An Introduction to Predictive Analytics Models
For businesses operating in the healthcare sector, descriptive analytics can only teach so many lessons. Yes, it’s valuable to have data regarding prior performance, but it’s just as important to understand how past trends will influence future results. And this is especially true for healthcare providers –– considering the ever-shifting landscape in which they operate. Naturally, this leads professionals to build predictive analytics models in order to establish a vision for how their business will progress in the future. But how do these models work, and how can you use them to improve the nature of your operation? We’ll explain below:
Forming a Predictive Analytics Model
Before attempting to use existing data to make extrapolations about what’s going to happen in the future, you have to first understand how to create an effective predictive analytics model. Obviously, the ins and outs of this process can become very intricate, but on a basic level predictive analytics depends on:
- Rich pre-existing data. Of course, the more information you can draw on (including current patterns), the better.
- Clear objectives. As we’ve covered before on this blog, specificity is crucial to connecting analytics to results. Rather than attempting to predict how every aspect of your company will perform over the next ten years, it’s much wiser to focus your efforts on specific targets, departments, or demographics.
- Managing variables. Any time you attempt to predict an outcome, you have to understand the variables involved. This will typically require a level of critical thinking and attention to detail to fill in certain blanks and eliminate redundancies within the data.
- Data testing/sampling. This ensures you can correct any errors in your model before implementation.
- Deployment. Unsurprisingly, the effectiveness of predictive data analytics depends on a company’s willingness to trust it and use the data actively. All the information in the world won’t help you enhance your performance if you’re not willing to use it!
What are the Different Types of Predictive Analytics?
Predictive analytics is a versatile field, and it can be applied to a number of scenarios in the healthcare industry. In many instances, the type of algorithm used for a predictive model will depend on the desired outcome. Does a company want to cut costs or manage risk? Predict income for the next year or determine a more efficient way to spend their capital? Sometimes businesses can use pre-existing algorithms to form a functional predictive model, while in other cases, analysts may have to devise a customized method for the scenario.
The best predictive analytics models don’t just take information from years past and regurgitate it in a new format. Rather, in order to achieve meaningful results, analysts need to understand everything about a business’s goals, challenges, and circumstances. At Amitech, we specialize in providing data solutions for healthcare providers, which means that we appreciate the unique (and constantly changing) features of the healthcare industry. For more information about how we can help you use data analytics, contact us here!