Predictive analytics – the ability to extract information from data and use it to predict trends and behavioural patterns – has been harnessed by many industries, from retail giants to insurance and technology companies. Indeed, Amazon's product recommendation engine and Microsoft's Cortana digital assistant are classic, everyday examples of smart software programmes being used to predict what may be of interest to customers.

While pharma is very well versed in performing clinical trials, generating data, and then disseminating it, the industry has yet to fully realise the potential of predictive analytics to take its business to the next level. However, advances in data integration and data mining, as well as metrics analysis technologies, mean that the often-overwhelming array of medical and scientific data that exists across the globe, and that is constantly being created, can now be filtered, aggregated, analysed and presented in more meaningful ways.

Indeed, instead of being viewed in isolation from each other as ‘snapshots’ or ‘points in time’, data collections can now be contextualised to enable the analysis of patterns and trends, and to make relative assessments on what they tell pharma about how things may change in the future.  This could potentially bring huge competitive benefits to pharma. For example, predictive analytics could help pharma more effectively position new molecules for entry into the market based on their efficacy and safety profile to better serve prescribers and patients.

Keeping abreast of the latest thinking and discussions in relevant therapy areas and identifying Key Opinion Leaders (KOLs) is key to helping pharma stay ahead of the game. Thanks to new tools that analyse the quantity and quality of scientific dissemination on a company or its products, it is now possible for pharma to assess its own ‘share of scientific voice’ and that of its competitors. This helps pharma to gauge not only how well it is doing competitively, but also where its scientific information can best be disseminated in an effort to reach those who can best use it. It is also a powerful way to track new discoveries, research trends and shifts in the market place, so that pharma can understand what’s happening and adjust its future objectives and behaviours accordingly.

Being able to analyse share of scientific voice and overall therapeutic area landscape also means that pharma can seek out the most appropriate investigators for research and choose the best journals for publications with greater accuracy. Indeed, pharma can now accurately identify the right scientific leaders and define how best to engage with them based on their body of work and specific research interests.

Predictive insights help medical affairs teams work more effectively with marketing departments, since they enable them to provide highly comprehensive data that can aid commercialisation decisions on a product ahead of its launch.

Predictive analytics will be particularly valuable for pharma as the industry heads towards the manufacture of specialty drugs, rather than blockbuster drugs, which will require a more targeted commercialisation. By having access to forward-thinking analytic data, pharma can take a more strategic approach to its sales and marketing strategy, and fulfil unmet patient and prescriber needs by ensuring that it is disseminating the science to the right people.

Having access to this level of data also enables pharma to identify and model best practices for a new product launch. By looking at the scientific meetings where competitors are focused prior to their own launch, pharma can better position its information and improve upon past activities of competitors.

Applying predictive analytics to accurately predict what is likely to be of interest to key prescribers and KOLs can also help tremendously with product positioning and enhance early adoption. Having the right information for the right clinician or KOL at the right time is very valuable especially in light of declining pharma salesforce numbers and difficulty with access to clinicians.

In summary, predictive analytics have huge potential for Medical Affairs and other teams within pharma, enabling them to quantify and measure the impact of their efforts on medicine and the market. In turn, they can also use these measures to predict how and where they should be concentrating their efforts for dissemination of scientific information which will help clinicians use their products more effectively, ultimately helping patients.  This can give companies a strong competitive edge, enabling them to streamline and accelerate the decisions that guide the discovery, development, approval and commercialization of a new drug.

Joseph B. Laudano is vice president, medical affairs strategy for Medmeme