The healthcare industry is flooded with an overabundance of raw data. The problem lies in figuring out the best way to manage this data. But that aside, all of this raw data can actually be used to solve many day-to-day problems in the healthcare industry. Predictive analytics is rapidly becoming one of the most-discussed, perhaps most-hyped topics in healthcare analytics. Predictive analytics is used for extracting vital information from existing data. This helps to understand various patterns and also predict future trends and outcomes. It has a bright future in the healthcare industry because of factors such as the important need to understand the raw data and its meaning, and a significant number of business-oriented applications to which data analysis can be applied. However, predictions made solely for the sake of making a prediction are a waste of time and money. In healthcare and other industries, the prediction is most useful when that knowledge can be transferred into action. In order to implement a predictive analytics solution in the healthcare industry successfully, it’s important to have a clear vision of the desired outcomes, have IT systems in place that are interoperable, and have a strong commitment to knowledge-sharing across departments.
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Here are some of the key steps for setting up predictive analytics for your organization:
Understand Organizational Requirement
Predictive analytics is designed to make a real impact on existing organizational processes and to improve efficiency. The healthcare industry possesses an abundance of data, but the major challenge most organizations face is determining what to do with all of the data and how to make sense of it. There is too much data available in almost every aspect of healthcare, but this large amount of data also opens up the potential to implement a predictive analytics system. A predictive analytics platform can provide a strong pathway for providing answers as to what to do with this data.
Collect and Cleanse Data
Once companies in the healthcare industry have identified their analytics requirement, the next step is to sort the data. It’s important to define where exactly the data is coming from in order to cleanse it. This process includes a variety of tasks such as dealing with redundant data, missing data, and unformatted data. The most important thing is that the dataset is standardized so that the process can be automated.
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Focus on End-user
In the healthcare industry, analytical results must be made available to nurses, physicians, and insurance analysts who mostly deal with day-to-day operations. Therefore, predictive analytics platforms must be easy-to-use and accessible across an organization. Professionals in the healthcare industry deal with large amounts of data every day, so the results obtained from an organization’s predictive analytics platform must be integrated smoothly into their existing daily workflows to be truly beneficial.