Here's how health-related tweets can help predict emergency room traffic

Washington: A team of researchers has created a model that was able to predict with 75 percent accuracy how many emergency room visits a hospital could expect on a given day.

Twitter users who post information about their personal health online might be considered by some to be over-sharers, but University of Arizona study suggests that health-related tweets may have the potential to be helpful for hospitals.

The researchers looked specifically at the chronic condition of asthma and how asthma-related tweets, analyzed alongside other data, can help predict asthma-related emergency room visits.

Lead author Sudha Ram said that they realized that asthma is one of the biggest traffic generators in the emergency department and often what happens is that there are not the right people in the ED to treat these patients or not the right equipment and that causes a lot of unforeseen problems.

The researchers found that as certain air quality measures worsened, asthma visits to the emergency room went up. Asthma visits also increased as the number of asthma-related tweets went up. The researchers additionally looked at asthma-related Google searches in the area but found that they were not a good predictor for asthma emergency room visits.

The research highlights the important role that big data, including streams from social media and environmental sensors, could play in addressing health challenges, Ram said.

Ram added that people can get a lot of interesting insights from social media that they can't from electronic health records. People only go to the doctor once in a while and they don't always tell doctor how much they've been exercising or what you've been eating, but people share that information all the time on social media.

She said they think that predict ion models like this can be very useful, if they can combine various types of data, to address chronic diseases, adding that they've got really good results and now they're working on building even more robust models to see if they can increase the accuracy level by using more types of datasets over a longer time period.

The study is due to be published in IEEE Journal of Biomedical and Health Informatics.