Listening Priorities for Medical Science Liaisons (MSL) Part 3

Part 3 concludes my interview with Nicolas Georgiades. Our conversation reminded me of the value of dialogue on this topic. He emphasized many important points including the need to incentivize quality over quantity, how we must continue to evolve using tools such as AI to identify meaningful insights gathered, and the need to continue to build upon the skills MSL use as they practice their art and have impact. The importance of considering different approaches to capture meaningful insights including through listening priorities that move the medical strategy forward are one of the great values MSLs have for the medical affairs team and the company more broadly. 

Kim: How do you feel about insights as key performance indicators (KPIs)?

Nicolas: You get what you incentivize so it boils down to KPIs. If you want quality insights, focus on quality or utility incentives, not the number of insights gathered. Reconcile insights with strategy and provide timely coaching. Use AI to support the identification of good insights and trends within in the CRM.

Kim: Before AI, we extracted insights from spreadsheet and this manual process was inefficient. We would consider the data in month or quarterly silos, likely missing trends that could only be identified by monitoring the full dataset as insights were entered. Once collated, slides were built to summarize the findings. How are you using AI to facilitate this process?

Nicolas: AI is valuable to mine the full dataset of insights to identify trends that were not seen by the short-term, more myopic view of the manual review. Our tool, Medical Excellence Application connects to the team’s CRM through API and determines impact in an automated way. A report is autogenerated, saving time, saving time to consider the strategy the insights will impact.

Kim: So how does your tool measure MSL impact?

Nicolas: It’s not about the number of KOL meetings, but rather the quality of those meetings. Moving from the quantitative measurements to the qualitative has been more challenging. Our tool measures value through an algorithm using AI and a personalized review to quantify the impact of the MSL insights to determine if it is meaningful against the high listening priorities.

Kim: What about insights gained outside of the listening priorities? Does your tool capture spontaneous insights?

Nicolas: Information entered into the free text section of the CRM will also be assessed by AI and incorporated into the report. Training to ensure MSLs contextualize their free text comments in a compliant manner can’t be reiterated enough.