Leveraging AI in Healthcare Market Research: Insights from Industry Leaders

In a recent Peer Connection session, healthcare industry professionals met to explore the evolving role of artificial intelligence (AI) in market research. Hosted by Escalent Vice Presidents Courtney Kerwin and Carla Essen, we discussed real-world applications of AI, particularly in data collection and insight generation, two sensitive areas. While AI’s applications in market research are broad—from transcription to knowledge management—our focus for the session was specifically on its role in data collection and insight generation.

From our discussion, it was clear that each organization and individual researcher is on their own journey with AI, and there is significant distance between where each organization and individual is at. Some are actively experimenting in controlled settings, while others are more cautious, preferring to learn from early adopters. We discussed opportunities and concerns around AI and shared examples of how Escalent is experimenting with AI for analysis and synthesis, deliverable generation, and creatively elevating stimulus.

Read on for key takeaways from our session and the learning we’ve had at Escalent in our own journey deploying AI for clients that are at various places on their own AI journeys.

Where Researchers Are Today: No Single Approach to AI

Tempered, intrigued, cautious, uncertain, excited were all words used to describe how Peer Connection participants are feeling about their individual journeys with AI. While there’s novelty and intrigue, the session was headlined by the fact that there must remain a delicate balance between leveraging technology and retaining the humanistic side of research, especially as we look to adopt more tools geared towards speed and automation. AI holds the promise of making market research more efficient, but some researchers have reservations about whether those gains offset the insightfulness humans can deliver. Experimentation is occurring in the industry, but there are questions about how AI works and the magnitude of the benefits remain up for debate with the tools currently available.

At Escalent, we believe the path forward is through experimentation, and we are quickly learning the best use cases and how to mitigate some of these early challenges—all under the guidance of our full-time head of AI development.

The Opportunities and Challenges of Being on the Forefront of AI in Insight Generation

AI has many applications in market research and, indeed, it’s actively being used by many businesses for everything from transcription to translation to knowledge management. For many participants in this session, this is one of the areas where AI’s strength lies: in its ability to quickly query and summarize historical data. Insight professionals also mentioned using AI to build predictive models for advanced analytics, sentiment analysis, feedback categorization, but that’s as far as it went for now. When it comes to the value of AI in data collection and insight generation, feedback from the group varied significantly based on the depth of experience.

While AI will continue to change the way we do research, its use in the core research collection and insight generation process still requires oversight and validation to ensure accuracy. So, while AI will eventually save us time—particularly for insight generation—today it doesn’t always meet our lofty expectations. This will undoubtedly change as tools continue to evolve.

In our experience, we look to AI as an enhancer to workstreams vs. outright replacement of the insight development process. With that said, we should not discount the fact that AI has enabled real strides in decreasing timelines across other parts of the research value chain. Think about navigating cumbersome knowledge management systems, transcriptions, translations, etc. These are all areas that have been streamlined with new AI-enabled platforms, and we anticipate the profound progress made in other parts of the research value chain to transform data collection and insight generation in the future.

One of the more contentious areas discussed was AI’s role in qualitative research. Several participants expressed excitement about AI’s potential to enhance creativity in delivery of stimulus and final output. Yet, despite the intrigue, there was skepticism, especially around use cases like AI moderation and synthetic respondents. Participants voiced questions like:

• Is it difficult to show up like yourself with an AI moderator?
• Can a bot really engage empathetically and appropriately with a patient like another human?
• What happens when the medical terminology is not fully understood?
• How do you know if the findings are accurate when you see nothing?
• How do you ensure AEs are still captured?
• How do you really know what is happening with the data?

All these questions highlight that there is a need for more experimentation and tool advancement before researchers are prepared to lean on AI without any oversight. We’ve had to navigate this ourselves at Escalent.  We’ve embraced AI as an experimental co-pilot in market research, but we’ve also put in place quality control processes at the beginning of every workstream. Additionally, we continuously train our employees as new tools emerge to ensure that we are using the right tool for the right task.

Trust Underpins Beliefs About AI in Research

A consistent theme throughout the discussion was the issue of trust and concerns about whether AI tools could reliably tell the story that researchers want to convey. AI’s ability to synthesize data is impressive, but the nuanced insights that often arise from impactful research remain challenging for AI to deliver. There was also recognition that AI’s usefulness is contingent upon how well researchers engage with the tool, likening it to an ongoing conversation rather than a hands-off solution. In a world where there’s too much information and a rise of misinformation, the virtues of AI came even more into question. The key tension underpinning the conversation was trust. And as we know, trust is hard to gain and fast to lose. Trust will be a key hurdle to the adoption of AI in market research ahead, especially in an industry where we know trust is a vital yet very complex topic. Over the past year, Escalent has developed a smart, strategic roadmap that ensures we build a foundation on trust for our employees and clients. We have experts demoing and piloting tools, and only whitelisting those that meet our compliance standards.

“We thoroughly evaluate AI to enhance our research quality by improving accuracy, efficiency and reliability at various stages of the research process. This includes validating incoming data against predefined quality parameters real-time, identifying errors and inconsistencies in the raw data sets, flagging anomalies in aggregated data and detect for original and human generated content vs synthetic datasets. The underlying goal is to empower our researchers with tools to analyze large volumes of high-quality data and uncover hidden trends, correlations and patterns that might go unnoticed manually.” —Abhinav Kothari, Chief Information & Technology Officer, Escalent

The Road Ahead: What’s Next for AI and Research?

While there are a lot of questions about what’s next with AI, the consensus on the present state of AI in data collection and insight generation is that it delivers some real advantages on timeline savings. It can, however, fall short compared to its human counterparts on insight generation, storytelling, and emotional intelligence—the foundational elements of incisive research. For that reason, the human role remains central in the deployment of AI in data collection and insight generation. AI is the co-pilot, not the captain.

As companies continue to pilot AI tools in their research processes, our conversation amongst researchers underscored the importance of finding the right balance when embracing innovation. There is a lot to be excited about, but there are also multibillion-dollar decisions at stake. No researcher was prepared to leave the entire research process to AI, and yet no researcher denied the important part that AI will play in market research as we go forward.

The key is ensuring that the art of research remains at the heart of what we do, even as we embrace new technologies. No matter where you and your organization are on your AI journey, you can always lean on us for due diligence. Chat with us about how you can leverage AI across your workstreams.

Want to learn more? Let’s connect.