By engaging with the right medical experts as early as possible in a drug’s lifecycle, pharmaceutical companies can gain enduring competitive edge. However, despite medical affairs departments continually undergoing profound change and growth, studies show 50% of new drug launches fail to meet company expectations. Could embracing AI in life sciences help to tackle this trend?
Medical Science Liaisons (MSLs) are highly trained experts who are in regular contact both with healthcare practitioners (HCPs) who treat patients on a daily basis and leading academic minds in their field. They also attend medical conferences and read scientific publications to stay up to date. These professionals are incredibly valuable - there just aren’t enough of them to go around!
MSLs learn about the successes, issues and challenges experienced by HCPs when treating patients and conducting clinical trials. Historically, their role has revolved around educating and supporting Key Opinion leaders (KOLs) but mobile devices, big data, and artificial intelligence are poised to transform this.
Increasingly, MSLs are perfectly positioned to gather information in the field that will facilitate decision making throughout the entire drug development pipeline.
Dealing with data
MSLs in the field collect information in many formats, from lab reports to x-ray images and physicians' notes. However, none of this is useful unless it can be turned into actionable insights in a timely fashion.
This time-consuming task has historically been a major barrier to the productivity of MSLs, who suffer from inadequate access to internal data science resources.
Until now, AI in life sciences hasn't typically extended to give MSLs access to machine learning capabilities. However, Mind Foundry is about to change that.
The benefits of AI in life sciences
With the emergence of accessible AI in life sciences, MSLs can transform their output as well as the breadth and depth of their insights, both for internal and external dialogue and decision-making.
AI-powered clinical data reviews will unlock insights as to how clinical data is received, how well a drug meets unmet clinical needs and whether any other criteria has the potential to cut costs and support with drug reimbursement.
HCPs and MSLs can explore real-world insights with regard to safety concerns and how they might be addressed, and consider how influential factors such as age, gender, race and co-morbidities may impact treatment regimes and prescription patterns.
Keeping human factors front and centre will remain a key success factor in successful medical science liaison engagement . However, leveraging the power of AI in life sciences by upskilling, equipping and augmenting scarce and valuable MSL resources with accessible ‘citizen’ machine learning tools can transform the return on investment for pharmaceutical companies in this critical area.
