OnWatch Predict

Prevent unplanned downtime and reduce disruptions with the power of 24/7 remote monitoring and GE Healthcare predictive analytics software.

AT A GLANCE

Monitor

Keep a close eye on critical component indicators and performance trends.

Predict

Apply healthcare predictive analytics to cut unplanned downtime by +40%.

Repair

Plan repairs far in advance, around your schedule, to fix emerging issues.

Restore

Maintain image quality and help improve uptime by 4.5 days per year.

OnWatch Predict™ for MR takes predictive analytics in healthcare to a new level—helping you leave unplanned downtime behind

Gain peace of mind with healthcare predictive analytics software. Leveraging Artificial Intelligence, OnWatch Predict for MR watches out for you 24 hours per day, 7 days a week. It helps you spot and prevent potential problems with critical components, and gives you considerable time to plan maintenance around your schedule.

Driving predictive, preventive, planned maintenance

40%

Up to 40% reduction in unplanned downtime, anticipating component failure before it happens.¹

4.5 days

Days per year of increased uptime and reduced operational disruptions.¹

35%

Up to 35% reduction in customer-initiated service requests–less stress for your team.¹

24/7

Continuous monitoring of your equipment to help maximize uptime and maintain image quality.¹

THE ONWATCH PREDICT ADVANTAGE

Overcome the disruptions with healthcare predictive analytics

Just one day of unplanned downtime can mean canceling 18 scans (2) and losing ~25,000€ in revenue (3) But what if you could see a system failure coming from far away? You could maximize on-time exams. Minimize disruptions. And optimize exam revenue.

REAL TIME MONITORING

Keep close, continuous watch on all critical MR components.

OnWatch Predict uses healthcare Internet of Things technology and AI learning applied to real-time equipment data to drive new predictive analytics in healthcare.

Unlock valuable new insight encompassing:

Environment: Temp & humidity, System coolant supply, Notching detection
Coils: SNR degradation, Low signal channels, High noise coil, White pixels
MR magnet: Compressor status, He levels, Cryocooler efficiency
Patient handling: Cradle failure, Table alignment
Main system: LV shim calibration, Fan failure HD health

VIRTUAL SYSTEM

Experience the predictive power of "digital twin"

OnWatch Predict compares your machine data against a digital twin—a virtual avatar of your system—to create a unique virtual model, based on AI learning. Information is collected and processed, predicting the estimated remaining life of all monitored components.

PREDICTIVE INTELLIGENCE

Reduce disruptions and maintain image quality

If the OnWatch Predict algorithm identifies a potential failure, GE Healthcare is well positioned to provide advanced part delivery and service scheduling. Any corrective action can accommodate your team’s schedule. So you can help reduce overall limited use or unplanned medical equipment downtime, and better plan for a smoother repair experience.

REAL TIME ALERTS

Gain anytime, anywhere control of corrective and predictive maintenance

Whether you’re on a desktop or mobile device, you can get OnWatch Predict alerts (such as part notification, planned maintenance, and system fix, etc.) and manage service and support in near real time.

Advantages

Reduce disruptions. Enhance care.

Greater confidence

Avoid unplanned downtime, and better plan and expedite maintenance when you need it.

Financial performance

Optimize capital investment, maintain exam revenue, and optimize labor costs

Operational efficiency

Decrease staff stress, reduce mistakes due to overtime fatigue, and minimize workflow disruptions

Patient satisfaction

Reduce exam rescheduling, reduce patient stress, and increase on-time diagnosis

FOOTNOTES

1. GE Healthcare calculations based on 1500+ systems across multiple regions in 2020.
2. IMV study conducted in January 2020 with average 380 exams per month in the United States.
3. Estimated amount for direct and indirect revenue loss impact.

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