Twitter Leveraged To Predict Asthma Patient ED Visits
By Christine Kern, contributing writer
Researchers have created a model able to predict with 75 percent accuracy how many asthma-related emergency room visits a hospital could expect on a given day.
Researchers at the University of Arizona have found health-related tweets can be leveraged to help hospitals predict how many asthma-related emergency room visits they might see on a given day. According to the University of Arizona, the research was led by Sudha Ram, a UA professor of management information systems and computer science, and Dr. Yolande Pengetnze, a physician scientist at the Parkland Center for Clinical Innovation in Dallas and specifically examined how asthma-related tweets, when analyzed in conjunction with other data, could help predict asthma-related visits to the ER.
Researchers were able to create a predictive model that achieved a 75 percent accuracy rate in anticipating whether the number of asthma patients frequenting the emergency department at a large Dallas hospital on any given day would be “low,” “medium,” or “high,” using the predictive analytics of data provided from electronic medical records, air quality sensors, and Twitter feeds.
“We realized that asthma is one of the biggest traffic generators in the emergency department,” Ram said. “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 will be publishing their findings, which according to Ram has the potential to improve staffing and resource management at hospital emergency departments nationwide, in the IEEE Journal of Biomedical and Health Informatics' special issue on Big Data. Ramexplained the study underscores the importance of big data and analytics, including streams from social media and environmental sensors, in addressing health challenges.
The study also lays the groundwork for other predictive models for other chronic diseases such as diabetes that could help EDs anticipate medical needs. “You can get a lot of interesting insights from social media that you can't from electronic health records,” Ram said. “You only go to the doctor once in a while, and you don't always tell your doctor how much you've been exercising or what you've been eating. But people share that information all the time on social media. We think that prediction models like this can be very useful, if we can combine various types of data, to address chronic diseases.”
Study co-author Pengetnze explained the research reflects a unique approach to population health, stating, “The multidisciplinary collaboration in this study combines clinical expertise, health services knowledge, electronic health records, and non-traditional big data sources to address the major health challenge that is asthma. This multifaceted approach could have important implications for the timeliness of public health surveillance, hospital preparedness and clinical workflows, first for asthma then for other burdensome chronic conditions like childhood obesity, Type 2 diabetes, and cardiovascular diseases, to name a few.”