Leveraging medical taxonomies to improve knowledge management within online communities of practice: The knowledge maps system

Abstract

Background and ObjectiveOnline communities of practice contain a wealth of information, stored in the free text of shared communications between community members. The Knowledge Maps (KMaps) system is designed to facilitate Knowledge Translation in online communities through multi-level analyses of the shared messages of these communications. MethodsUsing state-of-the-art semantic mapping technologies (Metamap) the contents of the messages shared within an online community are mapped to terms from the MeSH medical lexicon, providing a multi-level topic-specific summary of the knowledge being shared within the community. Using the inherent hierarchical structure of the lexicon important insights can be found within the community. ResultsThe KMaps system was applied to two medical mailing lists, the PPML (archives from 2009–02 to 2013–02) and SURGINET (archives from 2012–01 to 2013–04), identifying 27,924 and 50,597 medical terms respectively.KMaps identified content areas where both communities found interest, specifically around Diseases, 22% and 24% of the total terms, while also identifying field-specific areas that were more popular: SURGINET expressed an interest in Anatomy (14% vs 4%) while the PPML was more interested in Drugs (19% vs 9%). At the level of the individual KMaps identified 6 PPML users and 9 SURGINET users that had noticeably more contributions to the community than their peers, and investigated their personal areas of interest. ConclusionThe KMaps system provides valuable insights into the structure of both communities, identifying topics of interest/shared content areas and defining content-profiles for individual community members. The system provides a valuable addition to the online KT process.