Nowadays, governments world over are pushing healthcare organizations towards decreasing the healthcare costs. The healthcare solution providers today also have increased liabilities, which forces them into identifying and helping their chronic care patients in managing their conditions better. The IT vendors today have simultaneously been working on expanding their big data armories. This will help providers such as accountable care organizations in mining claims and clinical data, which in turn will help them in getting a better understanding of their patients’ outcome, performance and ways of cutting costs.
With time, more and more healthcare solution providers are transferring their on paper data on to the cloud with the help of IT health vendors. By putting their data on the cloud, health care providers have been able to accurately assess their patient information. By doing this, they can mine patient data, which will help them predict outcomes. The providers today, use four different types of data analytics for quality involvement in population health. The four types of data analytics used are as discussed below:
This type of data analytics is used for a large chunk of big data across the industries. It focuses on assessing aspects related to outcomes in terms of why they are higher or lower than expected. For the provider, it helps them in assessing their patients’ requirements, etc.
Under predictive analytics, big data is generally used for the purpose of identifying patterns, know how to predict future outcomes and preventing events in order to reduce healthcare solutions cost.
Recent statistics suggest that healthcare solution providers are showing greater interest prescriptive analytics of their patient data. This type of data analytics can help the providers monitor and manage a specific population, for e.g. those suffering from heart problems.
Comparative analytics allow providers to use big data in a much more interesting way. They can compare their patient data across the various healthcare facilities. They can compare the data of their patients over a specific health issue over several criteria such as age, race or geographical location. This data can then be used to improve their own performance.