If global leaders want to contain the coronavirus, the healthcare industry needs access to the most accurate data about how the outbreak is changing from day-to-day. Enter, data analytics.
For the data science world, COVID-19 represents a “gold rush” of information. But all of this data is only of value if it is used to develop strategies needed to curb the spread of the virus and address many of the complexities relating to it. Locally, we’ve seen health, academic and ICT sectors working together in an attempt to unpack and analyse healthcare data sets and better understand the potential impact of COVID-19.
“Machine learning and data analytics are going to play a really important role in understanding the spread of disease, as well as understanding the effectiveness of our different responses to disease,” Joe Corkery, MD, director of product management at Google Cloud, told HealthITAnalytics. “This kind of research is highlighting the fact that there are a lot of new things that we can do to make data analytics more easily repeatable and specific to healthcare use cases.”
For example, contact tracing entails getting in touch with the close contacts of a patient after that individual tests positive for the virus. The process is typically quite time-consuming. Data scientists and medical experts have partnered with researchers from Oxford University to create mathematical models that speed up contact tracing. They developed a mobile phone-based solution to eliminate the need for people to call the contacts manually. Instead, text messages are sent out confirming that the individual needs to self-isolate.
All of this enables a more predictive approach to medical care. When a person’s medical history is stored on a smart digital system/platform, healthcare providers will have less trouble assessing patient risk because they have a more comprehensive picture of the person they’re treating.
Beyond the here and now, the data generated – and analysed – during this outbreak will prove invaluable in preparing the world should this kind of thing happen again in the future. And the hope is that our current experiences will push global healthcare systems to approach data analytics in a more effective way.
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