Network Security

Improving the efficiency of clinical operations through Network Analytics

Improving the efficiency of clinical operations through Network Analytics
Written by ga_dahmani
Improving the efficiency of clinical operations through Network Analytics

In any organization, and to a large degree in any industry, most network analytics are IT-driven. Health care providers are no exception. The insights extracted through network analysis offer serious opportunities for operational improvements and added value across all departments. There are also significant secondary benefits of a more cyber-aware and “connected” organizational posture, including better information sharing, collaboration, and security.

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Today, the challenges with smart grid analytics have less to do with the availability of actionable insights and more to do with data “silos” and respective cultural barriers to interdepartmental communication. As more connectivity is built into the digital arena of healthcare IT, IoT, OT, and clinical assets (IoMT), cross-departmental communication becomes increasingly essential to promoting a secure organizational culture. In a hospital setting, perhaps the most acutely felt benefit of taking network analytics beyond the halls of the IT department is the added value (and removed friction) of healthcare technology management workflows. (HTM).

Biomedical engineering in a clinical context

Biomedical or clinical engineers and technicians have a long list of important responsibilities. These include managing an organization’s fleet of connected devices, from procurement, tracking and inventory management to quality control, hardware maintenance and performance management to patching and upgrading. software. Biomedical engineers are also responsible for the decommissioning of devices that have reached the end of their life cycle, including their safe disposal. Ultimately, they are responsible for ensuring that the organization remains compliant with all regulatory considerations related to connected devices and data privacy.

To put into perspective just how big of a job the biomedical department faces, modern medical facilities average 15 to 20 connected medical devices at each bedside. In 2022 there is a total of 920,531 staffed hospital beds in the US that means there are between 13 and 19 million connected medical devices in use. The trend towards remote patient monitoring (RPM) alone will likely produce 70 million devices by 2025.

Because biomedical engineers are the de facto owners of medical devices, that increased connectivity will mean more to manage.

The need for network analysis in the modern hospital

Illustration: Hospital teams protecting the digital terrain

Analytics and automation have been driving industrial productivity for decades. During that time, the health industry has been the exception. There are many reasons why healthcare has been a digital laggard. None of them stem from lack of applicability. In fact, there are many areas in health care that are at the forefront of smart technology applications. Whether it’s for more accurate diagnoses, personalized treatments, patient comfort, or the many examples of telehealth and telemedicine, a lot of technological innovation is happening in healthcare.

While some of these technologies process information at the edge, most are based on more traditional network computing models that transmit data between endpoints and servers. There are also multiple nodes and workflows that interact along the way to intelligently process, share, and action information. When we inject data analytics into this connected ecosystem at the device or endpoint level, we can see what information is captured from the field or from the patient and how it is used. This type of information is usually reserved for nurses, physicians, and researchers involved in the treatment and care process.

However, at the network level, this connected ecosystem reveals information about the devices being used, how they interact with them, and how they process data. This type of information is not only important for IT, data protection and compliance staff, but also for HTM teams. Network analysis exists one level above and one step beyond the point of care. It represents a form of metadata that reports not on individual care issues, but on organizational efficiencies and operational imperatives. This information can be leveraged to streamline processes, eliminate bottlenecks, identify asynchrony, identify automation opportunities, improve resource allocation, or provide organization-wide visibility into critical assets.

It is this type of network analysis that offers the greatest utility to biomedical engineers.

How do network analytics help biomedical engineers?

The 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 them in good working order.

In most hospitals, this is much easier said than done. When hospitals have hundreds or thousands of connected devices with a lifespan of 10 years or more, keeping track of each device that comes and goes and where it is in its lifecycle is challenging. Added to this is the criticality of medical devices, where failure means injury or death to the patient.

Leveraging the intelligence of network-based cyber solutions, you can discover every device communicating on your network and create a live inventory of your connected devices. From the analysis of the network, you can enrich that inventory with information about the hardware, software and configurations of each device. Network analytics triggered by intelligent cyber tools can even help biomedical engineers pinpoint the physical location of a device, reducing the need for surplus equipment stock and improving response time rates.

Network analytics can also help biomedical engineers understand which of their medical devices require patches or updates, and how urgently. It can help quantify which assets are used the most and least. In this way, when network analytics are placed in the hands of biomedical engineers, they not only ensure smarter procurement decisions, but also support a prescriptive maintenance model based on actual usage and historical asset performance. Additionally, cybersecurity is increasingly seen as a shared responsibility across the organization and encouraging the use of network analytics by the biomedical engineering team not only helps build cyber awareness and fluency, but also allows them to communicate and collaborate directly. with security and IT teams.

Here is the end result

Biomedical teams face increasingly difficult challenges medical device security challenges while taking on greater responsibilities and facing more sophisticated (often structural) obstacles. They need data to act quickly and with conviction. Most of the time, that requires a close working relationship with IT and information security professionals. It is critical that these departments work together, using network analysis as a shared frame of reference and basis for operational improvement.

The potential to tap into previously untapped synergies by sharing not only technology systems and knowledge, but also a spirit of collaboration and shared responsibility is enormous. To achieve maximum efficiency and contribute to safer, higher-quality outcomes for patients, this cultural change must be encouraged. Great strides have been made through the availability of insights from cyber intelligence tools. Today, the key challenges are primarily organizational. Establishing a culture of data-driven processes, open communication, and technology-enabled cross-departmental collaboration not only creates a safer environment, but also a more efficient one.
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The charge Improving the efficiency of clinical operations through Network Analytics first appeared in Forest ranger.

*** This is a syndicated Security Bloggers Network blog from Forest ranger written by Tamer Baker. Read the original post at: https://www.forescout.com/blog/improving-the-efficiency-of-clinical-operations-through-network-analysis/

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