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When you look at healthcare from a governmental standpoint, it is a very costly affair. Even some of the world’s most developed economies struggle to cope with the sheer scale of rising healthcare costs. This is happening across all age groups within the society.

In a bid to improve healthcare at a mass scale, West Virginia University (WVU) has been given a funding of US $3 Million by the National Science Foundation (NSF). The aim of this initiative is to improve general healthcare across the population.

This initiative mainly hinges on the use of two key technologies, data science and Artificial Intelligence (AI). Instead of reactive treatment methodologies, which are very costly for healthcare providers, and ultimately the state, this initiative is all about prevention.

The prime focus of this initiative is to reduce healthcare costs by going for preventive therapies and treatments. This in turn will be made possible by AI and data science. This initiative mainly revolves around collecting data of the population via different sources.

This health related data or metrics, will then be analyzed using Artificial Intelligence (AI) and other simulation models to detect and address serious, costly health issues well before they increase in their severity.

This revolutionary approach to healthcare has a two fold advantage. Firstly, this approach to healthcare will immensely improve the quality of life of the masses, as they will be much more self aware about the current state of their physical health.

The other major advantage of this initiative will be to reduce the overall financial impact of healthcare, by detecting and arresting diseases in their early stages. One example of this initiative is smart wearable devices, like smart watches, which will be transmitting data.

In the near future, we might find ourselves in the midst of a healthcare system that is powered by digital technologies. If this vision comes to fruition, we will be living in a world where serious and fatal illnesses will be addressed at much earlier stages.

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