7.5M patients waiting for NHS elective care - the untapped potential of technology.

In the late 1990s and early 2000s, elective care waiting lists were high and growing. The Labour government at the time, successfully reversed the tide, bringing the waiting list down to acceptably levels over a period of 7 years.

One of the Government’s measures was the establishment of the Modernisation Agency, tasked with training a dedicated task force in service improvement techniques, of which I was a part. A primary objective was to enhance elective care capacity, focusing on improving operating theatre utilisation through minimising cancelled operations, avoiding late starts, and reducing delays between cases. Interventions included enhancing communication with patients, ensuring patients' clinical fitness for surgery, establishing preoperative assessment clinics - now commonplace in the NHS, unblocking flows within the system, aligning demand with capacity, and improving the allocation and booking of theatre lists.

Fast forward 20 plus years, and while we have advanced in technology, little has changed regarding the issues. The waiting list is now higher than at any point since the NHS’s inception. There remains a mismatch between system demand and its capacity in terms of funding, workforce, processes and flow, to cope with it. Contrary to some beliefs attributing this to the Pandemic aftermath or the recent strikes, it’s worth noting that the elective care target of 18 weeks from referral to elective treatment hasn't been met since 2016.

Theatre utilisation remains suboptimal. NHS Improvement reports that an additional 300,000 operations could be conducted each year if theatres were utilised optimally, highlighting enduring inefficiencies. The need for more efficient use of surgical capacity has never been more urgent.

Safety considerations are another important factor in surgery, which remains one of healthcare’s highest risk areas. Adverse surgical incidents affect an estimated 15% of patients undergoing procedures, with postoperative complications contributing to around 1 million deaths globally each year. Each patient with a post-surgical complication not only faces a slower recovery but also adds to the burden on health resources, increasing costs.

These challenges are experienced against a backdrop of worsening financial performance across many NHS Trusts, with deficits doubling in the last year. Addressing these issues demands a different approach than the one used 20 years ago, to tackle systemic bloackages and inefficiencies once and for all.

Enter the digital revolution…

The proliferation of digital health technologies over the past 20 years has been staggering. We now have technology capable of disrupting traditional healthcare delivery models and addressing some of its most pressing issues. Technologies such as artificial intelligence, digital biomarkers, predictive analytics, and whole-genome sequencing present opportunities for heightened visibility of patient risk, and improving outcomes, while automating and streamlining processes. Artificial intelligence (AI) in particular, offers unprecedented opportunities to leverage vast data to enable health providers to make more informed decisions and deliver increasingly predictive care and more personalised treatments.

Alongside the growth in health technologies, there's been an exponential increase in global health data availability, which now represents over 30% of the available global data pool, its growth outpacing even industries like media, manufacturing, and finance. Despite this, a significant portion of healthcare data remains underutilised, with only 60% used to inform decisions, something we can change, with the right approach.

?AI-driven diagnostic tools can significantly improve diagnostic speed and accuracy by analysing complex medical data to provide far more precise diagnoses. Glass Health for example, is a sophisticated AI model trained on the breadth of available medical research, and suggests a patient diagnoses based on a set of patient symptoms and will then draft a comprehensive and fully referenced clinical management plan.

Imagine the scenario where an AI tool scans a patient's health records, wearable data, genetic information, and imaging results to accurately predict a patient’s risk of surgical complications and anticipated recovery times, suggesting tailored optimisation plans for patients and their care teams based on identified risk factors. Tools like MySurgeryRisk are doing just that. The technology has been proven to accurately predict postoperative complications, sometimes more accurately than the patient’s clinical team.

We can use AI to improve procedure scheduling to significantly improving theatre utilisation, minimise disruptions and downtime while enabling hospitals to more accurately allocate resources and reduce surgeon burnout and stress. Such systems analyse multiple health datasets to more accurately predict surgical procedure durations with far more precision than the current ‘average patient’ approach used today. Getinge are helping hospitals achieve this through their Torin system?which delivers AI-led scheduling and backlog management, more accurately predicting surgical procedure durations and automating resource and staff allocation to achieve optimised theatre capacity and improved overall efficiency and patient care.

Intraoperative guidance systems have huge potential to optimise surgical procedures while enhancing patient safety, leveraging data from surgical automation technology, imaging and surgical devices to provide real-time feedback to surgeons during a procedure, enhancing surgical precision and importantly, reducing surgical risk and rates of complications. ?Stryker’s Ortho Q system uses optical technology to provide guidance, visualisation and data analytics to support surgeons with instrument alignment and orientation during orthopaedic procedures, to improve the surgical experience and reduce the invasiveness of procedures.

Despite the immense potential of technology to solve some of the NHS’s most pressing challenges, the NHS lags behind in adopting transformative technologies, facing significant challenges. Digital maturity within the NHS is behind the curve, with well-documented gaps in basic digital competencies across all staff types, from frontline clinicians to managerial and support staff, which not only hinders new technology adoption but also fosters a culture of resistance to change, hampering efforts to drive technological innovation in service delivery.

There is a lack of understanding the value of emerging, sophisticated digital solutions like AI, which is essential to lead and drive digital transformation efforts, and understandable challenges in prioritising digital investments and allocating resources amidst so many competing demands and priorities requiring investment.

Further hampering technology adoption is to enduring widespread use of outdated legacy systems, lacking modern architecture and interoperability, which poses significant hurdles to seamless integration with newer technologies, exacerbating the complexity of deployments and slowing progress. These are significant structural challenges which will take time and effort to rectify.

Regardless of the election outcome, the next government will face the monumental challenge of improving access to elective care to meet current targets and reduce the waiting list. NHS England is currently contemplating proposals to halve the elective waiting list to under 4 million over the next five years, required to achieve treating 92% of patients within 18 weeks. Labour has similarly committed to a substantial reduction if elected, promising additional funding and the introduction of evening and weekend appointments to bring down the waiting list within a single parliamentary term. Achieving this ambitious reduction will demand a multifaceted approach, including targeted measures to increase capacity, streamline outpatient services to reduce unnecessary demand, and finally resolve the workforce strikes.

However it is recognised that technology has the potential to support this ambition. While the recent announcement of ￡3.4 billion capital investment fund to expand technology will go some way to support, ￡2 billion of which has been earmarked to replace legacy systems. However it will also require collaborative efforts from across Government and the wider health system to improve NHS staff digital literacy, design, deliver and expand digital services and importantly shape a culture that is conducive to technological innovation within the NHS. All which is needed if we are to accelerate the delivery of the benefits that technology can bring to the transformation of healthcare delivery.

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