• MEMORI analyses multimodal clinical data in real-time to accurately predict the risk of hospital-acquired infections, alerting clinical teams up to seven days before signs of infection.
• The grant will also support enhancements to MEMORI’s existing capabilities by allowing the integration of additional clinical data sources and further optimisation of its machine-learning models to improve accuracy and scalability.
• Hospital-acquired infections account for more than 20% of NHS bed days each year, with up to 55% considered preventable.

A collaboration between the NIHR HealthTech Research Centre (HRC) in Sustainable Innovation and UK healthtech company Sanome has secured an Innovate UK SMART grant of over £300,000 to co-design the evolution of MEMORI, an AI-powered clinical decision support platform for earlier detection of hospital-acquired infections (HAIs)…

Read the full article on Healthcare Newsdesk

The scale of the problem: HAIs and NHS capacity  

Healthcare-associated infections (HAIs) affect over 850,000 patients each year in NHS hospitals annually, contributing to 50,000 deaths annually. These infections are linked to longer hospital stays (typically an additional 8 days [3]) and higher care costs [1] – the impact of which is not insignificant at a time when services are already over-stretched.  

In turn, lengthy stays also increase the risk of additional infections, associated healthcare conditions, such as pressure sores or falls, a reinforcing cycle of clinical decline, as well as antimicrobial resistance (AMR) – one of the biggest health crises worldwide. With growing pressure on NHS bed occupancy which currently stands at 94.8% [4], a remarkable 15-20% of inpatient beds are occupied by HAIs [1], exacerbating the current elective care backlog and estimated to affect around 7 million patients [5]. Evidence shows that more than 50% of these infections could be prevented through better infection control practices [6] – something often deprioritised due to understaffing and overcrowding. 

Despite decades of research, current detection systems fail to identify infection risk early enough to prevent deterioration. With increased pressure on services and AMR soaring, the need for intelligent, predictive systems has never been more urgent. Advances in clinical AI offer a compelling solution: early warning tools that not only detect infection sooner but support proactive clinical decisions before deterioration begins. 

Accelerating the AMR crisis 

AMR is a serious global health issue, shaped by numerous factors including inappropriate use of antimicrobials. The spread of AMR is rapidly accelerating [7], resulting in longer, harder-to-treat illnesses, higher death rates, and significantly increased treatment costs. By 2050, AMR is predicted to cause more deaths than cancer and diabetes combined [8]. There is a strong association between HAIs and AMR, as infections acquired in healthcare settings are more likely to be caused by resistant pathogens. The fight against AMR therefore requires a coordinated, cross-sectoral approach.  Whilst public education is essential to promote awareness about the appropriate use of antimicrobials, healthcare systems, including the NHS, must be equipped with advanced diagnostic tools to effectively fight AMR.  

Missed opportunities: System shortfalls 

The primary tool used for detecting deterioration in patients, including infection-related deterioration, in UK hospitals is the National Early Warning Score v02 (NEWS2) [9]. This rule-based system evaluates six key physiological parameters, including respiratory rate, temperature, and oxygen saturation, to detect acute deterioration. However, NEWS2’s reliance on single-point measurements means it cannot track the trends or changes over time that are crucial for early detection of infections. This limitation can lead to delays in identifying infections, especially in patients who may exhibit atypical vital signs and struggle to communicate their symptoms [10]. While NEWS2 can identify general clinical deterioration, it lacks the precision needed to detect infections specifically. As a result, infections are often detected only after the patient’s condition has already deteriorated, at which point opportunities for early intervention are missed with potentially fatal consequences.  

Catching it early: The clinical and operational benefits of early HAI detection 

Rapid and accurate identification of infections allows clinicians to prescribe the most appropriate treatment sooner, improving patient outcomes and preserving the effectiveness of existing antimicrobials and reducing the rise of antimicrobial resistant pathogens. Early intervention in infection cases has been shown to reduce high-acuity transfers, decrease sepsis rates [11], and lower the incidence of other healthcare-acquired conditions [5]. From a hospital operations perspective, it will help reduce infection-related bed occupancy [6], thereby improving patient flow and contributing towards reducing the elective care backlog. AI-driven patient risk stratification can help overstretched teams prioritise high-risk cases, reducing clinical teams’ cognitive burden and improving overall efficiency.  

