Robots Reducing NHS Waiting Times: Future Healthcare Insights

Key Things to Know:

• NHS A&E waiting times are rising due to a combination of factors, including economic challenges, funding issues, and increased patient admissions, especially among older patients and those with multiple health conditions.
• Technological solutions like AI and robotics are being explored to improve patient care in emergency rooms, aiming to reduce waiting times and alleviate workload pressures on medical staff.
• Addressing systemic challenges is crucial for the effectiveness of any technological solution, with a focus on enhancing primary care access and community service capacity.
• The integration of AI and robotics in healthcare promises to make healthcare more efficient and accessible, potentially lowering the cost of healthcare and improving patient outcomes.

For those in the UK, it is well understood that NHS A&E waiting times have only continued to increase, thanks to growing economic trouble and issues with funding. One solution to this challenge could very well lie in the use of robots that could help see patients while emergency staff treat those who need immediate attention. What challenges has the NHS faced over the past few years, how could robots help with A&E times, and could automation help reduce the cost of health care?

What challenges has the NHS faced over the past few years?

Anyone who has used the NHS over the past few years will be fully aware of the numerous challenges that it currently faces. Just before the COVID pandemic, the NHS was already showing signs of struggle, with long wait times both in A&E and in regular appointments. Making matters worse, the quality of NHS services would be entirely dependent on the postcode (and still is), meaning that while some may have decent healthcare access, others would not. 

The NHS Confederation has highlighted that A&E waiting times are not solely due to patient overattendance but are influenced by a complex array of factors including increased patient admissions, particularly among older patients and those with multiple health conditions. This complexity underscores the necessity for a multifaceted approach to healthcare improvement, beyond simple capacity expansion. (NHS Confederation)

The Impact of Systemic Challenges on NHS Performance

During the COVID pandemic, NHS services were not only pushed to their absolute limits, but the NHS decided to delay numerous appointments and procedures, creating a massive backlog. Combined with the struggle of retaining staff and a worsening economy, the NHS simply couldn’t handle the sheer volume of patients, and the result has been a health service whose quality of care continues to fail each year.

Fast forward to 2024, and there are little signs of improvement. For example, the number of patients currently on waiting lists stands at around 7.6 million, but it is estimated to be much higher due to the fact that those with ongoing conditions are not often considered in such figures. Additionally, numerous hospitals across the UK are facing structural issues, along with flooding and sewage issues caused by recent rainfall. 

Similar issues plague A&E waiting times, whereby patients can often expect to wait at least 4 to 8 hours to be seen. Such long wait times has even seen an uptake in self-treatment, whereby patients are tending to their own conditions, utilising online medical knowledge. While this can be beneficial for small incidences (such as bruises, cuts, and minor conditions), it carries a massive amount of risk should an incorrect diagnosis be made.

Recent NHS statistics reveal a continuous challenge in meeting A&E waiting time targets, with a notable increase in patient admissions exacerbating the situation. This data not only reflects the growing demand on A&E services but also highlights the critical need for innovative solutions to streamline patient flow and improve care delivery efficiency. (NHS England)

Could AI and robots help A&E waiting times?

Sitting in A&E is far from fun, and being in pain while waiting is no better. The high cost of staff combined with the limited resources at the NHS's disposal all guarantee such long waiting times, and in many cases, those who have waited for long times only get told to go home after being looked over.

But could there be an alternative solution to seeing human doctors? Could robots and AI be used to examine A&E patients, and provide basic medical care and/or diagnosis? According to researchers from the University of York, this might be the solution that the NHS needs, and is now actively investigating its viability.

The project, led by Professor Radu Calinescu and Dr Tunde Ashaolu, aims to utilise AI software and social robotics to enhance patient care in emergency rooms. By employing a 'social robot' to guide patients in collecting their own vital signs, such as body temperature and pulse rate, the project seeks to improve communication effectiveness, reduce patient waiting times, and alleviate workload pressures on medical staff.

Understanding the factors contributing to A&E pressures is crucial for developing effective interventions. The NHS Confederation's analysis suggests that the issue is not merely about patient numbers but also about the complexity of cases and the broader systemic challenges, including primary care access and community service capacity. Addressing these underlying issues is essential for any technological solution to be effective in the long term. (NHS Confederation)

Bridging Technology and Healthcare: A Collaborative Approach

Acknowledging the importance of patient safety, the team will also investigate various social, legal, ethical, empathetic, and cultural factors associated with AI and social robotics. They will aim to address concerns and tensions through close collaboration with clinicians and patients, particularly focusing on areas where AI may not detect human behavioural clues crucial for triage.

To facilitate this research, the team is developing a prototype Diagnostic AI System for Robot-Assisted A&E Triage (DAISY), with Dr. Ashaolu providing clinical expertise. Through simulations in a laboratory test-bed and collaboration with legal experts, the project aims to ensure the ethical and legal integrity of the technology.

The ultimate goal of the research project is to integrate DAISY into real hospital settings, pending acceptance and feedback from patients. Dr. Chiara Picardi emphasises the system's potential for early diagnosis and disease outbreak detection, aiming to improve patient outcomes and population health.

Could automation be the key to cheap healthcare?

While the costs of pharmaceuticals are hard to address, the development of medical AI diagnostic systems could help to drastically lower the entry to quality healthcare. By eliminating the human factor in diagnostics, it becomes extremely cheap to take a series of symptoms from a user (along with medical data and measurements) and produce an accurate diagnosis.

Furthermore, by eliminating the need for GPs, human doctors can go off to become specialists, which further helps to reduce waiting times for specialist care. With specialists being of higher availability, the cost of specialists would fall, thereby making it cheaper to get patients in front to the appropriate doctor for their needs.

The integration of AI and robotics in healthcare, while promising, must be navigated with a clear understanding of the NHS's current challenges and the needs of its patients. Leveraging technology to improve A&E waiting times and overall healthcare efficiency requires a balanced approach that considers patient safety, data privacy, and the human aspects of medical care. The ongoing research and development in this field are steps toward a more efficient, accessible, and equitable healthcare system. (NHS Confederation, NHS England)

Automation tools could also be crucial in ordering appropriate tests, which can aid in early diagnostics and treatment. As treatment is often far cheaper at the beginning of a condition than it is in the end, the NHS could thus reduce the price of treatment for patients, thereby making funds available for other areas, including specialists, medical research, and AI improvements.

But fundamentally, making healthcare cheaper via AI and automation would give access to high quality medical care to the masses, which itself would be a major humanitarian move. Such technologies could be exported overseas, especially to countries that struggle to attain such quality of healthcare, thereby helping to reduce mortality rates globally and provide the next generation a much better standard of living.