Make Way for Intelligent Hospital Infrastructure (IHI)
When you think of possible scenarios that could shut down a hospital, it is usually fire, earthquakes, or some form of physical disaster. However, there is growing threat to medical institutions of all sizes from medical equipment failure. Medical device failure occurs when the device fails to perform its intended objectives. These failures can cause unexpected adverse incidents as the safety of the device users (including patients) and technicians may be at stake.
Resolving medical device failures can prove to be complex. As a case in point, on this active LinkedIn forum for CT SCANNER EQUIPMENT, we found several solutions for troubleshooting problems to medical devices. Often, it means a field service engineer must go through these alternatives to discover the root cause of failure. With billions of devices getting ready to be connected, these legacy workflows will prove to be prohibitively expensive and ineffective.
Diagnosis begins at the home
Medical institutions do have systems in place that ensure the reporting of devices issues, effectiveness of device management systems, and their condition and performance including utilization, maintenance, repairs, and calibration history.
But here is the catch: these information rich datasets are unstructured, complex, and come in a variety of formats. Take the example of the CT scanner equipment: error logs typically reveal a lot of useful pointers for a field engineer to for resolving device issues. Apart from the logs, there are mechanical parts, for instance the LED lights that indicate something is wrong and electronic parts such as resistors, transmitters etc. that needs to be checked to ensure they are functioning as intended.
What if field technicians could know beforehand that a particular CT scanner is going to go down?
Business leaders are looking to avoid multi-year lock in for deployments. Instead, the expectations are to find a solution that is lean and adaptive. This lean solution is expected to handle complexity, inter-connectedness, and diversity in data sources. As a result, finding ways to discover insights from assets that already exist in any equipment across the medical institution’s install base is crucial to effective decision making.
Convergence is the secret sauce
It isn’t about confirming the information and decisions we already know about the device or have considered in the past, it is about gleaning predictive insights like “Hey your CT scanner in the Entomology lab located in building Z is likely to fail, because I can see part no: 1EX300980 resister is getting overloaded”.
This knowledge we have about the machines and an intervention using “prescriptive” log analytics can reduce device failures significantly. How?
Enter, Predictive Analytics
We recently made available a state-of-art predictive analytics solution on our SCALAR PLATFORM. This new age machine is ready to take on hundreds of thousands of data sources and make available information that can be ingested easily.
Here’s what medical institutions should expect in the future: an intelligence hospital infrastructure (IHI). This infrastructure will have state-of-the-art IOT Analytics on telehealth devices, offer remote patient monitoring machines by intelligent sensors, will include Technology pathways to discover equipment stability in real time that include combining learning from every potential data source. Futher, this infrastructure will offer abyss-level institution-wide visibility and have capabilities for converged device management.
Big data analytics means better hospital infrastructure and by association, better health. To learn more about our offering in this vertical, please visit our MEDICAL DEVICE PAGE
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