AI is remaking health care – and that includes gene-based precision medicine, among other types of rapid advances in treatment. The growing field of GenAI, or genetic AI, uses advanced algorithms based on machine learning and other systems to match up molecules with conditions in patient groups. These algorithms ferret out which molecules will be most useful to which patients – and provide pharmaceutical companies with the data they need to develop new, life-saving drugs. Beyond the development of the drug itself, this data enables pharmaceutical companies to identify potential drug targets more accurately, predict patient responses to treatments, and develop personalized therapies tailored to individuals’ genetic profiles.
As part of this GenAI development process, pharmaceutical companies collect huge amounts of data – about drugs, interactions, patient conditions and outcomes, demographic data of patient populations, how drugs are used by healthcare professionals (HCPs), genetic data for patient populations, and much more. This data can be useful not only for developing those drugs – but for enabling sales teams to present those drugs to HCPs and other customers, ensuring that the patients who need these drugs receive them in a timely manner.
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Drugs are among the most difficult products to sell today. Teams need to be able to discuss the expected effects, and side effects, of the drugs they are selling; how those drugs interact with other products; why a specific drug is better than a competing one; how a HCP’s patient population is most likely to use a drug; regulations that could affect how a drug is used; and much more. For teams selling precision drugs made with Gen AI, which typically have smaller potential audiences due to their tailored nature, this knowledge and data is especially critical.
That is why sales teams at companies developing precision drugs should take advantage of data and research from the company’s development process in addition to other sources. Much of the data they will need to develop messaging, presentations, and information about their products and who will most benefit from it are right there in the data that was gathered for drug development. Federal regulations require an extremely high level of detail for genetic-based research, with data even far more detailed than sales teams require. With the data right there in company databases, all sales teams have to do is develop a way to query those databases to ferret out the information they need for their messaging, customer outreach, and presentations.
Other critical data for a successful commercialization and sales campaign comes from HCP preferences, including what types of patients they serve, what risks or side-effects those patients may be at risk for, what drugs those patients currently use, how HCPs treat patients, the methods they use, the beliefs in different practices of treatment and more. The amount of data available is vast, complex and constantly changing. AI is essential in order to make sense of it. AI algorithms can analyze this data and ensure that changes in the data are included in analyses as they occur, and help pharma companies to develop methods to reach out to the HCPs most likely to prescribe these specialized precision drugs. Unlike in the past, when pharma companies would simply target the largest practices, this data allows them to target the most relevant practices, regardless of their size, making the marketing and sales process much more efficient.
Here, too, recent tech development can help sales teams; instead of having to search out experts who can write algorithms that can query the data – an almost impossible task, given the shortage of AI experts – teams can partner with AI companies that offer platforms that will enable teams to make their own queries, based on specific needs and contexts. With a platform, sales teams can automatically prepare their sales plans, including approach, messaging, and presentations with a few button-clicks. The AI-driven platform will analyze all the relevant data and provide specific direction on the various stages of the sales journey – as well as provide guidance on how to deal with objections.
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Sales teams can also use these AI platforms with user-friendly interfaces by themselves, removing the need to hire outside consultants in order to bring in data-driven strategies. With such a platform, teams can get the guidance they need as they need it – and exactly as they need it, because they will be most familiar with the issues they need to deal with. Consultants, who deal with multiple accounts, will be far less familiar with issues specific to a certain brand. The increasing availability of automation features on such platforms also make these tools even easier for pharma sales teams to use. When using such platforms, pharma sales teams will be fully in control of their strategy and execution. Teams will thus be able to act in a more dynamic, accurate, and timely manner.
As GenAI and other technologies advance the speed of development and the precision of drugs, pharma commercial teams need to make sure they are harvesting the right data with the right AI tools in order to ultimately reach the right patients. The combination of internal data from advanced drug development processes, outside data from HCPs and a powerful AI-driven analysis platform that sales teams can operate themselves, are key for today’s successful commercialization of the newest and most advanced drugs.
Photo: Galeanu Mihai, Getty Images
Doron Aspitz, Founder and CEO of Verix, is a passionate, visionary executive with 20+ years of leadership experience in disruptive, fast-growing technology companies in dynamic markets. He specializes in building and expanding early-stage companies with a customer-centric approach. As the CEO of Verix for the past 15 years, it has been his mission to help pharmaceutical and biotechnology companies use data to provide better, more comprehensive care to millions of patients worldwide. As a tribute to his contributions to the industry, Doron was awarded “Pioneer of the Year” by Call Center Magazine.
