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Open data: Holy Grail or Pandora’s box?
By Gaurav Chandra  |  Jun 23, 2022
Open data: Holy Grail or Pandora’s box?
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Dr Gaurav Chandra discusses the importance of making medical data accessible to all, connecting global medical data with precision medicine, and calling awareness to the necessity for different governments to open up their medical data to promote the progress of digital health. The conclusion is that the value of artificial intelligence to healthcare lies in better collaboration, innovation, the inclusion of all stakeholders in decision making, and integrity in data custodianship.

DENVER, COLORADO - "We can only see a short distance ahead, but we can see plenty there that needs to be done."- Alan Turing.

From the time people wake until the time they go to sleep, they generate data. Real-time data analysis has the potential to transform global healthcare. Smartphones and wearables like smart watches make it possible to record people’s locations on Google Maps, the number of steps they take, and the number of hours they sleep, withelectrocardiogram apps and other health apps recording people’s heart rates and rhythms. Still, this begs the question: Who ultimately controls these data?

While most people are not concerned with the altruistic use of data for the benefit of achieving better health outcomes for all, the most significant concern is that if not regulated appropriately, such health data could be abused for financial gain or sold to third-party users like health insurers. This, in turn, could cause future healthcare to become more stratified and lead to biases in its provision.

The world needs to strive towards a healthy, open data ecosystem that considers each of the various stakeholders, as well as other factors like bioethics, de-identification, correct data preparation, and information policies. In addition, there is a need to strike a better balance between reaping the benefits of an open data policy for healthcare and ensuring privacy protection.

Benefits of open data in healthcare

Opening up data makes it possible to share crucial medical information both nationally and globally. It allows the study of disease etiology, epidemiology, diagnostics, and response to therapeutics to align with ‘stratified medicine.’ It also goes one step further by eliminating a problem common to both medical research and machine learning: the issue of bias. It is well-known from academia that most medic

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