Harnessing the latest advancements in AI to improve healthcare-associated infection management  

While using healthcare data to develop intelligent tools presents numerous challenges, recent advances in clinical AI have made it possible to develop increasingly sophisticated Early Warning Systems (EWS) and risk stratification tools capable of identifying infections, sepsis, and other adverse conditions. However, there are numerous traditional challenges with healthcare data that these tools must overcome to benefit clinicians and patients: 

1. Beyond the Numbers: The power of multimodal data in predicting infection Most early models relied on a single data stream, such as vital signs, missing the richer clinical context available from combining physiological measurements, free-text notes, images and lab results. In the context of infection risk prediction, using multimodal data can offer a more comprehensive view than relying solely on vital signs or lab results, which are often more effective at detecting active infections than predicting them early. For example, researchers at Columbia University Irving Medical Center [12] found that nursing behaviour, specifically, the tendency of experienced nurses to write longer, more detailed notes when concerned about a patient, was a strong early indicator of infection risk.  

2. The data dilemma: Unmasking the challenges of real-world data 

Patient data is often unlabelled, inconsistent and noisy. Large, accurately labelled datasets are hard to find and expensive to generate. When it comes to infections, this challenge is particularly pronounced, as infections are rarely recorded in a structured or consistent way. Instead, researchers must rely on indirect indicators, such as antibiotic prescriptions or clinical documentation, to infer infection events. Identifying proxies that are both accurate and unbiased is difficult, and poor choices can lead to misleading labels and reduced model performance. 

3. Location matters: How data variability impacts AI’s reach 

Healthcare data can vary significantly in structure, the way or how often it is recorded across different care settings. Further, demographics, comorbidities and disease prevalence vary by region and care settings, challenging the transferability of models trained in one environment to another. Risk factors for infection can vary widely between younger, urban populations and older, rural ones. Socioeconomic context also plays a key role: models developed using data from wealthier communities may fail to generalise in more deprived areas, where social determinants of health significantly influence infection risk. These discrepancies make it difficult to standardise datasets for training and can degrade model performance upon deployment. 

There have been a number of recent breakthroughs in clinical AI that address these obstacles: 

• Deeper clinical insights with multimodal AI 

Recent progress in pre-training techniques, such as self-supervised learning, and in feature fusion methods is helping address the challenges of working with multimodal data. These approaches allow models to process and combine information from different data streams in a way that preserves the most relevant and complementary signals from each. Rather than treating each data type in isolation, these methods help the model learn a shared representation that captures the full clinical picture, improving its ability to detect subtle patterns.  

• Self-supervised Learning (SSL) and healthcare data:  

This allows models to learn meaningful representations from vast unlabelled datasets, mirroring approaches used in large language models, so they can infer relationships (for example, between antibiotic orders and infection indicators) without requiring manual annotation. 

• Domain adaptation and continuous learning  

This provides the flexibility to acknowledge the changing nature of data in different care settings, different patient populations, and different recording practices etc. Domain adaptation allows models to adjust to these differences when retraining on a new dataset by learning a shared feature space. Simultaneously, continuous learning prevents “catastrophic forgetting” (forgetting everything learned from previous datasets) by replaying samples of older datasets during retraining. 

Overcoming these data-centric hurdles demands a new generation of AI advancements, which recent breakthroughs in clinical AI are now poised to deliver. To ensure that AI tools are actively used by clinical teams, four critical requirements must be met: 

• Seamless integration into hospital systems: To minimise additional cognitive burden on already overstretched staff, AI tools must plug directly into existing electronic patient record (EPR) systems. Embedding alerts, risk scores and decision-support prompts within familiar workflows prevents tool fatigue and supports timely, context-aware interventions. 
• Codesign with clinical teams: AI solutions must be both useful and usable, which means building them hand-in-hand with the end users. This collaboration should not limit itself to user interface design, but it should also touch upon key product considerations such as setting up alert thresholds. For instance, in infection prediction, the risk threshold for flagging a patient as “high risk” will vary by patient group and care setting. In highly vulnerable populations, where missing a single infection could have catastrophic consequences, clinicians may accept more false positives, so no true infections are overlooked. Ultimately, the real value of AI lies not in the algorithm’s score itself, but in the clinical decisions it enables. 
• Trust is driven by explainability: Clinicians must understand why and how a model generates each prediction. Explainable AI not only fosters confidence in the tool but also allows teams to identify and correct model shortcomings. For example, if a particular feature carries disproportionate weight in the model compared to its real-world significance, clinicians can flag this and guide its recalibration. 
• Regulatory approval as Software as a Medical Device: Any AI tool employed in patient care must satisfy the same safety and efficacy standards as traditional medical devices. Demonstrating compliance with regulatory requirements reassures both clinicians and patients that the technology meets rigorous benchmarks for clinical use. 

