Healthcare IoT

How Network Analytics Can Enhance Biomedical Engineering Workflows

3 min read

In large organizations it's only natural for most of the workforce to ignore network analytics — mentally relegating it to the  domain of IT and IT alone. In hospitals, more often than not, that type of mentality proves a mistake. The insights extracted via network analytics offer serious opportunities for operational improvements and added value across departments.

Of course, there are also significant ancillary benefits to be enjoyed from a more cyber-aware and "plugged in" organizational posture including improved information sharing, collaboration, and security. 

Luckily, with modern technological solutions, contextualized network data analytics are readily available and can be made easily accessible. Not only does that go a long way toward protecting the network from malicious attack, but the insights gleaned can be used to improve operational capabilities and efficiencies.

Indeed, these days the challenges around smart network analytics have less to do with the availability of actionable insights and more to do with data siloing and the cultural barriers to interdepartmental communication and data siloing.

As more and more connectivity is built into medical devices and healthcare services, interdepartmental communication becomes increasingly essential to promoting a secure organizational culture. In a hospital setting, perhaps the most acute benefit felt from taking network analytics beyond the corridors of the IT department is the value added to and friction removed from HTM workflows.

Biomedical Engineering in Clinical Context

Biomedical engineers have a long list of important responsibilities. They are charged with managing an organization's fleet of connected devices — from procurement, to inventory tracking and management, to quality assurance, to hardware maintenance and performance management, to software patching and updating.

Biomedical engineers are also responsible for decommissioning devices that have reached the end of their lifecycle and disposing of them in a secure manner. Finally, biomed is responsible for ensuring the organization remains in compliance with all regulatory considerations related to connected devices and data privacy.

To put into perspective just how big of a job the biomed department is facing, modern medical facilities average 15-20 connected medical devices at each bedside. According to the American Hospital Association (AHA), there are a total of 931,203 staffed hospital beds across the United States. That means that just at the bedside, there are over 16 million connected medical devices in the US. The total number of healthcare IoT devices is estimated to be around 120 million when factoring in non-bedside devices such as nurse workstations and lab equipment! 

With biomedical engineers positioned as the de facto "owners" of medical devices, that increase in connectivity has also meant a hearty increase in responsibilities. 

The Need for Network Analytics in the Modern Hospital

Analytics and automation have been boosting industrial productivity for decades. Over that time, the healthcare industry has been the exception. There are plenty of reasons to explain why healthcare has been digitally laggardly, but none of them have to do with a lack of applicability.


Indeed, one could argue that there are more (and more valuable) applications for smart technologies in healthcare than in any other industry — with the potential to issue more accurate diagnoses more rapidly, to communicate and coordinate disease and contagion control strategies more widely, to better customize treatment plans for each patient, to reduce strains on overtaxed infrastructure, improve patient comfort, and generally collect and respond to information more dynamically. 

Today, the technological means are finally being put into place and the appropriate processes implemented to see many those potential applications through to actualization. Driving this digital transformation is technology that collects and analyzes information putting it in the right hands at the right time to take the right actions.

While some of these technologies process information at the edge, most rely on more traditional network computing models that relay data between endpoints to servers, with various nodes and workflows interacting along the way to intelligently process, share, and action the information. 

When we inject data analytics into this connected ecosystem at the device or endpoint level, we can see the information being capturing from the field or patient and how it's used. This type of information is typically reserved for the nurses, doctors, and researchers involved in treatment and care processes.

When examined at the network level, however, this connected ecosystem reveals information about the devices being used, how they're being interacted with, and how they're processing data. This type of information is not only important for IT, data protection, and compliance personnel, but for HTM teams too.

Network analytics exists one level up and one step removed from the point of care. They represent a form of meta data that informs not on individual care predicaments, but on organization efficiencies and operational imperatives. This information can be leveraged to streamline processes, remove bottlenecks / pinpoint asynchronicities, identify automation opportunities, improve resource allocation / procurement strategies, and provide organization-wide visibility into critical assets.

It's this type of network analytics that offer the most utility for biomedical engineers.

How Do Network Analytics Help Biomedical Engineers?

medical-device-inventoryingThe starting point for most HTM tasks is medical device inventory management. Without a clear accounting of what you have in your possession and where, you can hardly be expected to keep those possessions in fine working order.

In most modern hospitals, good inventory management is a lot easier said than done.

When hospitals have hundreds or thousands of connected devices with a lifespan of 10 or more years, keeping track of every device that comes and goes and where it exists in its lifecycle is a very tall task.

Add in the fact that the failure of a medical device can result in patient injury or death and one begins to appreciate how the tall task is only exceeded by the extraordinary stakes.

Leveraging intelligence from network-based cyber solutions, you can spot all the devices communicating in your network and build out a live inventory of your connected devices. Pulling from network analytics, you can enrich that inventory with information on each device's hardware, software, and configurations.

The network analytics unleashed by smart cyber tooling can even help biomedical engineers locate the physical location of a device reducing the need for surplus equipment stocks and improved time-to-response rates. 

Network analytics can also help biomedical engineers to understand which of their medical devices require patching or updating and with what urgency. It can also help to quantify which assets are being used most and which are being used least.

In this way, when network analytics are put in the hands of biomedical engineers, it not only ensures smarter procurement decisions, but can support a prescriptive maintenance model based on the assets' actual use and historical performance.

Of course, cybersecurity is increasingly considered a shared responsibility across the whole organization and encouraging the biomedical engineering team's use of network analytics not only helps build cyber awareness and fluency, but it empowers them to more directly communicate and collaborate with IT and information security teams.

For example, whereas IT professionals cannot be expected to understand the operational purposes and nuances distinguishing each and every connected medical device, that is precisely the sort of expertise that biomedical engineers already possess. Expertise that is vital to understanding normal/intended network workflows for each device and type; an understanding that is a prerequisite to intelligently restricting unnecessary network communications and reducing the attack surface.

Here's the Bottom Line

Biomedical teams are fighting increasingly difficult battles while shouldering greater responsibilities and facing more sophisticated (often structural) challenges. The only way to come out ahead is to let data guide you, as you act swiftly and with conviction. More often than not, that necessitates a close working relationship between biomedical engineers, IT, and information security professionals. It's critical these departments work together, utilizing network analytics as a shared frame of reference and basis for operational improvement. 

The potential to harness previously untapped synergies by sharing not only technology systems and insights, but a spirit of collaboration and shared responsibility is huge. To achieve peak efficiency and contribute to safer, higher quality outcomes for patients, this cultural shift must be nurtured. 

Great strides have been made in terms of insight availability, especially as more sophisticated cyber intelligence tools have made their way into hospitals. Today, the key challenges are mostly organizational. Establishing a culture of data-driven processes, open communication, and technology-enabled interdepartmental collaboration not only creates a more secure environment, but a more efficient one as well.


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