We call them disease communities for a reason. In a community, individuals interact with one another on numerous levels with varying spheres of influence, individual accomplishments, and levels of knowledge. Disease communities share these same dynamics, and understanding them – as well as the myriad connections within them – is critical to help commercial and medical affairs teams bring impactful new therapies to market. But just like in our wider, societal communities, the tastemakers and influencers within the disease community landscape (DCL) are just as complex and unexpected.
So, how does Within3’s DCL solution help teams understand the connections within disease communities? Read on to discover how you can reveal hidden influencers capable of making or breaking a new therapy.
The problem
Pharma teams invest a lot of time and money into engaging key opinion leaders (KOLs) and healthcare professionals (HCPs) capable of informing their medical and commercial strategies and influencing opinion and behavior. Despite this investment, they often leave many ‘stones unturned’ during expert identification, and instead return to the usual suspects for strategic input or insights – those experts who are well established, reputable, and widely known to their competitors. As a result, pharma teams may end up with ‘recycled’ insights from the same old faces, and overlook many other potentially valuable experts. Teams can rarely see first-degree relationships between influencers, so as to view them within the wider context of an ecosystem of KOLs and HCPs. They need to be seen holistically, inclusive of their influence and network within a dynamic disease community.
These days, teams have access to databases that include practically every expert they might want to engage – and yet most companies willingly acknowledge that they still get their insights from the same handful of experts every time. Locating the most influential people from within that database is just as complicated as it’s always been.
“They’re not sure if they’re talking to the right experts in the first place – but it’s the established way of doing things. The databases are accepted as ‘good enough’.”
– Tony Page, SVP Insight Analytics, Within3
The solution
The answer to this problem is community Network Analytics. Network Analytics can analyze all of the experts within a disease community, and assess all the different ways they’re connected. Where the traditional approach to expert identification relied on publication data – how often an individual was cited on PubMed, for example – Network Analytics parses the publications, congress abstracts, and clinical trials associated with a disease alongside industry payment data, social media data, and claims data. This process reveals the relational aspect of these transactions, painting a complete picture of how disease communities function from a human influence perspective.
The idea behind Network Analytics is not to simply identify the top 10 people in any community based on their publication history or congress speaking engagements. Nor is it to assess the 50 or so people they’re connected to. Network Analytics looks at many thousands of individuals, and all the different ways they’re connected across multiple domains – the same process employed by military intelligence agencies to track down terrorist cells. It’s a mathematically measurable ranking of experts based on the percentage of disease community members they can influence – directly or indirectly.
“Disease Community Insights Reports tell you everything you need to know about engaging with that community. We don’t filter people out – whoever’s involved in the community is part of the community. So when we look at the insights report, we find people that the clients otherwise wouldn’t see.”
– Tony Page, SVP Insight Analytics, Within3
The Within3 difference
Currently, Within3 is the ONLY company that applies Network Analytics to entire disease communities. And we’re the only company that pulls insights from those analytics. While other KOL identification tools exist, none apply community-level Network Analytics as comprehensively as Within3. Rather than selecting KOLs based on individual qualifications alone, we start by understanding our client’s strategic objectives – creating a dataset that’s customized to each client’s needs. Applying Network Analytics surfaces those who are best networked within the broader disease community – providing unparalleled strategic value.
Off the back of our proprietary analytics, we create disease community insight reports that unearth key information to help make client engagements with KOLs more impactful and informative. Our insight reports help clients engage experts efficiently and effectively, while revealing just how many experts they need to engage to reach an entire community – and who those experts are.
“Rather than cataloging individual trees, as traditional databases do, we map the entire forests that are disease communities.”
– Tony Page, SVP Insight Analytics, Within3
By using Within3’s community Network Analytics to engage an expert they’d never spoken to before, one client was able to uncover an entirely new multimillion-dollar indication. While we can’t promise this kind of outcome to every client, we can promise that you’ll discover rising stars before they’ve even published and other external experts that you – and your competitors – never knew existed, leading to invaluable new insights into how your disease community operates.
Contact us today to schedule a demo of Within3’s disease community analytics solution.
