The healthcare industry has a data problem. There is an overabundance of raw data available, and figuring out the best way to manage this data has presented many challenges. For example, the data from patient records from heart-rate monitors is quickly referenced and then forgotten. The good news is, all of this raw data can actually be used to solve many day-to-day problems in the healthcare industry using predictive analytics.
Predictive analytics is the practice of extracting information from existing data to determine patterns as well as predict future outcomes or trends. Predictive analytics has a bright future in the healthcare industry because of the numerous factors already in place that are working together: the enormous amount of data, the important need to understand this raw data and its meaning, and a significant number of business-oriented applications to which data analysis can be applied.
We are only at the beginning of using data analysis effectively in healthcare; however, predictive analytics can be used to solve many of the real-world problems the healthcare industry faces today. To successfully implement a predictive analytics solution in the healthcare industry, it’s crucial to have a clear vision of the desired outcomes, to have IT systems in place that are interoperable, and also, the organizations must have a strong commitment to knowledge-sharing across departments. If a healthcare organization can meet these demands, then here are some of the key steps for setting up predictive analytics for your organization.
Predictive analytics are designed to make a real impact on existing organization processes and to improve efficiency. In the healthcare industry, the data already exists in abundance, so the challenge most organizations face is determining what to do with all of the data and how to make sense of it. There is simply too much data in nearly every aspect of healthcare, but where that data exists, there is also potential to implement a predictive analytics system.
A predictive analytics platform can provide a strong pathway to answering the question of what to do with this data, and predictive technology has a wide range of applications in healthcare. For example, it can be applied to disease management, physician profiling, or the handling of no-shows and appointment scheduling.
Tackling these types of issues requires a comprehensive analytics platform that can not only automate and visualize data but also be used as a collaborative tool. Furthermore, it is equally necessary that an analytics platform proactively informs organizations for specific reasons to help with decision-making.
After a healthcare organization has identified its analytical needs, the next step is to collect and cleanse the data. It’s important to define from where exactly the data is coming in order to cleanse it.