Enhanced, Self-Repairing Networks Aid Healthcare Institutions in Maximizing Efficiency and Bolstering Security
Healthcare disruptions, whether caused by cyberattacks, natural disasters, or IT issues, are unacceptable. Maintaining continuity of care is crucial for organizations, and network downtime can be costly and harmful to both patients and clinicians.
A solution to this problem lies in self-healing networks, which employ AI and machine learning to deliver operational and security improvements, keeping healthcare systems running smoothly.
Network administrators have the critical responsibility of maintaining network uptime. When an outage occurs, troubleshooting can be time-consuming, negatively impacting patient outcomes and experiences. Self-healing networks automate network management, using AI to continuously monitor network performance, analyze traffic patterns, detect potential issues, and take corrective action – either directly or through human administrators – to maintain network stability and prevent downtime.
A self-healing network's capabilities can vary, but it requires networking components like switches, routers, access points, and cables built to support AI and ML-driven monitoring, analysis, automation, and optimization. Generative AI assistants may also serve as an easy interface for network administrators to interact with.
One of the primary benefits of self-healing networks is their ability to automate the delivery of a reliable user experience, especially when dealing with changing network conditions. In emergency situations, physicians and clinicians often struggle to deliver a reliable care delivery system due to the numerous factors affecting Wi-Fi signals. Self-healing networks can address this issue, offering recommendations to improve Wi-Fi settings without compromising performance from passerby traffic.
Self-healing networks can also enhance security by automatically responding to threats, including unauthorized access, DDoS attacks, and malware propagation. However, determining the optimal level of network automation and security responses remains a challenge.
As the AI managing the network has more time to monitor network activity, it gradually develops a baseline understanding of typical traffic and user behaviors. This allows self-healing networks to improve over time at getting ahead of potential operational and security problems. The level of automation network administrators are comfortable with will depend on the organization's culture and the level of trust it is able or willing to grant to the network itself.
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For more on:
- Artificial Intelligence
- AI Use Cases
- Network Monitoring
- Network Management Software
- Network Optimization
- Routers
- Software-defined networking
- Switches
- Wide Area Networks
- Machine Learning
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