How Artificial Intelligence Supports the Insights Process for Medical Affairs

Artificial intelligence is changing how Medical Affairs teams generate and analyze insights. Kwello recently hosted a webinar with Dave Harper, Senior Field Medical Director at Sanofi, on the persistent challenges in insight generation and the role AI can play in addressing them. This article summarizes that discussion. Watch the full webinar recording for the complete conversation.

AI insight generation is the use of artificial intelligence, including natural language processing, to collect, categorize, and summarize structured and unstructured field data so Medical Affairs teams can review it faster and act on it sooner.

Adoption is already underway across the sector. In a fourth-quarter 2024 survey of 150 U.S. healthcare leaders, 85 percent said their organizations were exploring or had already adopted generative AI capabilities (McKinsey, 2024). mckinsey

Key Takeaways

• Medical Affairs teams often collect tens of thousands of data points annually, making manual processing impractical.
• Delays in processing insights can push relevance and impact months past when the data was originally collected.
• AI can process both structured and unstructured data, including free text, using Natural Language Processing.
• Kwello's ElsieAI helps Sanofi analyze survey data and summarize key topics in a fraction of the manual time.
• AI enables real-time dashboards, giving field teams visibility into how their insights are used.

What Makes Insight Generation Difficult for Medical Affairs Teams?

In the webinar, Scott Thompson, Co-Founder of Acceleration Point, described the enduring challenges Medical Affairs teams face in generating actionable insights. Although insight generation is a core part of field medical activity, the work of collecting, analyzing, and disseminating insights often stays slow and inefficient. Dave Harper, with more than 20 years at Sanofi, walked through the main obstacles:

• Volume and complexity of data: Teams often collect tens of thousands of data points annually from organic insights and structured surveys. That volume makes manual processing slow and hard to sustain.

"If we're analyzing a year's worth of data, we're talking about tens of thousands of data points easily, and that's very hard to work through manually to try and find what's really useful and meaningful in there." — Dave Harper

• Timeliness and actionability: A primary issue is the delay between collection and analysis. Insights gathered early in the year may only be reviewed months later, by which point their relevance has faded. This is one of the recurring problems covered in Unlock the Power of Medical Affairs Insights.
• Integration of structured and unstructured data: Combining structured survey data with unstructured free-text entries adds another layer of difficulty, and it is hard to do manually. This challenge is not unique to Medical Affairs. Across healthcare, unstructured free text has been estimated to account for roughly 80% of electronic health record data and remains difficult to process at scale for secondary use (Li et al., Yale University, 2022). arxiv
• Communication and utilization: Insights collected by field teams often disappear into a "black hole," with little feedback on whether they are used. That gap can reduce the perceived value of field teams' efforts.

How Does AI Enhance Insight Analysis?

AI addresses these challenges by automating and streamlining parts of the insight generation process. It can handle both structured and unstructured data, allowing teams to process and review large datasets more quickly. This reduces the hours team members would otherwise spend on data entry and manual analysis.

• Automated data processing: AI can handle large volumes of structured and unstructured data more efficiently than manual methods. Natural Language Processing (NLP) can analyze free-text data and surface information that would otherwise be overlooked.
• Real-time analysis and reporting: AI reduces the time lag in processing insights. By automating analysis, it supports closer-to-real-time reporting so insights stay timely and usable. Kwello's AI-powered Medical Affairs Insights Assistant, ElsieAI, analyzes data from multiple sources, including field medical activities and social media interactions. For a broader view of how these tools fit into Medical Affairs work, see How Medical Affairs Teams Can Use Generative AI.

"We may decide that, say, for the first quarter we're going to look at all the insights for the first quarter, but maybe we start working on that in April, and by the time we can put together all that data into a meaningful report and share it, it may be the end of May." — Dave Harper

• Accuracy and consistency: AI can improve the consistency of data categorization and analysis, which reduces the risk of human error and variation in interpretation.
• Communication and feedback: AI-powered dashboards and reports can give field teams faster feedback on how their insights are being used and how they inform strategy and decision-making.

How Does AI Turn Field Data Into Actionable Strategy?

Dave Harper shared practical examples of how AI is used at Sanofi. By applying AI-driven tools, Sanofi has made its insight generation process more efficient. The parallels with broader field medical work are covered in Medical Excellence Across Field Teams: Insights from Sanofi. Key examples:

• AI for survey analysis: Sanofi uses ElsieAI to analyze survey data quickly and identify key trends without extensive manual effort. Watch this clip on AI-powered survey analysis.

"I can ask it to summarize only insights that mention a certain thing. And I ask it to provide me what the top five topics are with specific examples of each. And so it'll do it. It is almost instantaneous. I get that summary report with those top topics ranked, specific examples." — Dave Harper

• Real-time insight dashboards: AI-powered dashboards provide current updates on the status of insights, so field teams can see how their data is being used.
• Integration of diverse data sources: AI helps combine insights from different sources into a fuller view that informs strategic decisions.
• Training and support: AI can also support field team training by highlighting good practices for data collection and entry, which improves the quality of the insights captured. See this clip on AI-assisted field team training.

How Does AI Help Medical Affairs Teams?

AI changes how Medical Affairs teams work with insights at a practical level. By addressing the core challenges of volume, timeliness, and integration, it helps teams review their insights more quickly and use them to inform strategic decisions. As the technology continues to develop, its applications in insight generation are likely to expand, adding tools and methods that address the challenges discussed in the webinar. The skills needed to make good use of these tools are covered in 3 Key Skills Medical Insight Leads Must Master.

Kwello focuses on the process of insight generation, not only the underlying technology. The platform is built for Medical Affairs teams and reflects the specific workflows and constraints they operate within. This focus on process is intended to help teams of different sizes fit AI into how they already collect and review insights.

Kwello helps Medical Affairs teams process large volumes of field data into organized, reviewable insights, and it shortens the time between when an insight is collected and when it reaches the people who use it. The goal is operational: to make field insights easier to surface, share, and act on within existing Medical Affairs workflows.

Want to see how this applies to your team's insights process? Learn more about the Kwello platform.

FAQ

Why does AI matter for insight generation in Medical Affairs?
AI automates the processing of large volumes of structured and unstructured field data, reducing the delay between when insights are collected and when they become actionable.

What kinds of data can AI process for Medical Affairs teams?
AI can process both structured data, such as survey responses, and unstructured data, such as free-text field notes, using Natural Language Processing to extract meaningful patterns from both.

How does ElsieAI help Medical Affairs teams like Sanofi's?
ElsieAI analyzes survey and field data quickly, identifying key trends and summarizing top topics with specific examples, work that would otherwise take significant manual effort.

Does AI replace the judgment of Medical Affairs and field teams?
No. AI is presented as a tool that automates data processing and surfaces patterns faster, while field teams and Medical Affairs leaders remain responsible for interpreting and acting on the insights.

Further Reading

• How Medical Affairs Teams Can Use Generative AI
• Unlock the Power of Medical Affairs Insights
• Medical Excellence Across Field Teams: Insights from Sanofi
• 3 Key Skills Medical Insight Leads Must Master