Addressing Challenges in the Pharmaceutical Industry with AI: Where...
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Addressing Challenges in the Pharmaceutical Industry with AI: Where to Play and How to Win

By Dr. Eddie Guzdar, Physician, Medical Director of Neuroscience, Sanofi Genzyme UK and Ireland

Dr. Eddie Guzdar, Physician, Medical Director of Neuroscience, Sanofi Genzyme UK and Ireland

The traditional simplified pharmaceutical business model of developing medicines and then marketing them to physicians has already changed, and this will evolve even further with artificial intelligence (AI) related digital technology.

Our external healthcare environment is rapidly changing due to fundamentally three reasons:

• Healthcare systems are increasing in complexity: There are now many new stakeholders, beyond the physician, of increasing influence on prescribing decisions.

• Exponential growth in treatment options: While it is very good for patients, this does further exacerbate the underlying cost pressures and finite resources within the healthcare system. It also creates an environment of too much choice in treatment options that it increases demand from prescribers to seek even more evidence of differentiation between medicines.

• Accessibility of information and patient empowerment— The digital age has effectively exposed society to the full realm of science and medical information via the internet. This has empowered patients to seek out answers for themselves and be far more engaged and influential in their health. However, the relevance and meaning of all this information to the reader often remains a challenge.

"The reality is, we only have finite time and resources, so we need to focus on a few big-ticket items rather than trying to do everything"

Pharmaceutical companies are already dealing with these changes by building a more in-depth evidence base for their medicines in a race to be the first to market and then to maintain a competitive and differentiated position, so they can continually show the value proposition of their medicines. Internally, the traditional field force focused on product sales has also evolved into a plethora of different types of roles to reflect product sales, service evaluation, scientific exchange, and patient services. The aim is to gradually move the company from “selling product” to “delivering healthcare solutions.”

Where to Play? Areas where AI digital developments will drive value for pharma companies

Personalization of information for external stakeholders: For both patient and physician being able to provide tailored information that is relevant to their needs is invaluable in an environment where they currently receive endless generic information from a variety of sources. Marketing and sales can utilize advanced analytics to understand prescribing behavior and potential patient profiles, enabling more precise targeting.

Research and Development: Increasing the speed of trial completion and lowering their overall cost is likely to lead to significant competitive advantage and shareholder value while at the same time, increasing the speed of innovation for patients. AI is being explored in the areas of novel candidate creation by using machine learning to extract data from experimental compounds and then providing this data to researchers. It has also found roles in improving clinical trial recruitment, which is currently a major barrier to trial progression.

Business operations: There are an enormous range of internal activities within any company that could be made more efficient by either automation of simple tasks or augmenting human capability. Examples of this are already being seen in the medical function via chatbots for medical information teams and adverse event report processing in pharmacovigilance. This is simply the tip of an iceberg of opportunity.

How to win?

There is a clear recognition of the importance of digital technologies in the pharmaceutical industry now, but the path to success often seems fraught with obstacles. How can we create the right environment for success?

• Create the right culture of innovation: Develop a sense of urgency which allows for rapid pilots, build senior buy-in early to drive through alignment across the organization, and importantly, de-risk worries of failure. It should be ok to fail and for it not to be perceived as a negative. Otherwise, the risk of failure and being associated with it prevents any form of decision-making within a large pharmaceutical organization, resulting in a constant conservative approach.

• Build necessary people capabilities: Focus and hire the right people who have a diverse set of skills. Specialization is important in some technical areas but otherwise, a broader understanding of different functions is what is really needed. Teams often have skill sets that are either too digital/tech or pharmaceutical-heavy. The right talent mix is key to ensure you are identifying the right problems and developing valuable solutions. Build incentive plans for the long term in your digital team. People in large pharmaceutical companies move roles frequently, and this can create instability or a short-term approach, neither of which help digital transformation.

• Focus on a few projects at a time: The challenge with digital technology and AI is the enormous hype and their potential to solve everything. On the other hand, there can also be a level of skepticism within organizations around AI. The range of use cases risks a lack of focus. The reality is, we only have finite time and resources, so we need to focus on a few big-ticket items rather than trying to do everything. Go for the quick wins first, build internal momentum and buy-in, and then go after the harder problems.

• Collaborate externally: Pharmaceutical companies do not have all the expertise internally. Partner with the right external companies who bring the expertise you do not have. Build partnerships carefully, ensuring common goals.

Finally, if you get lost along the journey and are wondering what impact is really being made, then take yourself back to the problem, not the technology, and ask yourself:

1. What am I trying to solve?

2. Does it need solving?

3. Is digital technology & AI the right solution?

The challenges tend to occur when we have made our project around the technology rather than the actual problem for which we need a solution.  

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