Deep Web Health Research: Collaboration through Crowdsourcing
Currently, most research institutions rely on free monitoring services (Google Alerts) or subscription database services (LexisNexis) for online research.
According to an article in Modern Healthcare, physician medical researchers on average earn anywhere between $160,000 to $300,000. With this much money being invested in research, it is detrimental to have these professionals using methods of online research that don’t return complete results.
Researching should be about creating intelligence from new information, not wasting time sorting through irrelevant and inaccurate search results.
Current Methods vs. Deep Web Research Silos
There are millions of documents available on the Deep Web for healthcare research that current methods of online research have no way of finding or collecting.
Deep Web Research Silos can create collections of nearly any open-source content. For healthcare research, BrightPlanet creates disease and healthcare topic-specific Research Silos to which researchers can subscribe.
Unlike a traditional static database like PubMed or LexisNexis, where the dataset is predefined by the organization offering access, topic-specific Research Silos start with a base set of data and add additional sources requested by subscribers. This allows for collaboration between research institutions.
As more and more researchers request sources to be added to Research Silos, and as BrightPlanet continuously monitors these sources in key topic areas, Research Silos develop into some of the most comprehensive topic-specific research databases worldwide.
Since the subject matter experts, healthcare researchers, identify the sources and source types they want to draw from and dictate how they want it tagged and sorted, the Silos contain only relevant, searchable data. Tagging documents becomes crucial when creating intelligence from large datasets; the big challenge everyone has with Big Data.
A Little Math
Let’s say a Research Silo contains 126,000 harvested documents related to the broad topic of cervical cancer. If the researcher is only interested in patent applications mentioning the drug Interferon with the HPV18 strain, the user can create that advanced search.
The advanced searching narrows the huge dataset down to 77 very relevant patent applications mentioning HPV18 and the drug Interferon. Now any search query the user performs will comb through only those 77 super-relevant documents.
We have a case study available in our new whitepaper on harnessing the Deep Web for healthcare research that talks about how a research institution used Silos to make Sudden Infant Death Syndrome and Fetal Alcohol Syndrome research more efficient.
We also have whitepapers available that include information about creating intelligence from the Deep Web and five Big Data case studies.