How AI Can Help You Find the Right Key Opinion Leader (KOL)

Identification of Key Opinion Leaders (KOLs) is crucial for businesses seeking to establish partnerships, collaborate, or leverage the KOL’s influence for marketing and promotional activities. Traditionally, the process of identifying KOLs has been time-consuming and has required a significant amount of manual effort.

But how exactly are these technologies improving the efficiency of KOL mapping? This article delves into the process of KOL mapping and how modern technology has the potential to make it simple and efficient.

In today’s highly competitive pharmaceutical market, gaining credibility and trust for products like new drugs can be daunting. With so many regulatory hurdles and an ever-changing healthcare landscape, pharma companies must leverage the expertise of industry opinion leaders to help shape their products and reach their target market.

Pharma brand advocates are also known as Key Opinion Leaders (KOLs). These are often leading physicians or researchers with a substantial audience who command respect for their expertise in a specific area of the pharmaceutical industry. They are a valuable resource in healthcare as they provide sage opinions on activities that seek to expand market share or launch new industry-leading products.
How can a pharmaceutical company find the best KOLs to collaborate with for specific campaigns or product categories?

The process of sourcing KOLs is known as ‘KOL Mapping’. This is a crucial tool for pharmaceutical or medical device teams to effectively identify the most suitable key opinion leader for their project. This process involves a quantitative approach that aims to locate KOLs at regional, national, and global levels. In parallel, it enables pharma teams to obtain a wealth of knowledge that targets the most valuable industry voices for specific tasks.

In pre-internet days, sourcing a KOL was a difficult and time-consuming affair. It required detailed research by the Medical Affairs Team, which would scour journals, device registers, and conference attendees for suitable KOL candidates.

Clearly, this had knock-on implications for getting new KOL-endorsed products expeditiously to market ahead of the competition.

Even after the advent of internet search, collecting information about the right influencers is tricky. Thousands of KOLs publishing research papers weekly in various journals, the amount of data from electronic medical records, claims data, and daily practice was too overwhelming for any human to analyse.

It becomes even more complex when researchers present their work in obscure journals, over multiple websites, in a foreign language, or in non-standard storage formats.

So even with the advantages of basic internet resources, it’s still easy to miss suitable candidates.

Can AI be utilized to make data analysis in medical affairs more efficient and simpler? Let’s explore the potential impact of AI on medical affairs operations and data analysis.

The potential use of AI and machine learning makes the KOL mapping process considerably more straightforward. The technology can see through data noise and search using simple rules that cut to the heart of a KOL mapping exercise.

AI has the potential to be a vital tool for identifying Key Opinion Leaders (KOLs) across a range of industries. It may evaluate information from networks, publications, and social media to find people who are very influential and knowledgeable in a given area. Artificial intelligence (AI) can deliver insights that are challenging or time-consuming to gather manually by utilising natural language processing, sentiment analysis, and network analysis. For companies looking to build partnerships or collaborations or use the KOL’s influence for marketing and promotional initiatives, identifying KOLs can be useful. Fortunately, this procedure can now be automated and streamlined thanks to developments in artificial intelligence (AI). In order to ensure that the results are accurate and appropriate to the particular business or subject, AI should be used in conjunction with human experience.

The process starts with a Medical Science Liaison asking, Who is my ideal KOL? This approach clarifies the requirements that help identify industry leaders with the optimum audience credibility. It also promotes a much better understanding of the potential KOL’s areas of expertise.

An appropriately configured AI search applies some of the following skills to efficiently identify KOLs:

AI can use social media engagement and activity analysis to find people who are influential in a given profession or industry and have a significant following. Analyzing followers, engagement rates, content, and sentiment analysis are some examples of this. Using natural language processing (NLP) algorithms to evaluate text-based data, such as posts on social networks, is another method for identifying people who are frequently cited in talks about a
specific subject or industry. Machine learning techniques can be used by generative AI to find patterns in data and identify people who are likely to be influential.

The language used in articles, interviews, and speeches can be analysed by AI to find people who are regularly acknowledged as experts in a given topic. This may entail sentiment analysis, topic modelling, and keyword analysis. Machine learning is capable of handling huge quantities of information from numerous sources quickly. A possible AI can easily scan journals, social media, academic papers, and conference proceedings to find links among various concepts and subjects and assess the general suitability of a KOL.

Consider a scenario where the product is intended for a certain nation or region. In this situation, the AI can locate a well-known expert in this region who will contribute to the project’s strength and credibility that can be recognised. Those that appear genuine but may have stated opinions that would reflect negatively on the legitimacy of their support may be excluded by the AI. By comparing the search results with the databases of certified healthcare professionals, the potential AI confirms that the targets have the requisite qualifications to ensure the suitability of the potential KOL.

In order to find people who are well-connected and have a big impact on the community, AI can study the networks of people in a given field. Analyzing connections, partnerships, and co-authorships can be part of this. By utilising predictive modelling to identify individuals likely to become influential in the future, generative AI can help in the identification of KOLs. This may be achieved by identifying people who are on a path to being highly influential in their profession through data analysis such as publication history, conference attendance,
and engagement on social networks.

In general, generative AI can help in the discovery of KOLs by evaluating vast volumes of data and spotting emerging trends and patterns that human analysts would not instantly notice. Generative AI can assist in identifying people who are most likely to have an important impact on their sector and offer helpful insights for companies and organisations looking to cooperate with KOLs by utilising cutting-edge algorithms and predictive modelling techniques. To ensure that the results are exact and useful to the particular business or subject, AI should be used in close collaboration with human experience.

Modern AI technology can significantly accelerate how pharmaceutical organisations can identify appropriate thought leaders for their marketing and product development processes. In addition, the technology can help Medical Science Liaisons find and approach only the best-qualified and most respected advocates for any given initiative, saving time and money in the process.

When used expeditiously, MSLs can benefit from trending tech solutions like generative AI and machine learning, which reduce the effort involved in sourcing the most suitable KOL candidates for marketing and promotional initiatives. But it’s important to recognise that no technology can replace a Medical Science Liaison’s input. It is essential to train these tools in the particular domain and equip them with correct, regulated data in order to deploy generative AI technologies to optimize the process of identifying and evaluating possible candidates. This enables users to take advantage of technology to shorten the time and labour needed to find and assess qualified applicants.

While potential AI and machine learning solutions can provide valuable data, identify KOLs and supplement the firm’s knowledge about a subject area, they cannot and should not replace a Medical Science Liaison’s experience. This can be especially relevant around issues such as managing the ethics, data privacy, bias, and transparency that an AI-based search may occasionally breach.
In this and many other regards, the instincts, knowledge, and relationship-building skills of a Medical Science Liaison remain indispensable.