Q1 Healthcare Forums Recap: AI in Healthcare
As more healthcare institutions collect data about their internal systems, tools, and processes, machine learning software becomes more accurate and productive. Today, machine learning is empowering healthcare teams to make revenue-increasing predictions with greater confidence and precision.
A few weeks ago in Baltimore, Maryland, ElectrifAi executives Michael Fox and Nancy Hornberger discussed how machine learning technology like RevenueAi is helping healthcare organizations catch missed charges and save millions. If you missed their talk at Q1 Healthcare Forums, this article covers a few of the highlights:
A Netflix story: The power of predictive analytics
For most of us, our earliest relationship with Netflix was in the form of movies and TV shows being physically delivered through the mail to our homes. Netflix was essentially a DVD distribution company.
But within just a handful of years, Netflix shifted from distributor to producer, ultimately revolutionizing the modern television industry by creating highly-tailored and popular shows for their subscribers.
Historically, most TV studios have made shows based on gradual testing: they created a pilot, aired it on TV, and then decided to cancel or continue the show based on viewership counts and early reception.
When Netflix entered the production business, they took a different approach. Netflix invested $100+ million to fund full seasons at a time. Their internal data, based on viewer ratings of certain shows and films, gave Netflix the power to make data-backed predictions about how well certain shows would perform.
The rest, as they say, is history.
This story details the power of predictive analytics. Instead of incremental testing, companies like Netflix (and now other streaming services like Amazon) are creating content they know their users will love, based on data.
In the last few years, predictive analytics has spread into many other industries. Innovative businesses are finding new ways to leverage ML to make their data actionable. That’s why we created RevenueAi for the healthcare industry.
ML forces technology to get better
Humans have long been able to look at specific data to notice patterns. What machine learning does is accelerate that pattern recognition by not just looking at some data, but by looking at all the data.
People can’t match the speed at which machine learning uncovers correlations.
In regard to healthcare technology like RevenueAi, machine learning doesn’t abide by specific rules put in place by the healthcare institution. RevenueAi notices patterns in the data and automatically understands what charges are missing from a bill. As the system is used it gets smarter and smarter.
Rules based systems are a never-ending treadmill of complexity which increasingly fail to recognize missing charges.
To keep our products relevant and helpful to our customers long term, we are constantly working to improve our ML technology to ensure it continues to catch errors and unproductive patterns in healthcare revenue missed charges. In other words: as your business improves and changes, we are also constantly improving our technology to catch new and increasingly complex patterns.
Our tools get better with age.
Michael Fox and Nancy Hornberger covered a lot more ground in their talk than we could fit in this article. Learn more about RevenueAi here or learn more about attending future ElectrifAi talks by visiting our events page.
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