Internet of Things-enabled devices make it easy to accumulate far more data than at any time in the past, and make decisions based on that data. That means, for example, that retailers can find out how many customers have walked down a specific aisle over the course of a day, or athletes can use wearables to measure vital functions such as heart rate and muscle effort. Such data can be used wisely to influence individual decisions: i.e., move a display to a more heavily trafficked aisle in the first example; slow down your workout if your heart rate goes above your target zone in the second.
When such technology is adopted on a mass scale, however, it can have even greater implications, by recognizing data trends and correlations—finding value in the unknown. While IoT can make a major impact on finding timely solutions across many disciplines, it’s poised for an enormous impact in the healthcare field.
We’re already seeing the implications of “secondary data” (data that was not collected for the user’s express purposes) in recognizing epidemics and healthcare issues in real-time. For instance:
- Google search trends for flu symptoms can show a strong correlation to actual influenza outbreaks around the world, preceding numbers available from the CDC. While Google has been criticized in recent years for its accuracy, the service has now integrated traditional CDC data into its big data insights to provide a more detailed context.
- Twitter updates tracked the 2010 cholera outbreak in Haiti. A research study found that in the aftermath of the earthquakes in Haiti, cholera-related Twitter status updates around the region correlated strongly to the start and end dates of the epidemic.
These types of search- and social media-focused secondary data are self-reported by users, so they have some limitations in accuracy. But by using IoT-enabled technology, you can draw data directly from sensors on your equipment, providing scientifically accurate measurements.
For instance, imagine a future in which everyone wears a “second-skin sensor,” like this patch recently developed by researchers at Northwestern University and University of Illinois at Urbana-Champaign. Such IoT-enabled technology could wirelessly transmit its data-readouts to a central database, instantly identifying real-time shifts in temperature change in different regions to determine locations of disease outbreaks. Rather than waiting for physician-reported data, healthcare organizations could instantly take precautionary measures to limit and treat such outbreaks.
Such a future is not so far away—and, with the growth of IoT technology, we will be able to measure real-time data trends in ways never possible before, constantly discovering new correlations and patterns between data insights that would be invisible to the human eye alone.