Before we continue our deep dive into discussing predictive analytics in healthcare, I want to make sure you’ve had a chance to read the earlier posts in this series. If not, I’d encourage you to take a minute and check them out as they help to build into this discussion and also offer some insightful content.
For today, I’d like us to look at some of the framework that goes into predictive analytics in healthcare and pull back the proverbial shroud of mystery that sometimes hangs over the topic. You see, predictive analytics offer an abundance of value in the healthcare sphere, but only if those in charge of decisions and adoption truly understand the value the technology brings.
And understanding that value starts with understanding the building blocks and framework of the technology as a whole. If you’ve ever felt confused by some of the terminology and ideas you hear about regarding predictive analytics and artificial intelligence (AI), this post is exactly what you need to help.
When it comes to classifying analytics, there are several different ways to separate things out. The two most common ways of doing this are classifying the solution based on its functional arena (operational, clinical, etc.) or by the type of solution that it is. The latter is what I’d like to take a minute to define today, as it’s a strong way to start identifying the types of analytics solutions you may be seeking.
An example of descriptive analytics would be looking at different occurrences within your business or clinic. These types of analytics solutions always look at data in hindsight, or ‘WHAT happened?’.
An example of diagnostic analytics would be looking at why your referrals went up or why you were able to get more five-star reviews online. These analytics solutions are a mix of looking at data in hindsight and applying some insight into causation.
This is one of the biggest and most exciting areas that we’re seeing growth in the healthcare analytics field. An example of predictive analytics would be the ability to predict the likelihood that a client is going to self-discharge or miss an upcoming appointment. Software solutions that do this (like the one Net Health created) can have impressive impacts on a practice’s bottom line by helping you to maximize efficiency and cut down on losses from cancellations and no-shows.
Employing a mix of insight and foresight, prescriptive analytics are more about the actions you can take to make certain things happen. For example, having your clinical management team use certain communication practices to prevent negative experiences or missed visits is an example.
The idea of computers being able to learn and get better at tasks normally completed by humans is no longer just a wild idea or theory. Today, the healthcare industry is seeing rapid growth in the creation and adoption of artificial intelligence (AI) technology solutions. But what exactly is AI? Is it the same as machine learning? Deep learning? Let’s take a minute and sort this out.
All three of these technologies are interconnected. For a visual, imagine that the United States is AI, a handful of the states within the United States are machine learning, and then a few cities within those select states are deep learning. In other words, deep learning is a subset of machine learning which is a subset of AI.
Here’s a definition of each.
- AI is the simulation of human intelligence by machines; best suited for tasks that would take too long or are too difficult for humans to do by hand.
- As a branch or subset of AI, machine learning is the use of algorithms that use training data to identify patterns and predictions.
- As the most sophisticated on the list, this subset of machine learning refers to things like “deep neural networks” where the machine utilizes several layers to learn and adapt the processes.
A Great Next Step
If you’re enjoying this discussion, I’d highly encourage you to follow this account as this is an ongoing series of discussions on the ins and outs of predictive analytics in healthcare. Over the coming weeks and months, we’ll be diving deeper into some of the frameworks outlined today, which could provide some unique opportunities, insight, and ideas you can utilize in your practice!
From Data to Healthcare Insights: a Net Health Series
Also, be sure to check out an ongoing webinar series on analytics, in which we interview experts such as Dr. Bill Winkenwerder, former Assistant Secretary of Defense for Health Affairs for the U.S. Department of Defense, among others.
How Predictive Analytics is Revolutionizing Healthcare
The Expanding Role of Artificial Intelligence for Clinical and Operational Decision-Making