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Julian Upton is Pharmaceutical Executive's Online and European Editor. He can be reached at firstname.lastname@example.org
Killian Weiss talks to Pharm Exec about the modern challenges of identifying KOLs in oncology, and why technology can offer a solution to this vital process.
Veeva Oncology Link is an enterprise source of continuously updated oncology market intelligence aimed at facilitating better planning and engagement. It consolidates tens of thousands of experts and millions of activities worldwide into a single, complete source for medical and commercial use, offering “better coordination and alignment around the scientific experts who can make or break the success of a drug”. Essentially, it offers a technology solution, removing much of the subjectivity from the process of selecting and engaging with KOLs.
At the Veeva European Commercial & Medical Summit in Madrid, Spain, last month, PharmExec met with Kilian Weiss, Veeva Oncology Link General Manager, to talk about the modern challenges of identifying KOLs in oncology, and what Oncology Link can offer in improving this vital process.
Kilian Weiss: It’s really critical for the industry because KOLs really drive innovation, patient outcomes, and global health policy, and they shape how local physicians adopt innovative new products. The reason the industry needs it now is because the large oncology market is growing in complexity, with combination therapies and increasingly personalized treatments. It is very difficult for everyone to absorb that complexity; scientific experts help physicians, patients, payers and policy makers to translate it into clinical science. But the traditional tools to engage with these experts have been very fragmented. In today’s globally connected world, it is not sufficient anymore for every team in a pharma company to have that fragmented view. An enterprise needs to have the capability to know what is happening with KOLs globally and to engage with them in a very qualified way. Oncology Link builds the foundation for this process.
It’s a data platform, tightly integrated with our software; you can access the data via our expert software tool or via our CRM platform. You have access to tens of thousands of KOL profiles and you can identify and search for experts. For example, if you wanted to build an advisory board around very specific biomarkers in China, it used to take months to find these experts; now it can be done in seconds, and the profile are always up to date.
By having a globally unified data platform, our customers can see who else is doing things, who else is focusing on specific experts, so they can manage and co-ordinate across their company accordingly. That’s good for the company because it means it can engage in the right way with experts and not use them for small and maybe less relevant projects when another team might really need their strategic guidance for a bigger, more important project. So it drives a better customer experience.
It certainly needs a lot of calibration-we call it curation-to keep the data updated and high quality. We’re using advanced analytics and hundreds of curators to update and identify data. All this information is very fragmented in reality, so there are thousands of different data sources. Some of the data can be of low quality and can be hard to link to experts. We are applying a lot of technology and many engineers dedicated to solving the challenge of organizing this fragmented data, making it accessible, and linking it to a particular expert. There is a lot of manual labor on our part, hundreds of people curating the data, which feeds back into the algorithm to continually reinforce the system.
The old system was failing across every therapeutic area, creating a lot of pain in the industry and also for the customer. This is especially the case in oncology. If you look at one of our early adopters, a top ten pharma company which has since deployed Oncology Link globally, there was huge internal inefficiency. They had more than 100 projects mapping KOLs and that created huge spend because the information was fragmented. There was a lot of waste and operational inefficiency-many different systems, hundreds of lists, thousands of hours and millions of dollars-and no way of centralizing the approach to identifying and engaging with KOLs. And in the end the data was out of date anyway.
Also, I spent a lot of time meeting with KOLs and I was getting the same message, which is that they were having a bad customer experience with the industry. One team would reach out to them, not knowing what another team in their company was doing. What you had were all these teams overlapping and failing to co-ordinate their activities, thus creating a bad experience for what are the company’s most important customers. There was no mechanism within that company to get these teams to talk to each other, and in the end the customer suffered.
One of the things we have learned is that algorithms cannot replace local market knowledge or strategy, so there also has to be some real thinking. But this data capability does 90 percent of the work, freeing up teams to focus on the work that requires strategic thinking and human logic. Our tool can really target someone who is an expert in biomarkers, for example, so a company will ask, why are we not engaging with this person? There may be a reason, however, why it makes sense not to engage with this particular KOL-it could be an access restriction on the part of the institution-and that’s where subjective, personal market knowledge comes in. But companies need to make their processes replicable, and for compliance reasons they need to prove why they are engaging with a particular scientific expert, so it’s important maintain that 90 percent objectivity.
We now have five early adopters of the system. We thought it would take longer to convince our customers to make the fundamental shift from a small-project to an enterprise-wide approach. Customers quickly see the strategic benefits it offers and the cost savings. Also, the system’s global visibility is something that customers value. There are also a lot of additional use cases, which may be around science applications, around the R&D space, or around predictive analyses on the commercial side.
Right now we are laser-focused on scientific experts in oncology, which is a huge area, and we are making a big investment in managing this data. In the longer term, we can see additional need around other stakeholder groups and in other therapeutic areas, but for the moment there is such a great opportunity in oncology-our customers are really pushing us to grow and expand in this area.