We all know healthcare systems around the world are under severe strain. Demand is rising faster than capacity can match it, resulting in overwhelmed hospitals, staff burnout and prolonged delays. In 2022, around 21.8% of deaths in England and Wales were classified as avoidable, indicating that timely interventions could have averted them.
Meanwhile, approximately 13% of acute beds in the UK are occupied by patients medically fit for discharge, accounting for over 2.4m annual bed days and costing hundreds of millions of pounds. These bottlenecks prolong waits, worsen outcomes and escalate costs. The traditional reactive model, treating illness after it emerges, is no longer viable; a shift toward proactive, preventive care is essential to put the health service on a sustainable footing. This is especially relevant to major issues affecting the NHS, like hospital-acquired infections (HAIs), which can be mitigated through early intervention.
HAIs: A preventable crisis
Despite being largely preventable, HAIs continue to pose a significant and costly crisis for healthcare systems, affecting 8% of inpatients in England on any given day. That's approximately 650,000 cases, 22,800 deaths, over £2bn in direct hospital costs and contributing to a quarter of all sepsis cases and prolonged stays. The paradox is that 25–40% of HAIs are preventable with better infection control and earlier detection.
Yet, staffing shortages and fragmented systems mean many infections go unnoticed until they become severe, when they could have been avoided through proactive intervention. Early risk detection tools are needed to intercept infections before they escalate. However, part of delivering this successfully involves harnessing patient and medical data effectively to inform and shape effective clinical decision-making.
Untapped value of data for the system, clinicians and patients
Hospitals generate vast quantities of data, from structured lab results and vital signs to unstructured clinical notes, images, and device readings. Yet 80% remains unstructured and under-utilised, meaning early warning signals stay hidden within ‘messy' data. This makes it incredibly difficult for clinicians to join the dots and integrate these signals across modalities, however, modern AI excels at finding complex patterns across diverse data streams.
The challenge is to translate raw data into actionable insights and embed them seamlessly into clinicians' workflows. To date, most AI models have languished in research or as standalone apps that clinicians cannot easily adopt, leaving the promise of data-driven prevention unrealised, but advancements in technology are beginning to change this.
Supporting clinical decision making: the predictive power of AI
AI is bridging this gap. Specifically, AI that is built and certified for this purpose can now predict HAIs up to seven days in advance. AI clinical decision support making tools can use multimodal EPR data, including vitals, labs and notes to generate an explainable risk score for clinicians. This score helps identify early signs of infection and predicts the likely timeframe of onset. Co-designed with frontline teams, MEMORI offers:
● EPR integration: alerts and dashboards appear directly within the existing EPR interface, eliminating the need for a separate login
● Explainability: visual breakdowns illustrate the key factors driving each risk prediction, fostering clinician trust
● Information synthesis: a consolidated dashboard provides vital trends, relevant patient history, and summaries of clinical notes.
This timely intervention offers clear advantages by improving patient outcomes.
A new vision for the NHS's future
The NHS's 10-Year Plan prioritises harnessing technology, prevention and moving care into the community. A new neighbourhood health service will see a focus on strengthening primary care and a push towards community teams detecting deterioration before hospitalisation. AI clinical decision support systems (CDSS) can and should extend into general practice and home care: algorithms could analyse GP records, wearable data and remote consultation feeds to forecast exacerbations in the community. It's proactive insights like these that would mean neighbourhood teams can easily identify high-risk patients and provide the right support before crises occur.
An NHS where every clinician is supported by their own AI assistant is a compelling reality, with CDSSs acting to reduce information overload and automating routine tasks through AI-powered documentation could all add up to alleviation of burnout and boosted workforce resilience.
Turning the corner
We stand at a crucial turning point: merging big data, AI and digital health to develop a personalised, proactive and efficient care model. Instead of adding to pressures on hospitals, many emergencies might be prevented through early detection, whether stopping infections, averting cardiac events, or controlling chronic disease flare-ups. The NHS's 10-Year Plan offers the strategic blueprint that AI CDSS platforms are putting into practice.
By leveraging the wealth of clinical data and the power of explainable AI, we can shift from reactive to preventive healthcare, saving lives, optimising resources and empowering clinicians with AI assistants to deliver the right care at the right time. The tipping point is here; embracing AI-driven decision support could mark a turning point toward a healthier, more resilient NHS.
