The term wellbeing data science refers to any type of information that can be used to assess physical, mental, or social wellbeing. This data may come from biological or environmental factors, as well as socioeconomic factors. Some data are collected as part of routine clinical care, while others come from research studies. Examples include phone apps and climate monitoring data. Regardless of how the information is collected, it can be used to make informed decisions and innovate with it.
One of the biggest areas for research in this area is chronic disease, which comprises a large chunk of the healthcare pie but does not fit neatly into any conventional model of care. In contrast, the WHO defines health as complete physical, mental, and social wellbeing. AI can benefit from a more holistic view of health, since six out of 10 adults are affected by a chronic illness. Much of this is caused by our daily decisions.
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While there are several types of health data science, the majority of these are general data science degrees. In addition to applying general data science skills to the health field, you can also focus on bioinformatics, medical/genetic sciences, and health economics. The discipline of health data science is relatively new and is a combination of statistics, informatics, and epidemiology. This area of data science has huge potential for improving public health, so consider pursuing a degree in this area.
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