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Leveraging data and AI for better results in pharmaceutical development.

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.

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:

  • How many times has this KOL engaged?

  • What is their specialty?

  • What is the last journal article they published?

  • Who else has asked a similar question?

  • Are there observable geographic or biological trends that fit?

  • Does this information validate or inform the medical strategy to improve patient outcomes?

  • Will it impact patient access?

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.

Using interaction data to link activities to outcomes

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.

Tying the whole picture together.

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.

Author: Robert Eubanks, Erik Brown, Jason Smith (rMark Bio), Mike Abbadessa (rMark Bio)

Published:June 23, 2020

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