How to reach the stakeholders who can
help translate science into clinical practice
John Eichert,
Senior Principal, Thought Leader Network Center of Excellence, IQVIA Blog Aug
27, 2021
Healthcare thought leaders have a powerful influence on the
underlying beliefs and resulting behavior of those in their communities of
practice. When a thought leader adopts a therapy, the rest of the network
adopts it at a rate that’s 25% higher than in networks in
which a leader doesn’t believe in the value or hasn’t started prescribing the
product.
Life sciences companies typically engage thought leaders of
national prominence in specific therapy areas as well as high volume
prescribers to spread the word about scientific advances. However, there are
other important thought leaders between these two groups; those who lead the
way clinically and are trusted for their insights but are usually difficult to
identify using traditional techniques. Fortunately, by using multiple sources
of data and sophisticated analytics (with the aid of artificial intelligence
(AI) and machine learning (ML)), we can now identify these hidden influencers.
Four truths about thought
leaders
Over the past 18 years, IQVIA has worked extensively with
academics and applied our own practical analytics to understand the nature of
thought leadership, concluding that:
1. Thought leadership is not monolithic. Different types of thought leaders play
different roles in product adoption.
At the national level, there are prominent experts, or key
opinion leaders (KOLs). These luminaries create the treatment narrative that
frames how, when, and where a product should be used.
Regional influencers are the leaders that translate the science
established by the national experts into clinical practice. These influencers
are recognized as having a higher level of understanding of the science and
have practical clinical experience around a given disease.
A third tier of influencers are discussion leaders, or “near
peers,” to whom local healthcare professionals (HCPs) turn to for expert
clinical advice via what is often called “curbside consultation.”
The fourth group consists of local advocates who are high-volume
prescribers and to whom other HCPs refer patients. Their treatment preferences
are often shared by their referral sources.
It is the two groups in the middle (regional clinical advisors
and trusted discussion partners) who are often hidden from view but are the
lynchpins in accelerating how product information is shared.
2. The impact of thought leaders varies based
on the product or service development timeline. The earlier a product is in its lifecycle,
the more important scientific/research communities are to it. As development
moves toward Phase III, there’s a greater need to identify regional and
clinical influence networks. Thus, a product’s point in its lifecycle is the
basis for customizing a special selection of data sources and methodologies,
and for prioritizing the information uncovered.
3. There are different types of HCP
communities of practice. There are scientific networks of physicians who perform
research together and co-author scientific papers as well as networks formed
around organizations and clinical practice.
4. Different data sources are used to identify
thought leaders and align them to the product adoption process. Nationally prominent leaders can be
readily identified based on their publications and speaking engagements.
Practice and volume leaders can be easily identified using past experience,
prescription data, and data on their referrals and shared patients.
Identifying the hidden influencers – the regional clinical advisors
and trusted discussion leaders – is often more difficult, and requires
sophisticated primary research coupled with big data, and the application of
AI/ML. At IQVIA, we’ve developed an advanced ML module that allows us to
leverage IQVIA’s big data factory to build relationship insights using a sample
gathered from peer nomination data (gathered via primary research) and can
predict the connections between the nominated thought leaders and HCPs in the
target market. (See Figure 1.)
Figure 1: Combining methodologies to map the entire thought
leader landscape
IQVIA’s methodology to
inform dynamic targeting
At IQVIA, we’ve developed a comprehensive approach to helping companies
leverage thought leader communities (or networks) in their medical and product
communications:
·
First, we use advanced
analytics to identify, profile, and validate thought leaders, and then we align
them to the brand strategy, the medical narrative, and the scientific platform.
·
We then match those
thought leaders to their communities. We have a vast amount of secondary data
that allows us to see who initiates prescriptions and who follows their lead.
We can map target HCPs to thought leader segments to generate very comprehensive
sub-networks or communities of practice. This gives us a complete topology of
thought leaders and their communities to understand their common affiliations
and common interactions.
·
Then, we align leader
segments and their practice networks to those internal users who will be
engaging with thought leaders. Each type of persona in the company has
different types of specific engagements at their disposal.
·
Lastly, we orchestrate
engagement, delivering personal and non-personal omnichannel campaigns and
measuring their impact.
Life sciences companies have long recognized the value of KOLs
and high prescribers to their products’ success, but they’ve been unable to
connect all the dots of influence within and between HCP communities. Through
new analytical methods, it’s now possible to map the entire topology of thought
leaders and their communities to accelerate brand uptake.
Identifying and Engaging
the Hidden Influencers of Medical Practice: Those That Translate Science into
Practice
Healthcare thought leaders have a powerful influence on the
behavior of those in their communities of practice, at a rate that’s 25% higher
than networks without peer recognize thought leaders.
Learn how IQVIA, with the aid of artificial intelligence and
machine learning, can identify hidden influencers; those who
lead the way clinically and are trusted for their insights.
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