Finding Human Truth in the Age of AI: How AI is Transforming – Not Replacing – Qualitative Research in Healthcare

Authored by: Ellie Forde, Lead of Qualitative Center of Expertise, Research Partnership

First, AI appeared to be going after quantitative research. Now we hear rumours that AI will make the need for qualitative researchers obsolete.

Rather than making qualitative research redundant, AI is becoming a powerful enabler. It acts as a supercharged assistant rather than a replacement.  AI is certainly helping to make light work of qualitative tasks like big data analysis, interpretation, and reporting, but the human element of critical thinking, nuanced understanding, and contextual awareness remains crucial for delivering meaningful insights. We believe it will continue to do so.

So instead of fearing AI is going to take our jobs, our qualitative researchers are embracing tools that help us deliver deeper insights. Here are some recent examples of how it’s making our work better:

1. Smarter, More Sensitive Data Collection

AI tools for qualitative research are revolutionizing how we collect and interpret qualitative data. Where they have been particularly useful is in areas such as:

• Chatbots for conversational probing: Helping us collect deeper, contextual responses to quantitative surveys, chatbots can simulate human interaction, probing respondents’ answers to uncover richer insights. One example is their use in assessing retention and comprehension of materials. In a recent study at Research Partnership, we used a chatbot to follow up with respondents after they were exposed to a patient campaign. Rather than simply asking if they remembered the content, the chatbot engaged in a conversational format, prompting them to describe key takeaways in their own words. This allowed us to evaluate not only what was remembered, but also how well the information was understood and emotionally processed – insights that would have been difficult to capture through traditional survey methods.

• AI Avatar Moderators: These avatars can sometimes be effective in taking the role of human moderators, ensuring consistent and unbiased moderation across multiple sessions. They can communicate in multiple languages, operate 24/7, and handle sensitive topics with ease, making respondents more comfortable and open. For example, an AI avatar can replace a human when interviewing respondents on challenging or sensitive subjects that might ordinarily make the respondent more uncomfortable or anxious. The respondent, knowing they are not talking to a “real” human, is more willing to be honest about their feelings and experiences.

Where probing needs to be complex and adaptive, such as with key opinion leaders (KOLs); the role of a human moderator in reading subtext, body language and emotional nuance is more critical. In these cases, or for highly iterative or multi-stakeholder studies, we would consider a combined approach of letting AI lead the executional elements but having the human fill the gaps, so they play more of a role in strategic curation.

2. Faster, Deeper Analysis of Unstructured Qualitative Data

One of the frustrations of collecting big, unstructured qualitative data has been the challenge of making sense of it all. AI excels in handling and analyzing large, unstructured qualitative data. It helps researchers organize, analyze and find common threads and themes with ease. Here are some examples of how it has helped:

• Transcription and Text Analytics: AI tools quickly transcribe interviews and analyze text to identify themes and trends that might elude human analysts. This capability allows researchers to derive meaningful insights from vast amounts of data. We were recently able to analyze over 200 in-depth interviews and deliver a report in under two weeks using AI transcription and text analytics, helping the client make a fast decision on strategic communications direction.

• Facial Expression Analysis: Machine learning algorithms analyze facial expressions to understand respondent reactions and preferences, providing deeper insights into their emotional responses. We use facial analysis to augment traditional qualitative research and find it to be valuable in revealing natural and immediate reactions to communications materials that we can probe more deeply on in follow-up interviews.

3. Delivering More Impactful and Accessible Insights

When it comes to presenting the insights, a challenge for agencies and their pharmaceutical clients is being able to share insights across disparate teams, territories, brands and regions. AI can make light work of customizing, tailoring and sharing outputs across borders and time zones simultaneously in a number of ways:

• Automated Reporting and Visualization: AI generates comprehensive reports and visualizations, highlighting key insights and trends for different departments.
• Natural Language Processing (NLP): NLP tools summarize complex data into easy-to-read narratives, making technical findings accessible even to non-experts.
• Personalized Insights Delivery: AI tailors insights based on the recipient’s role and preferences. For instance, marketing teams might receive insights on physician behavior, while product teams get feedback on drug and packaging features. We have found that AI is also becoming more sophisticated at delivering them in engaging formats like podcasts or personalized AI avatars, both of which are beginning to look and sound increasingly lifelike and natural.
• Interactive AI Assistants: AI assistants like ChatGPT can answer questions about market research data, provide summaries, and suggest strategies based on the latest insights.

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AI isn’t replacing human insight – it’s amplifying it.

Our belief is that AI-driven tools are not a replacement for human insight – but they are proving to be a very powerful partner. AI enhances human capabilities and helps us conduct more effective and insightful research. As global healthcare becomes ever more complex, we need AI’s capabilities to help us deliver qualitative insights at scale. That’s where efforts should be focused – and as long-term advocates for qualitative research, that’s where meaningful progress will be made.

At Research Partnership, we’re combining AI’s capabilities with human expertise to uncover true human insight – at scale, with impact.

Interested in learning more about how we’re using AI in qualitative healthcare research?
Contact us to explore how we can help you deliver smarter insights.