Reimagining healthcare delivery: AI as a preventative tool 

The NHS, like many healthcare systems around the world, is facing unsustainable pressure, with demand consistently outstripping capacity and care often delivered too late. Healthcare-associated infections are a clear example of this reactive model: when caught early, treatment can be more targeted and less invasive, and timely isolation can prevent hospital-wide outbreaks. Yet in many cases, infections are detected too late, resulting in rushed interventions, broad-spectrum antibiotic use (which increases the risk of antimicrobial resistance), and serious complications for patients, including ICU admissions. 

Today, the widespread adoption of electronic patient records (EPRs) provides access to rich, multimodal datasets that can power the next generation of early warning systems and risk stratification tools. Recent advances in clinical AI, such as self-supervised pre-training, multimodal fusion, domain adaptation, and continuous learning, are beginning to overcome long-standing technical barriers  

However, technology alone is not enough. The most impactful AI solutions will emerge from close partnership with frontline clinicians, ensuring that models reflect real-world workflows and clinical judgement. A rigorous evaluation framework, ideally driven by public and independent bodies such as NICE, will be vital to establish both safety and true clinical benefit, avoiding pitfalls like “alert fatigue” or the Early Warning System paradox [14]. By combining data-driven innovation with practical, clinician-led design and robust assessment, we can begin to move from a reactive to a preventative healthcare model, and, in doing so, improve outcomes for patients across the NHS and beyond. 

• AI tool that spots hospital infections up to 72 hours early, newly backed by former health minister Lord Bethell, Prof Carole Longson and Dr Arrash Yassaee
• Sanome’s MEMORI becomes the first UK-developed AI medical device that predicts and detects infections earlier, to achieve Class IIb Medical Device CE certification and MHRA approval
• The technology delivers real-time alerts to clinical teams with no disruption, helping ease pressure on overburdened hospitals, supporting the new NHS 10-Year Plan’s focus on prevention and early intervention

London, UK | 2025-08-06 – Sanome, a company building Clinical Decision Support tools to detect emerging health risks, has secured Class IIb CE certification and MHRA approval for MEMORI – an AI-enabled warning system for the early prediction/prevention of hospital-acquired infections (HAIs) – at the same time as appointing new strategic advisors, including former Health Minister Lord James Bethell and ex-Executive Director of National Institute for Health and Care Excellence (NICE), Prof Carole Longson.

HAIs are a major threat to patient safety. In the US and Europe, HAIs account for $28-45 billion 1 and €13-24 billion 2 in healthcare spending respectively. In the UK, the picture is also stark, costing the NHS around £2.7 billion, with HAIs responsible for an additional 7 million hospital bed days annually. 3 Now, MEMORI can directly support the ambitions of the NHS’s 10-Year Plan by enabling earlier intervention, reducing complications and cost, and ultimately increasing hospital capacity.

The platform helps clinicians detect and forecast HAIs up to 72 hours earlier than current gold standard tools, enabling faster, more accurate, life-saving decisions and reducing strain on the NHS. Analysing live patient data, including vitals, and clinical notes from the electronic patient record (EPR), it delivers explainable alerts directly into a clinician’s workflow, with no extra logins or disruption, helping teams intervene faster and improve outcomes for patients.

1 PLoS One, 2023 https://pmc.ncbi.nlm.nih.gov/articles/PMC9949640/
2 World Health Organisation https://www.who.int/campaigns/world-hand-hygiene-day/key-facts-and-figures
3 UK Health Security Agency, 2023 https://assets.publishing.service.gov.uk/media/6827325d010c5c28d1c7e728/HCAI-AMU-PPS-2023-report.pdf

Class IIb certification is among the most rigorous for medical software in Europe, covering devices used to support diagnosis or clinical management. MEMORI is the first UK-built AI tool to reach this milestone, signaling its readiness for real-world use across the UK and Europe’s public and private healthcare organisations.

