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Bitscopic is the next big company in digital disease detection (DDD), which\u00a0essentially\u00a0uses\u00a0internet data for public or global health biosurveillance. In essence, DDD\u00a0combines big data and crowd sourcing to track disease outbreaks and other public health issues more quickly and with a higher geographical granularity than traditional disease surveillance systems, which have significant lag time due to their dependence on official reports made by physicians and health departments.<\/p>\n
Tools and applications for digital disease detection have been around for decades: Google Flu Trends is one of the most public and well-known examples of digital disease detection, as it leverages the geographical locations of search queries to track outbreaks of the flu in the US. GFT\u2019s big data approach accurately tracked flu outbreaks in real-time, which was a great improvement over the 1-2 week lead time it takes for the Centers for Disease Control (CDC) to collect and process this data. The Global Public Health Intelligence Network, sponsored by the World Health Organization, has been tracking disease outbreaks by trolling the Web for disease-related news stories since 1997. Newer tools leverage recent growth in social media platforms. For example, HealthMap made the news earlier this year when it became the first website to note the Ebola outbreak. On March 14, about nine days before government authorities in Guinea had even informed the World Health Organization of the first case, HealthMap had picked up mentions of the first few infections from a local newspaper in Guinea. Google Flu Trends now has a competitor: a Twitter-based program designed to forecast influenza patterns.<\/p>\n
Although these tools have been around for awhile and new digital detection disease tools often sprout up, very few of them are for-profit or able to capture the value they create. Most are academic or research based applications or are proprietary to large multilateral NGOs. Enter Bitscopic. Bitscopic is a startup that specializes in applying the latest advances in the fields of distributed computing and machine learning to biosurveillence and public health. The company works with large national health organizations to implement systems to detect the early outbreak of potential biological threats and contain their spread. Their flagship product, Praedico, is a next generation big data biosurveillance application that incorporates cloud computing technology, big data platforms, machine-learning algorithms, geospatial and advanced graphical tools, multiple electronic health record domains, and customizable social media streaming from public health-related sources to predict and track infectious disease outbreaks, all within a user friendly interface.<\/p>\n
Bitscopic\u2019s team is largely comprised of former Microsoft\/Bing employees, who are skilled at extracting meaningful insights from very large datasets from electronic health records (EHR) of federal hospitals. They work with hospitals or governments interested in tracking disease detection digitally, and leverage best-in-class analytics to collate different sources to track infections and outbreaks nationally as well as down to the patient level. Bitscopic offers a competitive advantage over Google Flu Trends because it combines actual EHR data, epidemiological data, advanced geospatial tools, as well as social media tracking for disease detection. Google Flu Trends has proved successful at demonstrating historical outbreaks and trends using the data that was used to build the system, but it has proved significantly less successful at predicting behavior based on new\/future data. Google Flu Trends retroactively predicts flu activity well in 2009, but it missed two large swine flu outbreaks later that year, due to changes in internet search terms. It also over-predicted the severity of the 2011-2012 flu season by 50%, which can lead to misallocations of resources and incorrect personnel deployment.<\/p>\n
Because Bitscopic leverages actual health records as well as search terms and other sources in a constantly adapting model, it is far superior at disease surveillance and outcomes predictions. Not only can it help localize and pinpoint hospital-acquired infections and local outbreaks of unknown diseases (think outbreaks of the relatively unknown Chikungunya virus in the US last year), but it can also facilitate automatic communication between healthcare providers and local, national, and international health authorities in real time \u2013 with no lag.<\/p>\n