How AI Can Help Medical Science Liaisons Juggle Their Ever-Growing Responsibilities

Over the past decade, the role of Medical Affairs has expanded to impact the entire drug development continuum—from assessing unmet needs to drive early clinical development to communicating scientific data to key stakeholders to managing assets across pre-, peri-, and post-launch portfolio expansion efforts.

Traditionally, Medical Affairs has focused on cross-functional collaboration with commercial and R&D teams, medical communication, evidence generation, and dissemination. Field medical affairs representatives or medical science liaisons (MSLs) are responsible for driving bi-directional scientific exchange with key opinion leaders (KOLs). To be most effective, MSLs are expected to do more—capture more of the latest advances in respective therapy areas, ingest more nuanced scientific information on more complex therapies, communicate across more channels, coordinate with more internal teams, and bring back more insights to share with their organization.

It’s a lot to juggle.

The good news is many data sources are available to help inform decision-making. The bad news is these sources are rarely integrated. The biggest challenge is synthesizing all the different structured and unstructured data in real time so they can be analyzed for actionable insights and delivered to MSLs through a single, easy-to-use platform.

The solution may lie in another technology that is already being used successfully by other areas of the life sciences organization: artificial intelligence (AI).

New and Mounting Stresses on MSLs

Several new stressors are impacting the modern MSL.

First, the sheer quantity of drugs is increasing. In fact, the pharmaceutical industry’s development pipeline has reached an all-time high of 20,109 products under active development at the end of January 2022, an increase of 500 from 2021.1 And, on average, the FDA approved 60% more drugs between 2010-2019 than the yearly average over the previous decade.2

At the same time, therapeutics are increasingly complex as companies diversify into new modalities and broaden product specifications to emphasize device and software components. Likewise, a greater focus is being placed on developing novel, specialty drugs to treat chronic, complex, or rare conditions. Many specialty drugs are biologics (large-molecule drugs based on living cell lines) compared to simpler, small-molecule drugs based on chemical compounds more common in previous decades.

Between the number of new drugs in the pipeline and their increased complexity, MSLs are tasked with ingesting larger amounts of highly scientific information quickly to drive meaningful scientific exchange.

“MSLs generate a lot of valuable data themselves, often in the form of unstructured call notes, plus there are many sources of purchased data like real-world evidence, influencer data, clinical study data, and more,” explained Dan DeStefano, Global Medical Affairs, Digital Implementation Lead at GSK. “If you apply the right AI engines, you will get increasingly better insights. The key is to continually invest in these technologies and adopt a ‘product mindset’ rather than a ‘project mindset’—like the way software providers deliver periodic product enhancements. We need to keep evolving and improving.”

Second, MSLs must learn to strategically leverage various new engagement channels while coordinating with internal corporate medical functions and, where appropriate, commercial functions to meet HCP expectations for orchestrated interaction. At the same time, MSLs are being asked to help inform brand strategy with their learnings from talking to leading healthcare professionals.

But that’s just the beginning. MSLs are also now expected to play a bigger role in informing R&D strategy through coordinating investigator-initiated clinical trials, troubleshooting challenges at a clinical trial site with clinical science liaisons, and gathering insights to expand a company’s site and investigator network.

It’s a lot of balls to keep up in the air—and often without the right technology support. Some MSLs are stuck using the same old technology tools of old—yes, think spreadsheets—because the available tools do not meet their unique needs or are so siloed that they become more of a hindrance than a help. In an internal survey of MSLs, a top-20 global pharmaceutical company found that most are frustrated with all the fragmented data sources that they must track. Many MSLs resort to manually curating information from a myriad of third-party sources, including publications, clinical trial databases, conferences, grants, unmet needs in clinical care learned from KOLs, and clinical guidelines from medical societies—without the same analytics technology integrated into automation platforms.

“MSLs need to customize engagements based on an individual stakeholder’s needs and interests, and there are a wealth of tools, data, and advanced data analytics available to provide a 360-degree view of individual external experts,” said Emily Howman, Senior Medical Strategy Lead at Envision Pharma. “MSLs need to be able to leverage all the resources that are available to them to enhance their interactions with each KOL and the insights they bring back to the organization. AI can streamline the process to determine individual preferences and associated next-best-action recommendations based on a KOL’s activities, interests, and preferences.”

Out with the Old, In with the New

Empowering MSLs with software that can quickly aggregate, synthesize, and tailor data to meet the interest of each KOL relationship is now fundamental to success. “MSLs are key purveyors of scientific truth and viewed highly by HCPs/KOLs, and they are driven by their passion to help patients. Medical Affairs organizations should empower them with tools that make their jobs easier and maximize their impact,” wrote Sabita Sankar, PhD and Lana Feng, PhD in The MSL.

