Healthcare Will (Finally) Get Serious about IoMT Device Security
The interconnectivity of IoMT devices makes them particularly susceptible to breaches. And as wireless medical devices, such as wearables, venture outside care facility walls, they’re being used to transmit valuable patient data back to the facility via outside networks.
This type of engagement only escalates the potential for hacking and may compromise the privacy of any patient with records at that facility.
Healthcare IT teams, realizing the scale of these security gaps, are starting to explore more proactive approaches to defending their devices — and patients — starting from day one of an IoMT rollout.
Some are embracing robust inventory and network monitoring solutions to better manage their network security. Cognitive analytics are providing analysts with an in-depth view of their network, allowing them to quickly identify and respond to threats.
Others are taking a multilayered approach by deploying multiple endpoint management solutions. Boston Medical Center, in its attempt to prevent viruses and malware associated with its connected devices, has developed its own strong, highly strategic game plan.
“We have a zero-trust policy on the wireless network, where most of the medical devices transmit data,” Lee Cullivan, CISO at Boston Medical Center, recently told HealthTech. “Those endpoints are blocked from communicating with any others on the wireless network to ensure that they’re not compromised.”
As more IoMT devices are added to healthcare networks, I expect this trend to continue into 2020 and beyond. There’s simply no room for complacency.
READ MORE: Discover three ways to stay ahead of IoMT vulnerabilities.
AI and IoMT Will Become Further Entwined
By leveraging the data that IoMT devices collect, artificial intelligence is enhancing effectiveness of care and decreasing overall costs.
Business consulting firm Frost & Sullivan suggests that AI has the potential to save the healthcare industry more than $150 billion by 2025; however, only 15 to 20 percent of end users so far have been using the technology to measurably impact the way healthcare is delivered.
Although AI’s adoption in healthcare is still in its early stages, it’s already proved useful in enabling real-time, remote measurement and analysis of patient data. This approach was recently tested by a team of researchers from the University of Waterloo in Ontario, Canada. Their goal: to discover whether an accurate prediction of failing patient health could be made solely via a smart shirt.
The study, which monitored the heart rate, breathing and acceleration of healthy men in their twenties, compared the shirt’s readings with their laboratory responses. Researchers concluded that an accurate prediction of a patient’s failing health could indeed be made from AI.
“This multidisciplinary research is a great example of how artificial intelligence can be a potential game-changer for healthcare by turning data into predictive knowledge to help healthcare professionals better understand an individual’s health,” Alexander Wong, a co-author of the study, tells ScienceDaily. “It can have a significant impact on improving quality of life and well-being.”
As AI continues to evolve and instances like these keep expanding, there’s no doubt IoMT and AI will come closer together, making the everyday use of AI in healthcare a reality.
This article is part of HealthTech’s MonITor blog series. Please join the discussion on Twitter by using #WellnessIT.
