Machine Learning and Predictive Maintenance Maximizes Healthcare ROI

Vijay Vasudevan

Predictive analytics can be used to reclaim millions of dollars in operational costs for healthcare organizations.

As pressure mounts to lower healthcare costs, healthcare delivery organizations are taking a closer look at costs in all aspects of their business, particularly operations. More organizations are realizing there is a huge opportunity to lower operational costs by leveraging machine data and machine learning.

Data from today’s connected equipment, such as MRI machines and CT scanners, offer real-time insight into performance and functionality. Glassbeam’s predictive analytics solution can aggregate data from different machine types and manufacturers. By leveraging machine learning, the solution can predict and avoid unnecessary downtime, reduce maintenance costs, and maximize profitability from capital-intensive medical equipment.

Measuring the Business Impact of Predictive Analytics

We’ve created a Glassbeam Healthcare ROI Calculator so you can easily visualize potential saving. More importantly, you can customize the inputs to match the needs of your own organization for a more accurate picture of the business impact.

The algorithm predicts the total cost savings from using Glassbeam’s predictive analytics solution, estimating savings by calculating inputs including the number of MRI and CT machines, procedures per hour, operating hours per day, operating days per week, and current uptime levels.

There are two outputs, cost savings and revenues recovered. Healthcare organizations often shell out hundreds of thousands of dollars each year in service contracts to original equipment manufacturers (OEMs) and independent sales organizations to manage equipment. Cost savings indicates the amount of money that can be saved by optimizing these service contracts and by using proactive and predictive analytics to predict and prevent costly equipment failures. Revenues recovered refers to the revenue gained by increasing machine uptime from 96 percent to 99.5 percent, for example.

How Much Can Healthcare Organizations Save Through Predictive Analytics?

Let’s run an example. Consider a healthcare provider with five MRI machines that each run one procedure per hour and five CT scanners that run each run two CT procedures per hour. And assume those machines are operating 10 hours per day, six days per week, with 96 percent uptime (which is 12.5 downtime days per year, excluding planned downtime). Over 3 years of using Glassbeam analytics solution, that provider could increase uptime to 99.5%, recover nearly $5.3 million in revenue, and save more than $1.3 million in costs by optimizing service contracts.

A larger provider with double the number of machines and 12 operating hours per day could save more than $12.6 million in recovered revenue, with cost savings of more than $2.6 million.

That’s a lot of money that hospital operations teams could redirect to improved patient care, equipment upgrades and related uses.

Learn More About Predictive Maintenance

Curious about how much money healthcare organizations could save through predictive analytics? We invite you to test the Healthcare ROI Calculator out for yourself.

For a deeper understanding of the results and to find out how Glassbeam can work for you, feel free to talk to one of our team members.