Companies throughout the life science industry are facing a thorny logistical challenge: how to best collect valuable insights from their Medical Service Liaisons (MSLs) and incorporate them into their strategic goals. Neglecting to identify important insights can have adverse consequences to a company’s bottom line, as almost half of recent drug launches have failed to fulfill financial expectations. While MSLs regularly engage in meaningful interactions with Key Opinion Leaders (KOLs), the technology used to keep track of what happens during those interactions has frustrating limitations.
Most Customer Relations Management (CRM) software that MSLs use to log data from their phone calls and meetings with KOLs are simply not built to extract the most important actionable insights. As such, the valuable details within a CRM system never end up being communicated to decision-makers who can apply the information to their strategic goals.
The latest emerging technology best suited for life science companies to overcome the limitations of their CRM software is Artificial Intelligence powered by Natural Language Processing (NLP). NLP-powered AI is designed to analyze large amounts of human-written text and detect patterns to pull out the most important insights.
Much of the data that sits idle in a CRM system is unstructured text, entered by an MSL into some form of comment box after KOL meetings. A tailored NLP program can sift through the tens of thousands of words that have been entered into the CRM and extract insights that would be impossible to uncover otherwise.
Without the aid of AI, humans can only do so much. For every minute an MSL spends writing a detailed note, a human reader must later devote the same amount of time (if not more) in order to read and contextualize the note’s significance. Additionally, the reader needs to have a sufficient level of expertise to be able to place the comment in the broader context of the field or specialty that was discussed.
To fully extract all the insights that lie within their CRM system’s unstructured text data, a company might very well need more experts to read through it than are actually available to do that kind of work. The rate of text data production by active MSLs will ultimately outpace the speed at which the support staff are able to analyze the data that’s produced.
An NLP-powered AI simplifies the process by going through large amounts of text quickly and zeroing in on the most important insights for review. Applications like those powered by Sorcero can do just that. “A computer program cannot completely replace the role of human judgment, but it can help optimize decision-makers’ time by highlighting which topics and trends require their attention,” said Sorcero CEO Dipanwita Das.
NLP employs an advanced understanding of human language, combined with industry-specific glossaries and definitions, to find where a life science team needs to be focusing their discussions. “Managers can use the insights generated by NLP to set priorities, based on information derived directly from their boots on the ground,” Das continued, “and spur more productive conversations among the very people providing those insights.”
“What’s more, effective identification of meaningful insights helps to keep MSLs engaged with a company’s strategic goals. No one wants to feel like they are wasting their time,” added Sean Smith, CMO of Sorcero and one of the minds behind their new insights solution.
An MSL who regularly leaves detailed notes in the CRM after every interaction with a KOL will start to get discouraged if his or her comments are not having an impact. With an NLP-powered AI, the most important insights generated by MSLs are communicated to decision-makers as part of an automated process. That provides an incentive for MSLs, who want to have an impact on the field in which they work, to dedicate more energy into capturing data from their meetings with KOLs.
This creates a virtuous cycle, argues Smith. “More unstructured text data is entered into the CRM, which allows the AI to draw out more insights, which encourages MSL to input more unstructured text into the CRM. The technology facilitates a greater level of cooperation in the development and application of the company’s products.”
Some companies attempt to draw actionable insights out of their CRMs without the use of AI, instead building structured polls into the system. The idea is that MSLs will respond to a series of multiple-choice questions in order to report what happened in a conversation with a KOL, and the responses will generate structured data that can be consolidated into charts and graphs used to detect trends. However, there are limitations to this approach.
“The interactions between MSLs and KOLs are often more complex than can be captured in a multiple-choice survey,” said Tim M, MSL at Ameda Pharma, when interviewed.
Even if a sufficiently nuanced questionnaire were constructed, it would likely need to be updated on a regular basis to reflect the latest trends in a rapidly changing industry and evolving medical science. All the time spent updating the CRM’s survey could easily erase whatever efficiency gains it produced in the first place.
“Two key advantages of using an NLP system to identify insights in a CRM are its self-sufficiency and its adaptability,” Das said. With Sorcero, MSLs can enter unexpected insights gained during a conversation with a KOL, having confidence that those insights will be captured and ultimately considered as part of the company’s larger strategy. “The AI doesn’t need to follow a script to detect patterns, and it does not need constant updating to remain relevant. The NLP technology adapts to the information that is entered into it, producing detailed insights in real-time, without being held back by survey bias,” Das concluded.
The current regime of life science CRMs leaves important strategic information on the table. An NLP analysis of a CRM’s unstructured text data has the potential to identify opportunities to expand patient pools, improve market access, as well as close education gaps for KOLs both nationally and regionally. It’s also an important part of how a company can manage what Deloitte refers to as “benefit-risk ratio,” proactively taking control of its corporate risk tolerance and communicating risks appropriately to help reduce their negative impact.
At a time when life science companies are looking for ways to use AI technology to foster more efficiency in product development, NLP shows great promise in its ability to augment the work of MSLs and incentivize greater engagement among experts.
To learn more about what Sorcero can do with the medical insights at any organization, request a demo today.