Twitter to Predict Your Next Doctor’s Appointment

Nov 27, 2015
Reading Time: 2 minutes
Written by Dominic Woolrych

Imagine not having to wait in queues with a runny nose at the hospital during the flu season. Receiving proper attention from a doctor that is not swamped with patients seems more like a privilege than a right for people accessing basic healthcare. A collaboration of social media, technology and healthcare may assist in making that a possibility in the very near future.

While many people find it annoying when their friends live tweet their every emotion and feelings, researchers believe that it may actually assist them to develop an application that allows prediction of health epidemics and assist in the judgement of Australian population’s mental health.

Recently, researchers from the University of Bristol conducted experiments to determine whether twitter could be used to track an event or a phenomena including predicting a disease outbreak. The results pointed towards an affirmative answer. Through enabling users to tweet publicly and using geo-tagging, the researchers were able to create a model that predicted the severity of flu in an area from analysing keywords from the tweets.

This could be revolutionary for the healthcare system as it will hugely assist in not only allowing the system to be more efficient but also cost effective, while improving the service to patients. For example, the studies suggest that people are more likely to tweet their condition and symptoms before going to a local doctor or the GP.

The result: doctors can be rostered in accordance with the data, fluctuating to suit the population’s needs, with more doctors to be rostered during estimated peak flu seasons.

A similar approach has been adopted by the Australian researchers to judge the mood of the population in order to define and treat mental illness. Social media provides an instant pool of up to date information about people’s thoughts, feelings, emotions and actions. This can allow policy makers to ensure proper resource allocation by “sizing the problem” and extent of mental illness.

“We Feel” is an online tool developed to explore people’s emotions across a range of criteria including location, gender, economic and environmental factors such as the weather or time of the day (with a rainy Monday morning bound to increase the usage of sad-face emojis). Thus, it can greatly assist in “sizing the problem” of mental illness.

The only slight issue is that the data collected from social media might not be representative of the entire population, as younger generations are more active on social media. Also, privacy plays an important role. In order for the system to work, people need to share their condition and emotions publicly. If such a system were to be implemented in the near future, we might witness changes in privacy policies.

Let us know your thoughts on this method of improving the healthcare system by tagging us #lawpath or @lawpath.

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