Maximizing uptime of diagnostic equipment is vital to both patients and healthcare organizations. As medical imaging equipment becomes more sophisticated and the need for healthcare organizations to improve their availability becomes more acute, so does the value of machine log data and advanced analytics. Here, we’ve listed several important ways that hospitals can use machine log data and predictive/prescriptive analytics to optimize operational efficiency and revenues.
- Avoid expensive equipment failures with predictive analytics
Predictive analytics solutions apply AI and machine learning to predict and prevent machine failures by collecting and analyzing patterns in machine log data, recognizing problem indicators and alerting engineering teams. Anomaly detection is vital to fixing issues early before they become much larger and more expensive problems, potentially rendering the entire system unusable. Solutions such as Glassbeam can increase machine uptime to 99.5 percent – saving millions of dollars per year in lost revenue. More on anomaly detection: Machine Learning Based Anomaly Detection: Driving Proactive Machine Maintenance
- Monitoring environmental variables can ensure that medical imaging equipment is operating within safe parameters.
The continuous sampling of environmental machine log data (temperature, humidity, and current flow) can provide insights into the external conditions in which the medical imaging equipment is operating. These external conditions are directly correlated to the health of the medical imaging equipment and its support equipment.
Let’s say a user has been sampling the temperature of an exhaust water pipe for several weeks. During this time that user has created a baseline for an acceptable range in which the exhaust water pipe temperature can fluctuate. But one day, within an hour, the sampling data shows the exhaust water pipe temperature has exceeded the baseline by over 15 degrees. Why has the temperature increased in such a short period of time? Are the chillers not operating correctly? Is the imaging equipment overheating? These external changes detected in the environment are a good indication there is an issue with either the imaging equipment or its support equipment. The imaging equipment may still be operational, but the environmental changes indicate that there is a problem that will eventually affect the imaging equipment.
- Turn unplanned downtime into planned downtime
When you can use predictive analytics to anticipate a potential problem, organizations can plan downtime for equipment maintenance during non-peak hours. This avoids interrupting patient care while maximizing profitability and avoiding lost revenue. Improved machine uptime is a win-win for both organizations and patients, who take time out of busy schedules and off work to get imaging tests.
Visualize potential savings through predictive analytics: Machine Learning and Predictive Maintenance Maximizes Healthcare ROI
- Understand and optimize equipment utilization and budgets
Advanced analytics offer a snapshot of imaging equipment utilization and performance at a glance. The ability to collect, structure and analyze machine use data gives healthcare organizations a better understanding of exactly when and how they are using these expensive machines, including the number, type, and duration of procedures, uptime and idle time. Machine data can also alert operators of overuse, allowing teams to adjust as needed. For example, if a hospital has 10 ultrasound machines, analytics can help identify which machines are being overused – which could lead to part failures – and offset the extra usage onto a machine that isn’t being utilized to its full potential. This allows organizations to optimize budgets and utilize the equipment and people they already have to create more value and optimize the entire healthcare cycle before having to buy more (expensive) equipment.
- Identify gaps in equipment manager training and operation
This insight into imaging equipment utilization also offers healthcare organizations a look at the people behind the machines; such as how efficiently operators are handling the equipment and what can be improved. By viewing aggregated time taken per procedure, machine, operator, and facility, healthcare systems can set benchmarks for equipment utilization that can be used to identify and address gaps in training and balance load.