KJT’s BRAIN FrameworkTM for Leveraging Generative AI:
- Human Accountability: Ensure human oversight in all AI processes, including data inputs, monitoring, and validating outputs for accuracy and reliability.
- Ethical and Responsible Use: Align AI usage with KJT’s core values and legal obligations, emphasizing integrity and mindfulness while mitigating risks like AI-generated hallucinations.
- Balanced Integration: Promote a harmonious blend of human expertise and AI technology, ensuring ethical practices and relevant application of AI systems.
So, what are some of the current use cases of generative AI for patient-centered market research?
Conversational AI
Conversational AI harnesses both voice and text-based interactions to enrich the research process. Voice-based conversational AI generates adaptive surveys that adapt based on previous responses and are essential for tasks like patient journey mapping and market landscape assessment. This type of generative AI can also enhance the quality of open-ended questions in quantitative exercises through “smart probing,” facilitating deeper insights than typically possible
Qualitative Data Analysis and Synthesis
Generative AI accelerates qualitative analysis by interpreting open-ended text to uncover themes, sentiments, and essential insights. This capability is indispensable for identifying critical themes, mapping patient journeys, and more. Generative AI-powered data synthesis can compile and scrutinize data from diverse sources to reveal patterns and trends, enhancing everything from market landscape analysis to segmentation efforts. Nevertheless, maintaining moderator oversight and ensuring the use of the most up-to-date large language models (LLMs) are crucial for authentic insights.
Synthetic Data
Synthetic data, through the creation of AI avatars, simulates target customer segments based on aggregated data. These virtual personas can help deepen understanding during segmentations studies, value proposition testing, and optimizing product profiles. However, the validation with real-world data and incorporating genuine patient insights are critical steps in leveraging synthetic data—a strategy meant to complement rather than replace human-driven research.
In essence, while generative AI represents a transformative leap in healthcare market research methodology– it’s the harmonious integration with human expertise that ultimately ensures patient-centered research, enabling deeper, more reliable insights. However, doing this ensures pointed effort to remain patient-centered, which can be easier in theory than reality.
Below, download our guide to help you navigate using generative AI tools, with a human touch, for market research.
KJT’s Cheat Sheet for Using Generative AI for Patient-Centered Market Research
November 2024
The Evolving Market Research Landscape Generative AI & Ensuring a Human Touch
Generative AI technology is evolving rapidly. In our discussion, we explore the current state of LLMs, their applications and limitations, and anticipated advancements with GPT o1. However, as LLMs and their application to market research evolves it’s important to keep research human-centered, which requires a clear framework for accountability, ethics and integration.
- Michaela Gascon
- Dan Wasserman
Author
Dan Wasserman
Chief Operating Officer
Dan Wasserman is KJT’s COO, leading the integration of generative AI into client solutions to drive innovation and improve patient health. 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.
