Big data in healthcare
It refers to medical practices of storing, gathering, and evaluating data to understand public health in a better way and provide them with a higher standard of personal care.
Medical practices now have more data generated than they’ve done ever before. This is because digital programs, tools, and applications are more prevalent and are used widely.
It is not viable for human intelligence to analyze such information themselves; instead, healthcare systems are using digital means to assess the captured data. They further provide practitioners and other professionals in this field with actionable understanding to assist their decision-making.
Importance of big data in healthcare
When several figures and statistics claim to have more data, it does not mean a little bit more, it means an industry-changing amount more – in the last five years, the healthcare companies have had data that has multiplied nine-fold.
According to a compiled statistics report by Dell EMC, healthcare companies have witnessed an explosive health data growth rate of 878 % since 2016.
Whether it is retail or manufacturing business, healthcare, or any other sector all of them are affected by massive big data growth, which makes it important to use big data analytics in the healthcare sector.
Using big data in healthcare
Management of staff
Staff management is very important in hospitals. As costs rise, overstaffing can have a significant impact on profitability. At the same time, understaffed healthcare providers are not seen favorably by patients when their care is compromised. By implementing big data in healthcare and setting up a system that can evaluate past entry rates, a large amount of data can be analyzed automatically, so healthcare can quickly see the busiest time zone. Several hospitals are already doing this. For example, a medical practitioner used machine learning to screen 10 years of hospitalization data. The analysis obtained showed that shift supervisors predict enrollment rates for specific days over the next two weeks and use them to more efficiently allocate shifts to employees.
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Most of us are aware of wearables, but the vital information they provide and the use of wearables in healthcare as a means for medical practitioners to evaluate patients is another way for healthcare providers to use big data. The portable device can be used by consumers for a variety of purposes. Here are some examples:
Fitness Tracker: FitBits is probably the best-known example, equipped with sensors that help monitor heart rate and track the physical activity of users.
EKG device: Allows users to heart rate variability, monitor heart rate, temperature, and respiratory rate.
Blood pressure monitor: Measures blood pressure using oscillometric technology
What are their uses?
Well, these tools of big data in healthcare can report the data directly to the patient profile saved by the doctor. If patients experience any abnormalities such as heart rate, blood pressure, temperature, they can warn the doctor further to get in touch with the patient to make an appointment.
Big data analytics in the healthcare sector relies primarily on structured data for automatic scanning. The problem, of course, is that this field (like many other industries) is flooded with unstructured data that poses problems for people to use it most effectively.
80 percent of medical data remains unstructured after creation ( text, images, signals, etc.). It has long been ignored, not stored, or abandoned in most medical centers. This is why medical providers are turning to machine learning to analyze data and provide actionable advice that doctors can use. Essentially, this means that unexplored data that may have been missed before are now available and can be used to identify diseases that were not previously apparent.