Data analytics is becoming an increasingly important tool in a healthcare setting. The examination of both current and historical data provides the opportunity to identify trends, and areas for potential improvement, and to draw conclusions. Ultimately, it enables clinicians to deliver faster and more accurate patient care, in a cost-effective and streamlined way.
What type of healthcare data is collected?
The data can be from individual patients, or from the population as a whole. Data can also be collected on the business and economic processes of the healthcare sector. The data can be drawn from healthcare organisations, the government, and insurance companies, via electronic records, prescription services, patient portals, and more.
The impact of the pandemic
The Covid-19 pandemic brought the role of data analytics to the forefront of healthcare. It was used to aid decision-making, predict possible future outcomes, improve patient care, allocate resources, and much more. The amount of data recorded, collected, and analysed during this time presented both huge opportunities and challenges.
The different types of analytics
In order to clarify and refine the analytics process, it is broadly divided into three different types. Descriptive analytics uses historical data to identify past trends and patterns, which enables analysts and clinicians to make comparisons and helps them make more informed current decisions.
Predictive analytics uses both historical and current data to predict future outcomes and trends. This is especially helpful in cases where there is a mass outbreak of a novel disease, such as we are experiencing at the present time. Prescriptive analytics is used to help make decisions about the best course of future action.
When used effectively, big data analytics can predict treatment outcomes, identify risks, and improve patient experience both on a micro and macro level.
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