The new additions to Sanome’s strategic advisory board include:

• Former Health Minister and member of the House of Lords, Lord James Bethell
• Deputy Director NHS England of MedTech Innovation Dr Arrash Yassaee
• Former Executive Director of NICE and Association of British Pharmaceutical Industry (ABPI), Prof Carole Longson

Who join leading expert advisors:

• Author of Big Brain Revolution: Artificial Intelligence – Spy or Saviour and AI expert, Dr Michelle Tempest
• Cambridge Angels Chair, Pam Garside
• Investment expert and IQVIA alumni, Dr Cem Baydar

Lord James Bethell said “Like many people, I’ve had friends and relatives hit by unexpected infections in hospital, sometimes with serious implications for their care. I know from personal experience that catching the signs early is critically important, for their care, for the resources needed for treatment and sometimes the difference between life and death.

During my career, I’ve seen firsthand the pressure on our frontline services and how urgently we need smarter, faster tools to support clinical decisions and protect patients. What Sanome has built with MEMORI is more than just a breakthrough – it’s a transformative tool that allows clinicians to act sooner with more targeted interventions, saving lives, and reducing the pressures on an already burdened system.

I’m proud to support a UK-grown solution that combines world-class science with a deep understanding of frontline healthcare. This is exactly the kind of innovation we need to build a more resilient, preventative NHS.”

On achieving the certification and welcoming the new advisors, Benedikt von Thüngen, Sanome Founder and CEO said: “My father died in hospital because early warning signs were missed. We didn’t set out to build just another AI tool, we set out to create something that means no one else has to experience what I did.

MEMORI is about giving clinicians a window into the future, providing them insights and tools to act before it’s too late, and crucially, without creating additional burden. Achieving Class IIb certification is a major milestone in our journey and proves we can do that safely and at scale.

The support of our expert advisory board, who have firsthand experience of what it takes to achieve the radical change needed across healthcare, further evidences Sanome’s role in helping to shape it, for the system and for the people whose lives are touched by it every single day. Together, we’re building the future of proactive, personalised care – one decision at a time.”

Sanome is actively working towards FDA approval and is already collaborating with NHS Trusts and technology providers on a variety of data-sharing initiatives. The company is expected to announce additional UK, Europe and US partnerships and validation results later this year.

1. How do you define a “true human digital twin” in contrast to traditional digital health models or digital patient records, and what makes it fundamentally transformative for healthcare delivery?

A true human digital twin (HDT) comprises three crucial components: access to an individual’s personal health data, a certified AI model that uses this data to predict specific outcomes, and a mechanism to deliver these results to clinical teams for enhanced patient care. This approach builds on traditional healthcare delivery by providing powerful, actionable insights to empower overstretched clinical teams, giving them the relevant patient information, emerging risk predictions, and recommendations for the best actions to take.

The ever-increasing challenges facing healthcare systems today, significant healthcare professional shortages, coupled with an ageing population with more complex ailments, mean that relying on established care models can no longer ensure the high standard of care patients expect and deserve. The transformative aspect of an HDT lies in its ability to support clinical teams to deliver higher quality, person-centered care without drastically altering their existing workflow, serving as Clinical Decision Support.

The HDT approach is distinct from other digital models, as it provides a full 360 overview of the patient and models out the appropriate actions based on the totality of evidence available etc.

2. The concept involves integrating clinical, biological, mental, environmental, and social data into a single digital construct. What are the biggest challenges in harmonising such diverse and complex datasets into a reliable and real-time health representation?

The main challenges we have found are:

• Accessing, linking and standardising patient data: in addition to various levels of data-sharing across healthcare practices, vast amounts of our personal health data reside in siloed databases and are saved in various formats, creating a complex and labour-intensive process to create unified datasets.
• Building AI models that are fit-for-purpose and that can interpret the temporal value of data. For example, models that can assess the comparable relevance of blood test results from two years ago to current measures available, can provide clinically meaningful recommendations..
• Cybersecurity: of course, throughout this process, robust data protection and cybersecurity measures are crucial, with governance structures in place to ensure the safeguarding of patient privacy.

