The pharmaceutical industry is rapidly evolving its approach to strategic planning and decision-making. At the heart of this transformation is the role of medical affairs, which is undergoing something of a rebrand as it moves from supporting function to strategic partner within the broader pharma organization.
According to a report by McKinsey & Company, the medical affairs function is on track to rise in importance, becoming the third strategic pillar alongside the research and development (R&D) and commercial functions by 2025.
Such a restructuring highlights the increased importance of evidence-based decision-making, the bridging of clinical data to commercial goals, and the vital role of integrating patient insights into drug development and marketing processes.
However, this elevated status comes with the paramount challenge of data collection and management alongside the emergence of artificial intelligence (AI) and patients’ demand for a seat at the table. For the McKinsey vision to manifest, medical affairs teams must demonstrate clear value through precisely handling data and effective incorporation of patient insights. This is easier said than done, especially considering the complexity of life sciences companies and their historical reluctance to change.
Pharma’s Data Dichotomy
Without a doubt, pharma’s primary mission is to create and provide treatments for patients. While noble and necessary, this mission does not automatically align pharma teams with the sophisticated IT expertise required for modern data management and analytics.
This dichotomy becomes even more stark as AI plays a larger role in the industry, whether making sense of clinical trial data or distilling unstructured data into actionable insights.
The challenge, therefore, is not just about handling vast volumes of data but about ensuring that this data is leveraged effectively to guide the decision-making process.
Data Management Pitfalls in Medical Affairs
Medical affairs teams can run into to several pitfalls when it comes to data collection and management:
Multiple data sources and siloed storage. With various teams conducting research, publishing findings, and engaging with experts and patients, data can come from multiple and fragmented sources. Without a centralized system to integrate these data points and observations into insights, there’s a risk of duplication, contradictions, or overlooked data. Research by Reuters shows that just 9% of medical affairs teams use most or all of their data to generate insights.
Lack of technical experience. Medical professionals excel in their specific fields of expertise, but typically, they are not well versed in advanced technologies such as generative AI. This lack of understanding may lead to resistance or wariness of new technology. According to research published in Pharmaceutics, “acquiring a proficient workforce is a prerequisite” to harnessing the potential of AI at every stage of the drug development and delivery process.
Limited bandwidth and competing priorities. Many medical affairs teams spend hundreds of hours per month handling and analyzing large datasets. Without dedicated analytical personnel, teams can struggle to analyze trends and patterns in their data to draw meaningful insights in a timely and efficient manner. This may contribute to sporadic reporting and sharing of insights, causing companies to miss launch goals and lose to competing products.
Fragmented IT infrastructure. Pharmaceutical companies may not be IT centric by design or not have alignment on which tools and solutions are used across different teams. This can limit the capability for efficient data storage, retrieval, and analysis, leading to slow processes and missed opportunities.
Regulatory concerns. Data regulations are increasingly stringent, and managing patient data includes the challenge of ensuring compliance. A slip here can result in legal issues and harm the company’s reputation. Generative AI tools are widely available for general use, and many pharma companies have placed strict limits or prohibited their use outright as the industry’s discussion of AI evolves. For companies to take advantage of the power of AI solutions, they must thoughtfully create guidance that enables safe use.
The Path Forward
For medical affairs to secure its position as the third pillar in pharma strategy, addressing these pitfalls is essential. Collaborations with IT experts, investing in dedicated data teams, continuous training, and emphasizing the importance of a centralized data management system can provide potential solutions.
Moreover, it’s not just about managing data but interpreting it in a way that aligns with the company’s goals and patient needs. Implementing advanced analytics, machine learning, and AI can help transform raw data into actionable insights—the “ah ha” moments that lead to innovation.
In conclusion, the pharmaceutical industry is on the brink of a strategic shift, with medical affairs playing a pivotal role. However, this rise in stature brings with it the challenges of data management. Addressing these challenges head-on is crucial if medical affairs is to effectively guide pharmaceutical strategy in the years to come.
