In a review of data from athenahealth’s EHR, including weekly total visit counts for flu, vaccine visit counts and visits for influenza-like illness, among others, researchers found that accurate data about region-specific flu activity could be drummed up much faster than waiting for the CDC to publish its results, which can have a lag time of up to two weeks.
“In this study we have shown that EHR data in combination with historical patterns of flu activity and a robust dynamical machine learning algorithm, are capable of accurately predicting real-time influenza activity at the national and regional scales in the U.S.,” the authors wrote. ” Here we show that incorporating CDC’s influenza-like illness historical information and more of the available EHR information, using a suitable machine learning methodology, can improve flu estimates.”
They conclude their study helps demonstrate the value of tapping cloud-based EHRs for local public health surveillance.
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