Connecting with the right experts empowers medical affairs to develop a complete understanding of disease communities – and of the market environments new treatments will launch into. The insights help your organization answer questions like:
- How do patients view the existing treatment landscape?
- How are your products – and those of your competitors – actually perceived by those who use them?
- Who are the key opinion leaders in a disease community, and how do they connect with each other?
Medical affairs has traditionally looked to answer these questions through the process of expert identification, but this process is quickly evolving.
Below, we explore how data and analytics have transformed the process of expert identification.
Who do we mean by ‘experts’?
A historical view
Historically, expert identification was largely based on publication volume. Teams could consult sources such as PubMed to uncover experts in a particular area, or they could look to those who spoke most regularly at medical congresses and conferences. This approach worked up to a point, but its impact was limited. Publication data reveals just a small fraction of the potential experts in any given field, with a strong bias to those you might call the loudest voices.
“You’re almost operating with one quarter of the necessary information if you’re only working off who’s loudest, scientifically.”
– Lance Hill, CEO, Within3
A contemporary view
Now there’s a growing appreciation that expert voices can come from almost anywhere. Publication data does not tell the whole story, and if organizations are relying on insights from the same old voices, they’re unlikely to uncover anything new, or unique to what their competitors are hearing.
A more contemporary approach to expert identification sees medical affairs teams looking for those with contextual knowledge to the outcomes they’re hoping to achieve – ensuring that they target those individuals with the ability to actually impact their strategy.
“Our internal data has shown that if you’re only doing KOL ranking via traditional methods, somewhere between 20 and 60% of the people that come up aren’t the best people.”
– Lance Hill, CEO, Within3
If not publication volume, then what?
There’s a reason why publication volumes and congress appearances were the traditional benchmarks for expert identification: they’re clear and obvious. So if we’re no longer looking in these places exclusively, how else can we identify expert voices?
It’s important to consider how the disease community landscape has changed in recent years. Now, conversations largely occur online – in forums and chat rooms, in social media groups, on blogs, and elsewhere. And the people taking part in those conversations have changed, too. They’re patients, their families, and other patient advocates – as well as the clinicians and other HCPs you’d traditionally associate with expert identification. Traditional approaches to expert identification aren’t guaranteed to surface the rising stars within a disease community, nor the unheralded experts your competitors are yet to discover. Many of these new experts are digital opinion leaders – people who occupy different channels, and have an entirely different reach and audience base to your traditional KOLs.
Two technological innovations are at the heart of this new approach to expert ID:
A broader data set
Broadening the data set can effectively be thought of as extending the search for experts to new places. These different places might include the blogs, forums, and social networks we mentioned earlier, but they might also include different parts of the world where your organization is yet to establish a presence. Are you currently talking to the right people if you’re attempting to expand into new, global territories?
More data, of course, means more analysis. Without technology, leaving no stone unturned in the search for experts would be enormously labor-intensive and time-consuming. Technologies including artificial intelligence can help to automate the process of data analysis to surface the right experts quickly and efficiently – without the need for intensive human input.
Network analytics
In the new world of expert ID, simply identifying key opinion leaders and other valuable experts is just one part of a much broader picture. Network analytics helps to unlock the ‘invisible college’ – revealing the hidden connections between experts and institutions, and uncovering previously-unknown spheres of influence that can make or break a new therapy.
Like expert ID, historical network analysis was also based on publication data. Medical affairs teams could look at sources like PubMed and check co-author details to see who was publishing with who. This approach revealed a very superficial ‘network’ of experts, but failed to offer any real insight into the deep connections within a disease community. AI-powered network analytics has changed the game forever.
Disambiguation
There’s one further step to apply before the expert ID process can be considered complete. A process called ‘disambiguation’ applies an additional layer of nuance to your expert data. For example, your database may not make a distinction between Dr. John Smith, Dr. J. Smith, Dr. J. H., etc – potentially skewing the results and making it appear that there are more experts speaking on a particular subject than there really are. Within3 goes through a process of data ‘disambiguation’, where these kinds of discrepancies are identified and cleaned up before they can impact results.
Unlocking the power of the network
There’s power in these networks. In the fast-paced, highly-competitive world of life science, getting the right insights from the right people at the right time can give you the edge over your competitors – or spell the difference between success and failure.
“Imagine you’re thinking about your medical strategy, and who you should be interacting with. Imagine you’re 20% incorrect – one out of five of the people you’re talking to isn’t the right person. And imagine your competitor is only 5% incorrect. That’s a material difference in the effectiveness of your medical organization compared to a competitor.”
– Lance Hill, CEO, Within3
By using network analytics in a targeted way, aligned with both medical and commercial strategies, teams can begin to exert influence across entire disease community landscapes. With deep insight into how different networks operate and how different experts are connected, medical affairs leaders can target the key pieces whose influence spreads to every corner of their disease community.
If you’d like to experience the power of network analytics for yourself, schedule a demo of the Within3 Insights Management Platform today and uncover the hidden experts in your disease community.