Medical Insights Management
Successfully Extracting Value and Meaning From Insights
There are many customers that we speak with who regularly take on the task of looking at a bunch of insights, making sense of it all, identifying key trends & issues, creating a report, and communicating all of that to others.
If you’re one of those individuals, respect! Because extracting meaning and value from the mass of insights coming into your organisation is not just incredibly important, it’s also a significant task in itself.
Typically such a task may start by identifying a group of insights from a specific timeframe, a particular congress, etc. Once you have filtered down these target insights, then the fun (hard part) starts.
Making sense & extracting value from these insights is different from just ‘analytics’. Analytics is often just about graphs and visuals which tell you about the insights, but don’t necessarily give you the tools to extract meaning and value from those insights. Analytics can tell you incredibly important information about the nature of those insights, the sources, and even the context around which they were captured.
But to truly extract meaning and value from your insights you have to identify potential trends amongst all the insights, find repeating insights that occur more than once, look for signals worth exploring further, match insights to your strategic objectives, identify completely new ‘never seen before’ insights and much more.
Manually scrolling through a list of insights to extract meaning and value is important, but often unsustainable in the long-term given the speed at which insights come in and the volume of those insights. This is further compounded by the growing focus of our customers to collect insights from multiple channels, not just Field Medicine/MSLs. When you start factoring in insights from advisory boards, medical information, and even patient discussions, the sheer volume and scale of analysis required becomes apparent.
The use of AI and machine algorithms is therefore essential to help support such tasks. From enabling you to query insights using a free-text language model, to automatically identifying potential trends suitable for further review, machines can help to dramatically increase the efficiency of extraction and reduce the human burden.
Product development, engagement strategy, and data communication are just three areas where insights can transform your behaviour. And in all these three examples, human intervention is of course critical – the machine doesn’t know your strategic imperatives, nor what your organisation is thinking at that moment when it comes to where future product development opportunities lie.
Extracting meaning and value from your insights is therefore best done as a balancing act – the raw power and analytical prowess of machines overseen by the critical evaluation of the human.
That’s why our X-Fly insights management platform helps you create this balance by integrating machine capabilities right into the heart of your insights workflow, helping you to capture, analyse, and extract meaning from insights.
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Keywords: X-Fly, insights management platform, insight strategy, medical insights, field medicine