Every doctor wants to do right by their patients. But the reality of modern healthcare makes this harder than ever. According to a study published in Transactions of the American Clinical and Climatological Association, medical knowledge is now estimated to double every 73 days. That’s more than five times faster than in 2010. For a general practitioner or a hospital-based physician, that pace is impossible to keep up with. What was best practice last year may be obsolete today.
From the latest in cardiology to breakthrough oncology treatments, clinicians are under pressure to continually update their knowledge. But in practice, there’s no time. The average primary care visit in the US lasts just 18 minutes, yet physicians are expected to address multiple concerns, document everything meticulously and still deliver personalised care.
The result? Burnout. Over 60% of physicians report symptoms of burnout and a significant contributor is cognitive overload, being asked to do too much, with too little time and too many variables.
And that’s not all. Despite best intentions, two patients with similar symptoms may receive vastly different treatments depending on the clinician, department or hospital. This inconsistency contributes to unequal outcomes and a widening gap between what the evidence says should happen and what does happen in practice. A 2022 JAMA article shows that the time it takes for new clinical research to become standard practice can exceed 17 years.
It's not the doctors who are failing, it's the healthcare system that’s under pressure. Fortunately, AI is stepping in to bridge these gaps, offering a solution to help save time and improve decision-making.
Healthcare institutions are now using AI agents, not to replace a physician’s judgment but to improve it. These systems synthesise large volumes of data and provide actionable insights to support clinical decision-making. Whether embedded into electronic health records (EHRs) or deployed via conversational interfaces, their role is to reduce cognitive load and ensure evidence-based care is more accessible.
For instance, Seattle Children's Hospital developed an AI agent called Pathway Assistant to assist clinicians in making timely, evidence-based decisions. Faced with the increasing complexity of medical information and the challenge of providing high-quality care amidst time constraints, Seattle Children's saw the potential for AI to ease these pressures. They integrated AI Agents into their existing clinical workflows to help healthcare providers rapidly access and apply critical information in real time.
The AI Agent provides clinicians with instant access to Clinical Standard Workflows (CSWs), which are comprehensive, evidence-based guidelines covering over 70 common diagnoses. These workflows have long been a trusted tool for Seattle Children's but finding the right information often took valuable time, sometimes up to 15 minutes when done manually. With the AI agent, this time is reduced to mere seconds.
The Pathway Assistant streamlines access to information and synthesises data from various sources, including clinical guidelines, medical literature and patient records. Its conversational interface makes it easy for clinicians to ask questions and receive precise, actionable responses, allowing them to make faster, more informed decisions.
It’s easy to get excited about the front-end interface of an AI agent. But these AI Agents require massive computing power and scalable infrastructure to operate at scale. Let’s break down why:
AI agents need to analyse vast datasets and deliver answers in near real-time. This demands high-performance GPU Clusters for AI such as the NVIDIA HGX H100 and the NVIDIA HGX H200, capable of running LLMs and multimodal AI. Inferencing cannot afford latency, especially when querying large clinical knowledge graphs or patient-specific data.
Latency is not just annoying, it could be dangerous in a hospital setting. Our advanced networking solutions, like NVLink and NVIDIA Quantum-2 InfiniBand, enable low-latency, high-bandwidth data transfers across the system. This ensures clinicians get instant responses, even during peak usage.
AI agents often need access to large amounts of data, such as patient records, imaging files, research publications and real-time monitoring. Our NVIDIA-certified WEKA storage with GPUDirect Storage support ensures that AI agents can retrieve and process this data without bottlenecks.
Hospitals and research centres running these systems 24/7 need efficient data centres. Liquid cooling technologies help manage the intense thermal loads generated by GPUs, improving system stability over time. Hence, our GPU solutions are equipped with liquid cooling to help you scale AI systems efficiently.
AI agents help synthesise vast amounts of medical data, providing actionable insights to support clinicians in making quicker, evidence-based decisions.
By automating time-consuming tasks and providing instant access to critical information, AI agents reduce cognitive load, allowing physicians to focus on patient care.
AI agents can analyse vast datasets and offer timely, relevant recommendations, ensuring more accurate and consistent decisions in clinical settings.
AI agents help combat information overload, clinical variation, and long delays in translating new research into practice, improving overall care quality.