In addition to helping MSLs better personalize interactions with KOLs, the right technology can free up time and capacity to engage with a broader set of stakeholders—for instance, community physicians, digital opinion leaders, and emerging researchers in a therapeutic area. Advanced AI analytics platforms can turn the tide by providing MSLs with richer insights that foster important relationships with influential HCPs and, ultimately, bring the latest breakthrough treatments to patients faster.

Targeted content is also paramount in the virtual engagement era, where KOLs value MSLs who provide them with exactly the information they need at the right time. AI-powered platforms that can recommend the right content to deliver to KOLs on-demand is the key.

“The goal is to have very targeted content for KOLs,” said GSK’s DeStefano. “A continuously improving AI engine makes it dramatically faster, easier, and more effective to send the right content but there is also a human element that must be applied to create and select the best content for each medical expert.”

The Right Stuff

MSLs do not need another system designed for sales reps. They want a system that is right for them.

While customer relationship management (CRM) systems are relied on by thousands of pharmaceutical sales teams, they are not uniquely designed to automate the collection of the detailed scientific information MSLs need and then synthesize it into insights to coincide with the KOLs’ interests. For instance, MSLs need insights aligned with both science and branded products. This may include details about therapeutic areas and disease states, or clinical product data such as the right biomarkers for a drug.

“MSLs often use a CRM system to capture visit notes and other information in the field, but much of that information slips into a black hole,” explained DeStefano. “In order to harness KOLs’ valuable feedback, we need to combine it with other related data sources, apply AI, identify trends, and then feed that back to MSLs in the form of next-best-action recommendations—delivered in their daily workflows.”

Additionally, MSLs have the unique responsibility of communicating insights learned from KOLs to the rest of the company. For instance, many MSLs help identify potential physicians who would be ideal clinical trial investigators and can help cultivate that relationship—important, especially given the high competition for recruiting investigators to run clinical trials. The global pool of investigators is growing on average between 3.3-5.6% per year compared with an 80% increase in the number of clinical trials over the same period.3

MSLs also bring back information about therapy area unmet needs, indication expansion, and any workflow concerns that might impact the utilization of new therapies. A system that can streamline sharing this important information to the rest of the organization dramatically increases impact.

The Final Ball in the Air: Orchestrated Omnichannel Engagement

Orchestrated omnichannel engagement has grown into a buzz phrase for commercial teams ever since they started using digital communications channels to engage with doctors. The idea of orchestrating those communications has become increasingly important as not to overwhelm busy doctors now being sent emails, web invites, snail mail, and even text messages. For MSLs, this is even more significant as a lack of careful coordination of their company’s collective engagement with KOLs can mean the end of a valuable relationship years in the making.

AI platform technology can play a role here, too. Advanced AI-enabled technology can quickly assess a pharmaceutical company’s total outreach with an individual doctor, and then recommend the next best action in the best sequence—even if the next best action is no action at all. Omnichannel engagement with KOLs that’s well orchestrated across the entire organization may be the most significant way that AI-enabled platform technology can impact business goals.

Digital is one component of omnichannel that has grown in importance with the pandemic. “Many medical leaders are embracing digital following the forced adoption of digital channels during the height of COVID-19. Now, organizations recognize the value of all the data that can be collected this way and how important it is to turn it into insights,” concluded DeStefano. “Forward-thinking organizations will make this a top-down priority to invest in digital capabilities for their Medical Affairs organizations—this is essential for long-term success.”

MSLs are now at the forefront of operations, strategy, and execution. They are no longer “just” educating and informing doctors but helping to accelerate medical science innovation. Rather than spending their time trying to consolidate information from dozens of fragmented data sources, MSLs need to focus on cultivating new relationships with KOLs using AI-driven platforms that streamline information and generate reliable insights for greater success.


1. NSF, “Pharma Pipeline at an All-Time High,” from Informa Pharma R&D Annual Review 2022 (May 2022). See resource here.

2. Congressional Budget Office, “R&D in the Pharmaceutical Industry” by David Austin and Tamara Hayford (April 2021). See resource here.

3. Forbes, “How Technology Can Bring New, Diverse Investigators into Clinical Trials,” By Ryan Jones, a Forbes Council member (July 25, 2022). See resource here.

  • Deepak Patil

    Deepak Patil is a Senior Director of Medical Strategy at Aktana, where he leads the development of AI-driven intelligent engagement solutions for MSLs. He is passionate about novel applications of real-world data and artificial intelligence to drive decision-making in medical affairs and R&D. A physician by training, Deepak has over a decade of experience designing novel use cases of real-world data across the drug development continuum as a medical advisor at Sun Pharmaceuticals, strategy consultant at IQVIA and, most recently, as real-world data strategist at IBM Watson Health.


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