Every month, millions of conversations, influencers, and articles are analyzed, enriched and added to this neural network. The starting place is Twitter, with its outsized and leading role in public healthcare conversations. From there we pull in millions of shared content pieces from news media, scientific journals, forums, blogs, and other social networks like LinkedIn, YouTube, Instagram, Reddit, and Facebook. We monitor any mention of the tens of thousands of healthcare-related keywords, phrases, user accounts, URLs, domains, hashtags, and cashtags.
Enriching Everything with Healthcare Meta Data
Journal articles are identified and linked to their PubMed and Digital Opinion Influencers (DOI) identifiers, and Altmetric data is surfaced to highlight attention metrics.
KOLs and influencers are segmented by their healthcare stakeholder status and given an overall impact metric with the Healthcare Social Graph Score.
Conversations are categorized according to our healthcare taxonomy for further analysis and filtering.
Content thatis shared is summarized and analyzed with NLP algorithms.
Physicians and HCPs have their profiles linked to other identifiers such as NPI numbers or internal enterprise ID numbers.
Healthcare conferences and medical congresses are captured alongwith meta data on topics, dates, and location.
This neural network of insights found in the Healthcare Social Graph can be viewed from several different angles
Broad longitudinal analysis of diseases like Multiple Myeloma.
Time sensitive conversations from medical meetings like ASCO’s Annual Meeting.
Hyper-focused analysis of HCP discussions around SGLT2-related clinical trials results.
All provider or patient interactions around an account like @pfizer.
The complete debate resulting from a specific article and URL like "nejm.org/doi..."
Follower audience analysis of an account like @CircAHA.
Individual KOL analysis of healthcare impact for a profile like @hjluks.