One of the intriguing things about AI in healthcare is the way it can be applied across the range of healthcare sectors and activities and impact the way stakeholders view and pursue their business.

For example, a recent article at PharmaExec by three industry veterans looks at the question of how AI, along with Natural Language Processing (NLP), should be viewed by pharmaceutical Medical Affairs teams, or those people whose job is to ensure that patient well-being is at the forefront of pharma marketing decisions, while also carrying out “the critical mission of informing clinical practice patterns to ultimately improve patient outcomes.”

Specifically, they ask, should medical affairs teams use AI and NLP as support tools or as tools to increase their companies’ advantage over competitors?  The answer, they say, is “both.”

In their view, the real trick is to “think about this from a return on investment (ROI) perspective. Where can you spend the least money and get the biggest bang for your buck out of deploying machine learning and AI?”

In response, they point to three key examples of how AI and NLP can be utilized throughout a pharma operation. 

First, there are medical information requests from customers. 

“The longer it takes to answer a question or information request, the more expensive that request gets,” they note. “Add into this the growing complexity of information requests due to the ever-expanding platforms for asking a question (phone, email, SMS, and eventually Tweets) plus the breadth of spoken languages serviced, and you have a regulatory requirement whose costs are going to accelerate over time. Given the heavy regulatory requirements around MedInfo, it’s not feasible or advisable to remove humans from this loop. Still, if you can make humans more efficient, you can keep the costs in line. An AI-powered MedInfo system built into your company’s workflow can accomplish this.”

Next, there’s the category of Medical Science Liaison (MSL) insights.  In their view, the MSL “role has the potential to dramatically and positively affect the perception of a drug in the marketplace by leveraging the data collected . . . (but). (t)oo often, the data gets lost in a company’s CRM system. . .  Effectively mining this data requires a Natural Language Processing (NLP) engine tuned to the condition being covered by the MSL.”

Finally, there’s what they call “content meta tagging.” In large pharma companies, they point out, “the more data you collect, the more likely it is to get siloed and leveraged only by the group that gathers it. This particular issue is a mix of AI and human expertise, where you can design and build taxonomies and tagging schemes that work across various functions within medical affairs and beyond. These taxonomies coupled with NLP and AI can access and enhance the content within a CRM system to allow companies to get as much value possible from the data they are collecting.”

In short, they say, at a time when proliferating information streams means more data for medical affairs teams to manager, the “judicious use of machine learning and AI can save money and time  . . .  and, in some cases, can help protect revenue streams.”