KOL Network Visualisation
In order to efficiently manage KOLs, it is crucial to understand in which way they may influence peers – scientific papers, social networks, medias, professional associations, etc. – as well as the scope of their impact.
For this purpose, getting the right data obviously remains the first crucial step. However, using appropriate visualisation and analysis tools also makes a big difference in making the right decisions and going beyond basic result interpretation. It also provides invaluable help in addressing practical issues like how to select the best replacement to a KOL that cannot be reached.
Data Sources for KOL Identification
Databases of publications in peer-reviewed journals, participation in major meetings and collaboration to clinical trials can be queried in order to determine the involvement of each HCP into scientific research.
The main challenge consists of correctly assigning each piece to the right HCP. For this purpose, we have developed efficient algorithms with a very high success rate.
When it comes to KOL identification, the most valuable social networks to dig are Twitter and ResearchGate.
The former is the most valuable, especially to select digital opinion leaders. The use of the available API provides a wealth of information on the volume of activity, the scope of this activity and topics covered and most importantly which other HCPs each HCP is related to on the platform.
And of course, the inclusion of any data you were able to gather in the past or from a third-party provider should never be neglected.
The previous data can and usually is analysed separately in order to determine the importance of each HCP with regards to the most relevant characteristics. These characteristics may vary according to the type of KOL you are interested in, but in any case, efficient and objective methodologies exist in order to summarise the retained features as a workable index.
An additional way to investigate the data consists of looking at the relationships between each HCP and his/her peers. This means considering the co-authors in scientific publications, following/followers on social media, past and current colleagues in clinics and hospitals, and so on.
Such an investigation provides a whole new depth in the analysis of HCP interactions and allows more flexibility in the selection of KOLs as well as strategies to address practical issues.
To illustrate this, two common uses of KOL networks concern the capability on the one hand to select a “second best” when a preferred HCP is not reachable and on the other hand to identify pairs or groups of related KOLs which has become an option growing in popularity to amplify the signal sent and make the HCPs more comfortable in collaborating with companies.
Last but not least, the analysis of KOL networks rely on visual tools rather than more arid lists of values, facilitating group discussions, decision making and field force involvement. These tools can be easily customised to address your specific needs and available information.
A Simple Illustration
The widget below is an extract of a proof of concept application we developed with Australian neurologists.
The network is focused on scientific publications, but in practice, it can be applied in a similar way to Twitter or work networks or all combined. Colours identify Australian States, but could as well correspond to field force territories or any other relevant grouping.
You can click on any HCP to display relevant details (anonymised here) and drag them around to make the plot easier to read. You can also filter by state and click in an empty section to reset your search.
Feel free to play with the network!