The Internet of Thing (IoT) has been a subject of global interest over a couple of decades and the healthcare sector has started to comprehend its enormous potential and benefits in recent years. The technology has recalibrated the care space with its limitless applications in the structure.

IoT in healthcare can improve the quality of service and drastically condense healthcare costs. It has already taken place in some parts of healthcare, and it has much more potential to radically transform hospitals and medicine.

Increasing adoption of IoT has potentially offered life-saving applications within healthcare. By gleaning data from bedside devices, observing patient information and diagnosing in real-time, the entire system of patient care could be advanced with the help of this tech, including the patient experience.

IoT Risks in Healthcare

As the usage of IoT devices is growing rapidly in healthcare, it can also pose solemn risks to patient care and has the potential of causing major harm to people. Since the technology is not unique to healthcare at all, care centers have quickly embraced single-function IoT devices to advance mobility, expedite the patient care procedures and augment clinical productivity.

As per the reports, network-connected glucose meters, bedside heart monitors, EKG machines, infusion pumps, and environmental monitors are now littering the healthcare access networks in a move to escalate mobility and flexibility to accelerate the delivery of critical care services.

IoT devices can be utilized to subvert hospital networks, control the impact of hospital operations or even be monitored to steal private patient data. This is simply not an isolated problem with a few devices. Just take a look at environmental sensors, which market is projected to reach US$2.3 billion by 2023. These network-connected sensors are designed to monitor everything, including temperature, humidity, air, and water quality. In healthcare, environmental sensors are used for a number of essential purposes.

Environmental sensors and other new emerging IoT devices have been built to provide Biomed clinicians and healthcare facilities the essential information about maintaining and supervising a sterile environment, critical for patient health and necessary treatment. Besides the benefits, some big risks also appeared or come with these devices.

Similarly, most devices on a network, IoT devices also use a common network process, called DNS (Domain Name Server) to get the IP address of a remote server to make a connection. But unlike traditional client devices like laptops or smartphones, IoT systems are generally designed to connect to the specific manufacturer’s servers to check-in or gain software upgrades.

The DNS process is utilized to translate a server name into an IP address to which they can connect. And the DNS servers that client devices are supposed to utilize for this purpose is typically mandated by the network administrator for any given company through static configuration or using a DHCP service. In this case, several environmental sensors were simply avoiding their configured DNS and redirecting the devices to illegal DNS servers. While the DHCP service was indeed handing out the corporate DNS servers, the environmental sensors still reached out to unauthorized DNS servers.

It means the IoT devices receive false data that caused them to connect to very vulnerable service in the cloud that they don’t suppose to. This may lead to a major security and protocol breach, as well as hijacking web traffic to deliver ransomware.

Once a device within the corporate network is formed a connection with a non-sanctioned device elsewhere, a range of breaches can take place. Afterward, the breaches potentially enable the software to be placed in the IoT device that will allow various wicked attacks or unencrypted data from the devices that can be amassed. And this will create the nastiest scenario for healthcare organizations.

The presence of artificial intelligence-powered technologies in the market can help in overcoming these challenges and other IoT threats. The solutions are designed to employ constant network traffic analysis to recognize anomalies that indicate potential issues. In such solutions, machine learning algorithms are one used to quickly establish baselines, analyze behavior or link all the dissimilar dimensions of the network to determine which network is best.