Field Medical/MSL Management, particularly in Europe, more often abandon the concept of “KOL Influence Networks”. The negative connotation of the term “Influence” and the practical challenges in determining “Influence” ( e.g. asking MSLs to fill CRM data fields with information on who is influencing whom, etc.) have always been pain points in the global “KOL Influence Network” analytics, both from an operational and compliance perspective.
On the other hand understanding the dynamics in the communities of practice is highly valuable information for MedAffairs/Field Medical in order to accelerate the diffusion of innovation, calling for a data-driven, objective and compliant approach to KOL Network Mapping.
Joint scientific activities as a foundation to determine KOL Networks
With the evolution of data collection and processing technology in KOL research, analyzing “Professional Interactions” (instead of “Influence”) based on publicly available and objective data points for joint activities, e.g. publishing papers together, being in the same advisory boards, etc. becomes the gold standard of KOL Network analytics, providing very powerful insights.
Looking at the type of professional interactions (respectively joint activities), you can distinguish between those that would most likely imply some kind of personal interaction versus those that have not necessarily established a personal connection.
The strongest indicator for professional interactions leading to a personal relationship is obviously the same current or past workplace. If two experts have worked e.g. in the same department of a hospital, it is highly likely that they know each other (well). As a practical application, this piece of information (together with other data analytics) can be used to identify so-called Emerging Experts/Rising Stars. Medical experts early in their career who work under an established KOL starting to contribute to their research projects and receiving the KOL’s support are interesting candidates to become KOLs themselves in the future.
Other examples for joint professional interactions most likely indicating a closer relationship between the KOLs are:
- Industry Advisory Boards
- Journal Editorial Boards
- Medical Congress Organizing Committees
- Medical Guideline Committees
- Professional Association Committees
- Patient Association Group Committees
It becomes obvious that joint professional interactions in a setting with a comparatively small group of experts and regular personal or virtual exchange are strong (er) indicators for KOL relationships.
In contrast, activities with a large number of involved KOLs with no or only limited institutionalized personal exchange can be considered as weak (er) indicators for KOL relationships.
A typical example would be presentations at the same Medical Congresses or involvement in the same Clinical Trials as an investigator. Both are joint professional activities for which the relevant data points will become part of any KOL interaction analysis, however, those two activities alone would not be sufficient indicators to establish some kind of relationship between these KOLs. Joint publication authorship sits somewhere in between the above two groups of KOL activities.
Data Privacy regulations apply to data collection and processing
All of the above information on KOLs can be collected from publicly available data sources like pubmed, clinicaltrials.gov, and other clinical trial registries, medical congress websites, medical journal websites, etc. Although these data points are all freely available on the web, it is important to understand that under the EU-General Data Protection Legislation (and similar legislation for other countries) this data is considered “personal information” and for European data subjects (KOLs) the respective EU-GDPR regulations apply.
From an operational and compliance perspective, EU-GDPR Art. 14 “Information to be provided where personal data have not been obtained from the data subject” is probably the most important data privacy rule to comply with because it states that the data controller (a company who collects KOL activity data points) needs to provide the data subjects (KOLs) with specific information regarding data collection and processing as well as their rights under EU-GDPR including the right to “request the erasure of personal data” (opt-out).
Generating insights from millions of data points
Even if you only consider for example the last three years of scientific activities for a global KOL Network analysis in a broader indication with strong research activity (e.g. oncology), you would arrive at hundreds or even thousands of relevant KOLs and consequently a very large amount of joint activities to derive your professional interaction network from. In order not to get confused with hundreds of connections for individual KOLs here are a few tips on how to create insightful analytics:
1. Prioritize the joint KOL activities and weigh them accordingly
As explained earlier, different scientific KOL activities can be characterized as stronger or weaker indicators for the type of relationship respectively “connection strength”. Depending on the use case, it can be beneficial to develop a system combining quantitative (total number of joint interactions, total number of different joint activities) and qualitative (type of joint interactions) aspects of common professional interactions between KOLs to map their networks in a practical way.
2. Group the KOL Network connections
Even if you apply all of the above you still might end up with dozens of highly relevant connections for a Top Global KOL, so you might want to consider analytical options to further narrow down the network universe for immediate use cases, like looking at the TOP decile or TOP x number of network connections.
3. Use case-specific analytics
Depending on your actual use cases you can apply all sorts of analytics to your KOL network data sets. Here are a few examples:
Analyze a KOL network concentration curve. You would find those with a smaller number of comparatively strong connections compared to those with many but not so strong connections.
Analyze a KOL’s network development over time. Are there any experts with whom the KOL has developed a stronger relationship over time (collaborated more often and in an increasing number of different activities)?
The described approach to KOL Network analytics has three distinct advantages:
- It is data-driven and objective
- It can be applied in a consistent manner across countries and geographies globally
- It is fully data privacy compliant
…and thus eliminates subjectivity (and potentially related compliance issues) in the KOL Network Mapping process while providing high-quality, full market coverage insights.
Marcus Bergler, Msc
Marcus Bergler is a globally recognized thought leader in KOL Identification, Profiling, and Network Mapping.
Before joining D2L Pharma Research Solutions as Global Vice President of Sales and Strategy in November 2017 he served as General Manager Europe for Veeva’s KOL business unit (now Veeva Link) after Veeva’s acquisition of Qforma’s/Mederi’s Global KOL business in 2014 where he was also responsible for the EU KOL and Targeting business. Prior to joining Qforma in August 2013, he was VP Sales and Marketing at Cegedim Customer Information (CCI) providing nomination-based KOL Identification and Network Mapping to major life sciences customers.
Before he accepted the CCI assignment in March 2010, Marcus held positions as Consulting Principal and Sales Team Leader at IMS Health, Germany. From January 2003 until December 2006 Marcus was responsible for the business development of Rogers Medical Intelligence Solutions, New York (now Pharmaspectra) in the German market and for selected headquarter clients, providing innovative competitive intelligence and medical education services to pharmaceutical companies.
Prior to that Marcus gained consulting experience of 10 years in the healthcare/pharmaceutical industry. Marcus holds a degree in Economics from Ludwig-Maximilians-University in Munich.