Using artificial intelligence to improve patient outcomes and streamline processes

Global investment in artificial intelligence (AI) has more than doubled over the last two years — to a staggering $77.5 billion. In healthcare, AI is a multi-beneficial technology used in diagnosis, treatment, and both analyzing and reviewing medical images. In fact, AI can read imaging scans 150 times faster than a human. 

AI offers many benefits for healthcare practitioners.  Predictive analytic AI platforms, for example, can use patient DNA as well as radiology and pathology data to develop analytical patient wellness programs, However, AI platforms or applications are built on high-powered algorithms and massive data sets that require enormous computing power. This dramatically increases the demand for bandwidth. Without a high-capacity network, intensive AI and machine learning (ML) workloads lack the necessary processing power to run efficiently, ultimately creating network bottlenecks and reduced response times — negating the potential of emerging technology in healthcare.

In addition to being highly data-intensive, AI and ML often require real-time transit and processing speeds. As a result, health systems and physician practices need reliable and resilient networking bandwidth, which can support big data transmission needs and an increasing number of IoT sensors and devices. 

The combined adoption of AI and IoT technology is having a transformational impact on health care. Practices are using IoT devices to monitor and record patient data such as heart rate, blood pressure and temperature. When this technology and the data it collects is fed into AI models for analysis, it can lessen the labor needed to keep track of a patient’s well-being, reduce the need for follow-up visits and help predict health issues that might arise.

How artificial intelligence is improving patient outcomes

The rapid adoption and infusion of AI across the healthcare continuum is directly transforming patient treatment, improving physician access to vital information and augmenting the overall management of practices.

AI can free up time for physicians, allowing them to provide patients with more personalized care by reducing time spent on transactional tasks. Some practices are leveraging “AI doctors” to offer health advice to patients with common symptoms to better utilize primary care appointments for patients requiring more complex care.

AI-powered predictive analytics can also help forecast the likelihood of certain diseases or disorders. These include disorders that have been traditionally difficult to identify or diagnose, such as rare hereditary and neurodegenerative diseases. It can also predict other clinical issues, before they happen. For example, using AI, the U.S. Department of Veteran Affairs can predict acute kidney injury, a condition that affects one in five hospitalized patients, 48 hours earlier than was previously diagnosed. 

But AI’s main use and value currently is in making backend processes more efficient for practices. Nearly one-third of healthcare costs are associated with administrative tasks. By using AI to automate many of these tasks, leading practices and health systems are helping improve their bottom lines and reduce overall healthcare costs.

Predictive analytics can also be applied throughout a wide variety of front, middle, and back office tasks such as supply chain management, patient demand, streamlining purchases, pre-authorizing insurance, following up on unpaid bills, maintaining records and the consolidation of vendors to save money and reduce waste.

Modern networks with high capacity are essential to supporting AI

Many health systems and practices are increasingly transitioning to hybrid network architectures and distributed operating models across locations, clouds and users to effectively support digital transformation and take advantage of technologies like AI. Practices also need to move to a cloud-first environment to handle the speeds and unpredictable data volumes of AI applications. These types of pivots require agile networks and high-capacity bandwidth, but the payoff in improved patient outcomes, reduced costs and time savings to a practice’s back office is worth it. 

Practices may know they need to make the transition, but they also need a simple way to create a new cloud-first network with secure SD-WAN or transition to a hybrid network architecture that integrates the existing network and lets them migrate at their own pace. A fast, flexible network that can handle large complex AI data applications and provides access to multiple cloud service providers is necessary to support AI-powered technologies. Moreover, customized fully managed or co-managed services can help simplify operations, with a cloud-based portal that allows for real-time insights and administration of the network, including traffic flow and bandwidth capacity.

AI innovation is central to creating a future where healthcare is personalized, affordable and focused on preventative care. A network that can support AI applications is essential to bringing this future to light. The right technology partner can transform your operations and help you take advantage of today – and tomorrow’s – healthcare innovations.

Learn more about how Spectrum Enterprise can help your practice deploy a modern, high-capacity network.