Artificial intelligence (AI) is playing an increasingly important role in transforming healthcare, especially within the National Health Service (NHS). By harnessing the power of AI technology, the NHS is working to improve patient care, reduce operational costs, and better meet key performance targets. This post explores real-world case studies, to showcase the tangible benefits of AI in NHS settings.
AI has the potential to revolutionise the healthcare industry by improving patient outcomes, reducing costs, and enhancing the overall quality of care. The integration of AI in healthcare has been increasing in recent years, with applications in areas such as disease diagnosis, clinical decision support, and patient engagement.
AI has the potential to improve patient outcomes, reduce healthcare costs, and enhance the overall quality of care. There are, however, challenges associated with the implementation of AI in healthcare, including data quality, bias, and the need for human oversight and review in many clinical scenarios.
The National Health Service (NHS) is facing significant challenges in delivering high-quality care to its patients. The use of AI in healthcare has the potential to address these challenges and improve patient outcomes.
AI can help the NHS to improve its efficiency and effectiveness by automating routine tasks and providing decision support. AI can also help the NHS to improve its patient experience by providing personalised care and improving communication.
AI is a broad term that encompasses a range of technologies, including machine learning, natural language processing, and computer vision. AI can be used to automate repetitive tasks, improve decision-making, and enhance patient outcomes.
AI is already used in various areas of healthcare, including clinical decision support, patient engagement, and population health management.
Machine learning is a subset of AI that enables machines to automatically learn and improve from experience without explicit programming. Machine learning applications include predicting disease progression, analysing medical images, and optimising clinical workflows.
AI uses maths, logic, and patterns learned from data to simulate human reasoning and make decisions and recommendations. In this context, data refers to any information that can be processed or analysed to gain insights. Data can take the form of numbers and statistics, text, symbols, or multimedia such as images, videos, sounds, and maps.
AI holds significant potential for tackling pressing challenges facing the NHS healthcare system, such as staff shortages, long wait times, and barriers to patient accessibility. Aligned with the NHS’s digital transformation goals, AI can drive efficiency improvements and enhance overall patient outcomes. From automating administrative tasks to supporting clinical decision-making, the applications of AI in healthcare are wide-ranging.
As with any investment made by the NHS, there needs to be clear cost benefits and health outcomes to justify diverting funding from other areas that are already underfunded.
With this in mind, the first case study here is one that has brought both immediate improvements to patient experience and cost savings within GP surgeries.
A primary care surgery in the South East with 14,500 patients struggled with an overwhelmed reception team of 8 staff, highlighting the need for healthcare providers to find efficient solutions. Patients faced long call wait times (over 36 minutes on average) and a high call abandonment rate of 24% - significantly adrift from NHS care accessibility targets.
QuantumLoopAi’s AI system for automated call handling was integrated to address these challenges. The AI-powered solution answers all calls within 3 rings, captures patient details, and fills in Accurx forms from this call data. This seamlessly integrates with the surgery’s existing systems, and even automates the follow-up of dropped calls.
The outcomes have been transformative - 100% of calls are now answered within 3 rings, with a reduction of 220 calls per day for this surgery. This has resulted in the equivalent of 15 work days being saved each week. 82% of calls are now handled autonomously, with the remaining 18% being transferred through to the appropriate member of staff where a human touch is required.
This hasn't just been a success from an efficiency perspective; more than 90% of patients felt the service had improved as a result of the changes.
AI-driven image analysis technologies are enabling faster diagnostics and increased accuracy in radiology by analysing vast amounts of clinical data. By automating repetitive medical imaging tasks, these solutions have reduced radiologist workloads and improved patient outcomes.
The NHS AI Diagnostic Fund was set up with £21 million allocated to accelerate AI deployment in imaging across 64 NHS trusts. This initiative aims to support clinicians, especially in diagnosing critical conditions like lung cancer, using chest X-rays, which are the most frequently used tool in the NHS for this purpose.
Annalise.ai‘s AI-powered tool has been introduced across six imaging networks in England, covering around 2.8 million chest X-rays per year, or about 35% of all chest X-rays performed in the UK. Results show that Annalise.ai improves diagnostic accuracy by 45% and diagnostic efficiency by 12%. Its deployment reduced the average lung cancer treatment start time by nine days and increased the rate of early-stage cancer detection by 27%.
By prioritising cases based on urgency, the tool supports faster triage, reducing delays and helping NHS trusts meet performance targets. This deployment exemplifies how AI can enhance both patient safety and clinician workflow across high-volume imaging settings
AI-based applications like Babylon Health and GP at Hand are revolutionising patient triage and risk assessment by analysing patient data. These systems provide faster initial evaluation, reducing pressure on emergency services and enhancing overall patient satisfaction.
