In US, approx $3.8 Tper year are spend on healthcare, and approx $93 billion of it this staggering amount is spent on medical equipment lifecycle costs? As per Becker’s CFO Report estimates hospitals are missing savings as much as 12 to 16 percent due to “lack of accurate information, internal resources, bandwidth, and specialized expertise.” That averages out to $12,000 per bed per year!
The advancement in technology and innovation is bringing more connected devices and smart machines in Healthcare. There is no denying that advancement in MedTech is bringing greater efficiencies for hospitals, but it also needs a paradigm shift in the life cycle management of critical assets.
Currently, nearly 40 million MRI scans occur per year, and with a growing population, this number is likely to increase. Is your hospital prepared?
Medical devices like CT Scanners, MRI machines, and Surgical Robots are one of the most expensive devices and require multi-million dollar investments to buy/lease, deploy, run and operate. For smart healthcare provider or service management vendor or manufacturer of these machines, efficient running and operationality are extremely critical. With the multiplayer engagement, complex service contracts and stringent SLA and liabilities, high maintenance costs, and time constraints on medical treatment, the industry can benefit from the Internet of Medical Things.
Let’s closely examine, how the advancement in Technology, combing networking, software, and hardware via the Internet of Things can help in overcoming the above challenges.
As per Becker’s CFO Report estimates hospitals are missing savings as much as 12 to 16 percent due to “lack of accurate information, internal resources, bandwidth, and specialized expertise.” That averages out to $12,000 per bed per year!
Monitoring MRI Machine?
By producing detailed pictures of internal body structures, MRI machines are playing a critical role in the diagnosis and treatment o medical conditions. Spotting strokes, tumors,, spinal cord injuries, multiple sclerosis, and other disorders are some of the notable diagnosis done using MRI machines. The machine and its setup (machine, room, and managing the EMI/RF) make it challenging to install, run and operate the machine. For optimized operation, MRI machines are of one not only needs to monitor the information related to your cryostat, HVAC, chiller, power supply, but you must also be able to get any alerts if the helium boil-off is increasing, or the cold head or compressor is not functioning properly. And even if you have scheduled maintenance happening, a simple issue can put you at risk of a magnet quench. And this is about the machine, what the environment of the facility – the temperature, humidity, Air Quality and much more. Any downtime in the medical machines is a loss of revenue and can impact patient care and well-being.
Continuous monitoring, real-time alerting, and data insights from the MRI machine and the room help in improving the asset life by focusing on preventive and predictive maintenance. By leveraging the data and bringing AI&ML to the edge, one can do better diagnostics as well as issue commands to the machine (Via relays) is such a situation arises.
To address the above challenge, Revca has created a platform that leverages the computing power of the gateway, collects data from machines at the node level, and augments it with various sensors, hence creating a perfect Digital Twin of the entire ecosystem. It has data on machine, temperature, humidity, air quality and can also identify the foot traffic or any foreign particle/object left in the room by accident. Our gateways have the flexibility to customize the solution as per the unique needs of the hospital, thereby enhancing the ability to do pre-processing on the edge. Our cloud platform analyzes the real-time data streams, leverages the AIML algorithm towards predictive processing. Combing the power of edge computing as well as the processing in the cloud, we are able to provide real-time alerts, remote analysis and fault location, micro-level monitoring of parts and components, and remote condition monitoring. And all the communication happens over secure network layers and protocols, and best-in-class encryption for data in transit and at rest.