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Q&A With Philip Poulidis, CEO and Co-Founder of ODAIA


Poulidis discusses how AI is being used by the pharma industry for marketing and sales.

Philip Poulidis

Philip Poulidis
CEO and co-founder

(MDT:) How do you use AI to improve the efficiency of marketing in the pharma industry?

Poulidis: We take very complex data sets that exist in life sciences and we apply data science techniques, including elements of AI and machine learning, so that we can distill all of these disparate data sets that exist. This includes prescription transaction data, claims data, lab data, population, socioeconomic, and other sources of data. We then bring this information into our platform and inform the commercial targeting and segmentation for pharma companies so that they can get the medicines to the patients that need them in a timely manner without commercial or sales teams going through and doing a manual analysis of all of these data sets to find these patients and the doctors that are treating them.

(MDT:) What impact is this having on the day to day operations of sales teams?

Poulidis: Historically, and even today, many pharma companies on the commercial side use countless spreadsheets, databases, and other data sets that they license that they bring into the organization. For the most part, they derive historical and static insights from these data sets. They have consulting firms and internal analytics teams to sift through all of those data sets, which they try to come up with targeting and segmentation exercises to inform the sales and commercial efforts.

These are typically time consuming and highly ineffective. In the US alone, pharmaceutical companies spend $30 billion in sales and marketing, and $20 billion of that was efforts targeted directly at physicians. We take a very precise, methodical, and data-driven approach to identifying patients and doing the targeting and segmentation in real time. What we’ve seen is highly inefficient teams becoming more efficient, freeing up their time and resources. If we can take a little bit of that $20 billion that being spent on sales and marketing for physicians and bring that down a few percentage points, it could be applied to drug development and patient care.

(MDT:) What new strategies are being used?

Poulidis: We’ve got some customers that are introducing new therapies to the market and the goal with that is to quickly educate physicians on how to properly diagnose patients for the diseases those drugs treat. Our platform helps to quickly identify who the physicians are that need the sales and marketing teams need to be talking to. We do that by looking at claims data and historical prescribing transactions, but also precursor drugs that a patient may have prescribed that would suggest that the patient is a likely candidate for a new therapeutic based on their historical treatments. This can also include prior treatments, including surgeries.

We quickly identify who that treating physician is and inform the sales team to go and educate that physician on the therapy and the kinds of tests that can determine if a patient is an ideal candidate.

We also work with established brands that have been on the market for a while. For these, we look for new indications so we can help them segment and identify patients.

(MDT:) Is this similar to digital therapeutics?

Poulidis: No, we’re further along in the life cycle. We’re not part of the drug discovery or development component of it, or the delivery to patient. We follow the same ethos, though. We try to get treatments to the right patients at the right time, but we do so once those therapies are already, or about to be, in the market. We come in to make that process more efficient.

(MDT:) How can leveraging market intelligence and data help ensure that patients have access to treatments?

Poulidis: Over the past several decades, there have been advancements in precision medicine, along with the adoption of AI in drug discovery over the past decade. Not many advancements have been applied to the commercialization process. Specifically, I’m referencing the application the of advanced AI machine learning on large data sets to help drive the efficiencies in the commercialization process. That’s where we’re focused.

By accelerating the commercial process and making it more efficient and effective at getting the right drugs to the right people faster, that can save lives and improve health outcomes.

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