Medical Affairs teams need to be able to transfer information into well understood, viable data that translates to insights
This is the third in a three-part perspective on the modernization of medical affairs through applications of artificial intelligence and machine learning to drive decisions and outcomes. Read part one, “AI to Align Field Engagements With Strategy in Medical Affairs,” and part two, “How AI Meets an Evolving Need for KPIs in Medical Affairs Teams.”
Medical affairs professionals are an instrumental part of the multi-year, complex process that leads to the successful launch of new pharmaceutical drugs. Their crucial role is only increasing in importance as new digital technologies and market demands raise the stakes of what’s possible for new cures and treatments.
Comprised of scientific and medical professionals, medical affairs teams are positioned between external and internal stakeholders—including doctors, executives, and researchers. Prized for their scientific-knowledge, ability to parse real-world evidence (RWE), and established credibility, these teams are now increasingly trusted with guiding the 10-year or more development process that brings a new drug to market.
And the new age of digital technology is unleashing new potential for them to be even more impactful.
Science is at the heart of the pharmaceutical industry, and so is the importance of well understood data. Medical affairs teams want and need better tools to track their interactions, analyze third party sources, and turn every email, meeting, KOL discussion, and trial report into usable and valuable data. The momentum in the industry is clear: The winners will be the ones able to turn stakeholder learnings and interactions into viable data that can be used to draw meaningful insights.
Today, new AI technologies exist and do exactly that.
The day-to-day practices of medical affairs teams revolve around a multitude of interactions between team leaders, KOLs, sales, scientists, researchers, and executives. Further, these interactions take place in a variety of channels: email, calls, face-to-face, conferences, etc.
On their own, each interaction with a doctor or lecture at a conference may not be of strategic importance–but this complex web of stakeholders and information can yield valuable insights when taken together. The collection and normalization of this interaction data has up until now been incredibly difficult to access and disseminate.
Each time Dr. Jones gets in touch with an MSL to ask a question about a trial medication or discuss recent research, this scientific exchange represents capturable, valuable information:
With a tuned data analytics platform, not only are formerly siloed interactions captured as streamlined data, but that data is analyzed in real time to add layers of intelligence to transform data into actionable information. That information can then be communicated across the organization to close knowledge gaps and prompt actions.
When medical affairs teams prioritize data capture, they can use AI technology to ingest and analyze data from a variety of vectors and create relational quantitative relationships. By enhancing the ability of medical affairs teams to turn interactions into data, advanced applications—namely, rMark Bio’s Cue—enable more metric and tracking ability than ever before.
And here we get to a central, burning question that has sat at the heart of the expanding role of medical affairs: How do you measure success?
It’s a question that has grown in significance as companies have realized a barrier in their ability to quantify the value of KOL outcomes related to MSL activity. What specific activities and interactions do MSLs engage in? And what are the KOL outcome behaviors of those activities?
Many teams rely on basic KOL profiling and just a few bare bones, quantitative interaction metrics. They may know how many engagements a particular KOL has had with an MSL over the course of the year, but they generally lack the ability to tie those engagements and activity to actual behavioral outcomes. It’s an incomplete picture that captures surface, transactional data and lacks qualitative measurements.
The lack of objective, qualitative measurement leaves an information vacuum that is currently filled by an MSL’s inference or value judgement about any given exchange or activity. With Cue, instead of just relying on individual beliefs, you can objectively assess behaviors and outcomes. The platform provides the ability to aggregate KOL behaviors in a single profile, making it much easier to track their individual activity: What speaking engagements have they had? What content are the publishing on social media? What research are they doing or interested in doing?
Connecting the beliefs, gathered through MSL-KOL interactions with observed data points (scientific engagements – publication, trials, etc.) in one intelligent platform, yields an amalgamated dashboard of outcomes that both measure the impact of the field medical’s activities and act as a compass to guide medical strategy to improve the impact of a therapy on patients.
Until now, medical affairs teams have been swimming in murky data waters. With so many stakeholders, interaction points, technology, and data debt built up over the years, pharmaceutical companies have historically struggled to get one clear, accurate, complete picture of the stories contained within their data.
There are endless backlogs and reams of unstructured data that need to be normalized and analyzed. One of Cue’s most significant abilities is the application of natural language processing to the mountains of conversational exchanges buried within the servers. But MSL communications and interactions are not the only valuable data source to pay attention to; MA teams need to be able to pull in and synthesize many different types and formats of third party data including text, numbers, prescription records, compliance rules, regulations, and recurring research among others.
In fact, pharmaceutical companies can expect that a substantial amount of the data needed to accurately guide the long-tailed, complex process of drug development will originate outside of the company. Medical affairs teams will need to be outfitted with the ability to quickly and effectively research many data streams and will need to conduct fit-for-purpose evidence generation—for example, by combining real-world evidence with machine learning and advanced analytics—while still remaining nimble enough to respond to data generated externally. Is the data being generated having an impact on patient care? Do KOL’s believe the data is impactful? Is this belief supported by demonstrated behaviors? (e.g., improved clinical endpoints? Better or more coordinated care?
In this context, medical affairs will need to build proactive and responsive capabilities, and to develop deep expertise that is continuously refreshed.
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