Triage and risk assessment tools like Babylon Health’s AI systems and similar implementations within NHS trusts are designed to streamline patient assessments by analysing symptoms and medical history. These systems assist clinicians in quickly identifying patients who need urgent care.
Using AI to compare CT scans for signs of cancer, George Eliot Hospital saved radiologists time by automating the alignment and assessment of scans. Their AI tool, developed through the Accelerated Capability Environment, can flag suspicious lesions for further review.
Validation showed that the AI successfully detected anomalies in over 50% of tested images, helping to increase diagnostic confidence and reducing the need for additional manual analysis.
Such AI triage tools support faster and more accurate patient assessments, reducing pressure on emergency services. They align with NHS goals by improving patient access to timely care, ultimately enhancing both clinician efficiency and patient satisfaction.
AI-powered systems are being implemented to optimise appointment scheduling and reduce missed appointments (DNAs - Did Not Attends).
Deep Medical's AI software predicts likely missed appointments using algorithms and anonymised data, considering factors like weather, traffic, and job schedules. The system arranges appointments at the most convenient times for patients, such as offering evening and weekend slots to those who can't easily take time off during the day. Intelligent back-up bookings are implemented to ensure no clinical time is lost while maximizing efficiency.
Results from pilot programs have been promising, with Mid and South Essex NHS Foundation Trust seeing a 30% reduction in non-attendances over six months. There is an estimated £27.5 million per year that could be saved by continuing with the program.
AI is being used to prioritise patients on waiting lists based on their risk profiles.
C2-Ai's Patient Tracking List (PTL) triage system uses data from over 200 million records to review individual patient health profiles and estimate risks of complications. This enables surgical teams to take steps to reduce risks or prepare expert treatment if needed
Outcomes from implementing such systems include:
125 bed-days freed up per 1,000 patients on PTL
8% reduction in emergency admissions
100% reduction in avoidable cancellation rates
27% reduction in long-waiters and highest urgency cases
AI and process mining are being used to identify inefficiencies and optimise hospital processes.
University Hospitals Coventry and Warwickshire NHS Trust worked with IBM and Celonis to use AI and process mining to analyse patient journeys and identify areas for improvement.
This led to the development of tools like:
A chatbot for assessing patient pathways and flagging when patients should be discharged
An AI solution for predicting more accurate appointment durations based on patient factors
The case studies highlighted demonstrate AI’s ability to reduce operational burden, enhance staff productivity, and support healthcare professionals in improving patient engagement and care access within the NHS.
Hospitals and clinics that have implemented AI solutions have seen tangible results, including reduced wait times, higher patient satisfaction, and better alignment with key performance targets.
The QuantumLoopAi implementation, for example, led to a 128% increase in patient detail forms submitted and a 41% recovery of previously abandoned calls. AI technologies have also shown significant potential in managing electronic health records, leading to more efficient and accurate patient care.
Broader NHS-wide statistics showcase AI’s potential to drive significant cost savings, reduce clinician burnout, and improve overall patient outcomes.
As AI capabilities continue to advance, the potential for transforming healthcare only grows. In addition, AI will play a crucial role in drug discovery, accelerating the development of new treatments and improving patient outcomes.
Looking ahead, we can expect to see AI systems playing an integral role in personalised treatment plans, real-time analytics, and optimising healthcare workflows. AI will be instrumental in supporting NHS staff retention by automating administrative tasks and alleviating burnout.
As patient monitoring generates a greater variety and depth of patient data, such as genetic data, this will be used to deliver more tailored treatments to patients.
And as medical professionals train large language models (LLMs) to become more accurate, AI leveraging these will become more trusted to support clinical decision making.
Machine learning is already used in the drug discovery and the drug development process, but this is likely to expand further as AI technology becomes an essential tool to work with the complexity and breadth of data from innovations such as quantum computing become available to the NHS.
But it seems likely that the biggest impact of AI in health care, in the short term future, will be supporting overstretched healthcare workers in a variety of administrative tasks that require more flexibility and nuance than is offered by traditional robotic process automation (RPA).
Healthcare professionals look set to broadly benefit from the evolution and increased availability of AI throughout the NHS.
The case studies presented demonstrate the immediate and long-term value that AI can provide to NHS operations. By harnessing the power of artificial intelligence, healthcare organisations can tackle persistent challenges, improve patient care, and better align with key performance goals.
Healthcare workers should be poised to embrace AI as an immediate solution to pressing admin efficiency challenges faced by much of the NHS. By tackling high-volume, repetitive tasks like call handling, with AI technology, this will free up skilled admin staff for more valuable administrative tasks, where a human touch is needed.
Solutions like QuantumLoopAi already offer a proven path forward in the AI-driven transformation of the NHS. Book a demo today to see the impact this could have on your surgery's call handling.