The digitalization of healthcare is becoming more urgent than ever. Hospitals are under pressure to provide high quality patient care, and demand is growing from an ageing population with complex medical needs. As healthcare institutions continue to operate against the backdrop of staff shortages, limited access to diagnostic imaging equipment, and spiral backlogs of appointments, the waiting list for NHS patients is 7.5 million (1), and improvements in efficiency and accuracy have not been important to date.
However, the reality is that healthcare systems often include archaic, non-digit workflow processes that can result in unnecessary delays in efficient operation. But times have changed and we continue to do so. Using a SLR camera from 10 years ago and a smartphone today, comparing the complexity of photos and image quality gives you an idea of what's going on in today's healthcare industry.
Data and digital technologies such as artificial intelligence (AI) and automation are revolutionizing the healthcare sector when it comes to workflow optimization. This has become particularly pronounced in medical imaging and diagnosis. For example, breakthroughs in imaging technology, including advances in image quality and noise reduction, drive patients' more accurate and timely diagnosis and treatment planning, while automated techniques optimize the scanning process.
The right tools, healthcare providers and their diagnostic teams can significantly reduce time-consuming tasks, minimize errors, increase overall operational efficiency, and in doing so, improve patient experience. In addition to increasing patient throughput and reducing the backlog of appointments, this allows more time to concentrate on care, keeping up with evolving medical needs through ongoing training, and promoting better outcomes.
How AI accuracy is improved?
In 2025, the kickstart was launched with the launch of the UK government's AI Opportunity Action Plan (2). This outlined the steps required for effective AI implementation within the public sector. Since then, subsequent announcements, including the formation of the Healthcare Artificial Intelligence Solutions Framework (3), focusing on the application of AI in a variety of specialties, including radiology and diagnostic imaging, continue to shed light on technology and its role in the future of healthcare.
While these announcements highlight the future potential of AI in this field, innovative health technologies powered by AI and natural language processing (NLP) are already beginning to transform the industry by providing new ways to improve the accuracy, speed and efficiency of clinical decision-making. For example, today we are choosing medical imaging systems (such as CT or MRI scanners) with deep learning reconstruction AI algorithms. These technologies allow operators to easily distinguish between true signals and “noise” by effectively cleaning noise and artifacts during reconstruction. Doing so reduces errors by minimizing distortion and enhancing the details. By providing excellent clarity from the initial scan, the need for repeated scans is also minimized, which in addition to saving time and resources for healthcare providers, it also ensures faster and safer scans for patients.
AI is being used in new ways to help navigate labor shortages by managing unpredictable capacity demand, managing backlog management, and replacing repetitive tasks. Future AI can effectively support the role of radiologists with improved support and workflow, providing timely diagnosis while also improving patient care. But it's not the only technology to transform today's clinical workflow…
Building efficiency through automation
The spotlight is on AI due to its predicted role in the future of healthcare, but the impact of automation should not be underestimated. Today, workflow automation solutions can enhance every aspect of medical imaging, from patient positioning to patient scans and reporting.
For example, in diagnostics, using the right system will automate the scanning process more intuitive and seamless, while being more comfortable and safe for the patient. Automated scan planning using anatomical landmark detection (ALD) technology allows you to accurately identify the anatomy required to perform a fully automated scan plan in all routine trials. Solutions like these can reduce time per scan and allow clinicians to focus on patient care. We witnessed this with our own solution that reduces workflow steps by 40%. Not only is it beneficial for patients, this ease of use significantly contributes to increased patient throughput and increased overall scan productivity, leading to flexible allocation of staff to manage worker satisfaction, time savings and demand.
Automation also helps triage initial reviews, prioritize workloads, and flag scans that require urgent attention. This helps speed up treatment decisions while significantly increasing productivity. In the emergency department, AI-assisted diagnostic tools are particularly effective in optimizing treatment outcomes for A&E cases where speed and accuracy are important. Additionally, a tool is available that allows diagnostic brain images to be quickly and automatically classified following a CT scan to detect ischemia and hemorrhagic stroke in just 30 seconds.
Accepting the power of optimization
Workflow optimization through digital technologies such as AI and automation exists as the cornerstone of delivering efficient, patient-centered care within the complex landscapes of modern healthcare. A deep understanding of clinical workflow components, a skilled awareness of the accompanying challenges, and a strategic implementation of the solution is required. Through these efforts, the healthcare industry can benefit from a range of benefits, including increased patient satisfaction, streamline operational efficiency, minimal errors, and significant cost savings.
This becomes even more important as the UK government begins to expand its network of community diagnostic centres (CDCs) and surgical hubs, working on a backlog of current appointments in the UK (4). These centers provide greater diagnostic capabilities that are closer to the community, but labor shortages and equipment remain pressing challenges to address. Digitalising the workflow plays a major role in supporting diagnostic equipment by enabling flexible staff redistributing while allowing existing staff to perform their roles more efficiently and productively. These advancements can also make X-ray and diagnostic careers more attractive, as machines become more intelligent and workloads become more manageable.
However, it is essential to recognize that optimizing clinical workflows is not a static effort, but rather a continuous journey. Embracing ever-evolving trends and innovations while overcoming relevant barriers is paramount to achieving and maintaining optimal workflows, ensuring a healthy future for both healthcare providers and patients.
