Social Networks for Clinical Trials Recruitment?

Earlier this week I promised to come up with some (possibly mad) ideas for how social networking data (with the help of analytics) could be used to solve a variety of non-advertising / non-retail scenarios. Now not sure how far I will get out before I run out of steam, but here goes for the first one.

Before I start, I should clarify a couple of things as they will influence the way I look at this problem; Firstly, when I think about social networks I don’t limit this to social media since loads of “people networks” exist that are not explicitly stored in social media networks. For example; co-writing a paper with John Doe implies that I have a relationship with John and more than likely its a deeper relationship than those I have with most people I am connected to on social networking sites. Secondly, I am particularly interested in capturing & harvesting people interactions from a variety of data sources and blending them with pure social media data, in this way essentially extending cyberspace networks with real-world ones. Integrating relationships from the “real-world” with those of cyberspace is not a new concept and in fact is something we see services like Klout and Kred actively attempting to do. So over time I anticipate that more social media / social network data will incorporate people networks derived from a wide variety of data sources. Another nice way that social media sites can increase the value of their data & analytics, thus creating another revenue channel outside of pushing ads.

So Clinical Trials & Social Network Analysis?

Clinical Trials Recruitment is an ongoing challenge for pharmaceutical companies, from finding the right patients to ensuring that they remain compliant and that they stay with the trial for its duration. Social networks and social analytics could help address some of these challenges. Firstly in supporting the recruitment process, and secondly in providing a global support structure for participants within the trial; clinicians and patients. In addition, the data laid down by the social semantic network provides an opportunity for detailed analysis during and after the clinical trial to gain a better understanding of the patients state of mind during the trial and also to potentially improve structure and execution of future trials. In addition, generalized social networks also tell you something about the patients lifestyle which is often an important characteristic in recruiting and maintaining patients on a clinical trial.

There are a growing number of clinical networks emerging aligned around specific disease areas that are a hugely valuable source of data for both recruiting and support during the trial. In addition there are lots of healthcare blogs and also large libraries of medical research, such as The Cochrane Library or U.S National Library of Medicine, which could contribute to a rich clincial network of knowledge. {to my point earlier about deriving networks from existing non-social data}

The leverage of these “non-social” research networks could be particularly valuable when combined with these emerging healthcare social networks. In order to recruit the right patients to a trial, you first need to recruit the right clinicians. The ability to identify where to run your clinical trials — which hospitals and clinicians are best positioned to make it a success — is a key factor in this decision making process. Through analysis of clinicial research (identifying clinicians/authors, diseases, treatments, drugs, hospitals, universities, …) we can build a network that not only represents the knowledge within the system, but also the human connections. This network tells us; what clinicians are active and highly influential in a specific disease area, their institutional and academic affiliations, who they’ve worked with and where.

This tells us which clinicians and institutions are most active in certain areas, and their connectedness will indicate their breadth of influence and ability to work well with other members of the network. This information allows a trial to optimize itself not just around knowledge and expertise, but also people and organizations => The People Dimension!

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