AI in Life Sciences: Faster Drug Development to Personalised Medicine

When we consider the role of artificial intelligence in life sciences, it already has a proven track record as an effective tool to support pharmaceutical research. However, companies are also moving fast to explore the transformational impact it can have on their commercial operations, such as sales and marketing.

CEOs cite "mountains of data, molehills of intelligence" as a key challenge faced in today's pharmaceutical sector. Despite plenty of raw data being created, companies are lacking actionable insight. With AI technology starting to make earlier diagnosis and safer treatment possible, can it also augment the performance of marketing and sales departments?

Artificial intelligence in life sciences can go far beyond the lab

Healthcare policymakers and payers are increasingly mandating or influencing what doctors can prescribe. As treatment protocols replace individual physician prescribing decisions, Pharma’s target audience is also becoming more consolidated and more powerful, with profound implications for sales and marketing models.

According to Accenture, artificial intelligence in life sciences can have a dramatic impact, increasing labor productivity in the sector by up to 40%. This is exactly the sort of boost that the industry needs if it is to accelerate time to market and address the key issues of market access, pricing, accurate KOL targeting and salesforce productivity.

We are seeing a move to clinical-based formularies throughout the developed world, with increasing emphasis on the value of Pharmaceuticals. In the UK, the National Institute for Health and Care Excellence regularly recommends high prices be declined, citing cost to efficacy. Health plans and employers alike are increasingly looking for head-to-head and comparative effectiveness studies as well as real-world evidence to make formulary decisions, particularly around speciality therapies.

To combat the risk of significant revenue decline, pharmaceutical manufacturers need to determine pricing and marketing based on more than just recouping research investment and hitting profit goals. Analytics must include the effect of pricing on increasingly cost-conscious private and public payers, as well as the true value of the product.

Applying artificial intelligence in life sciences

Machine learning platforms, like Mind Foundry, can transform the ability of any life science company to mine big data for the right answers and influence formulary decisions and clinical guideline development.

For instance, machine learning can separate customers into highly specific segments, empowering sales teams to personalise their activity to a greater degree. For example, in addition to therapy areas, Key Opinion Leaders (KOLs) and Healthcare Professionals (HCPs) can be segmented based on factors including interest in new drugs, location and availability.

In addition, sales reps can benefit from artificial intelligence in life sciences in a more practical sense. Materials can be recommended based on a sales rep’s previous meetings with a customer and provided in an easy-to-access manner. For example, if a doctor has previously expressed an interest in diabetes drugs, the sales rep can be told to come equipped with information on relevant drugs, ensuring that personalised and impactful advice is available at the moment it is needed most.

Machine learning will not replace marketing operations teams and reps because it cannot think on its own.  However, teams that use machine learning and artificial intelligence in life sciences may reach markets and patients that others can't.