Patient data consists of all medical information concerning individual patients. Not only does this mean patient history, diagnosis, treatment, but increasingly means behavior and lifestyle information.
Doctors, nurses, and other health professionals record patient information based on tests and observation. Patients also provide data about their lifestyle and behaviors. They do so either in appointments with doctors and nurses or via wearable devices like smart watches.
Additional sources include research projects, such as the Human Genome Project, and family histories and historical records.
This data category consists of traditional medical data like health history, diagnoses, current drug treatments, and so on. New columns of information include genetic markers, lifestyle, and behavior data.
Naturally, this type of data records and helps manage and diagnose individuals with medical issues. It should also provide material for researchers to study diseases or the human body more generally.
Researchers also expect that AI programs will soon predict medical issues before they appear, thus transforming medicine into a preventative endeavor
Due to the variety of potential sources, you may find this data difficult to integrate consistently. If you establish clear goals and data integration standards at the outset and regularly update and cleanse it, you should have a high quality dataset.
But as Meyer pointed out, COVID-19 and the push for more remote care has offered a key use case for virtual platforms enabling patient data access. These types of personal health records, as well as patient portals, might be an opportunity to let patients be in control of coordinating their own care.
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