3. From a technical perspective, what role do AI and machine learning play in ensuring that a human digital twin is both predictive and adaptive to an individual’s changing health profile?

Our approach leverages a variety of futuristic techniques, including continuous and adaptive learning, integrated within our platform, to ensure the HDT operates consistently within regulated boundaries. In addition, we have pre-established robust mechanisms to retrain models automatically, maintaining accuracy and compliance.

4. One of the most striking claims is the ability to detect life-threatening illnesses up to 72 hours earlier than conventional diagnostics. Could you explain how this predictive window is established and validated in real-world clinical settings?

Similar to how we know that symptoms like an itchy throat, fatigue, or loss of appetite can appear a couple of days before developing the flu, HDT can identify early trends that indicate a patient’s changing condition by analysing several clinical data points live and in situ, eliminating the risk that these subtle signals will go unnoticed.

In collaboration with clinical teams, we have defined the ideal “window of opportunity” – the optimal time to alert HCPs to potential risks with clinically relevant sensitivity and specificity, thus prompting timely and effective action. Our models are capable of predicting risks up to seven days in advance. Through clinical co-design, we found the optimal alert window to be 12-36 hours, providing sufficient time to administer the right treatment or prevent ward closures by isolating patients promptly and balancing the risk of false positives, avoiding alert fatigue among clinicians.

Our latest feature, soon to be released, allows clinical teams to customise their preferred alert window and sensitivity. This flexibility enables teams to customise the alert frequency based on the individual patient needs; for example, it may be preferable to have earlier, more sensitive alerts for high-risk patients on respiratory wards, while general ward teams may prioritise higher accuracy.

5. Real-time monitoring at such a holistic scale raises questions about interoperability with existing healthcare infrastructure. How do you envision seamless integration of digital twins into current hospital systems, electronic health records, and diagnostic workflows?

We are able to integrate the HDT directly into clinical workflows by partnering with several leading Electronic Patient Record (EPR) providers to securely embed MEMORI and create a seamless experience for clinical teams who can continue using their familiar tools without disruption.

6. What ethical considerations come into play when creating a digital representation of an individual’s entire health profile, particularly regarding data ownership, consent, and patient autonomy?

Gaining consent to share patient data is a highly complex and often difficult process, but at Sanome, we’re acutely aware of the sensitivity surrounding accessing and handling such personal data, and don’t take this responsibility lightly.

Over 82% of patients support the use of their data for clinical research and the development of better tools to improve care, provided that appropriate governance structures are in place.

To ensure data is shared safely and ethically, we have worked closely with several Ethics Committees, Confidentiality Advisory Groups, and patient advocacy groups to establish robust governance and oversight frameworks to ensure that data is shared safely and ethically:

• Data ownership always remains with the individual, while the healthcare provider (in this case, the NHS) acts as the Data Controller.
• We have implemented strict data de-identification protocols to protect individuals.
• We acknowledge and uphold patients’ rights to opt out at any time.

7. Environmental and social determinants of health are often overlooked in medical diagnostics. How does incorporating these dimensions into a human digital twin improve early detection and overall patient outcomes?

Absolutely, these factors play a significant role in patient health. For example, high pollen levels can worsen conditions like asthma or chronic obstructive pulmonary disease (COPD), increasing the likelihood of hospital admissions or the need for additional oxygen treatment. Similarly, elevated temperatures can contribute to blood pressure complications, raising the risk of cardiovascular issues.

Additionally, unfortunately, lower socioeconomic status is often linked to poorer health due to a range of factors such as inadequate housing, underlying health problems, and poor nutrition.

Our HDT model takes these additional factors into consideration, along with the patients’ personal and clinical data (health profile, mediEXPERT TALK www.europeanhhm.com 77 cal records and any wearable data) to provide healthcare professionals with a full picture of the patients’ overall health, as well as accurate insights and actionable recommendations.

8. Predictive models in healthcare are often criticised for a lack of explainability. How does your system ensure transparency so that clinicians can understand, trust, and act upon the insights generated by a human digital twin?

From day one, we have prioritised transparency and clear communication. MEMORI clearly highlights the key data points that explain why the model predicts a certain outcome – we call this the AI explainability. These have been co-designed with clinical teams to ensure alignment with real-world applicability and to enhance readability and interpretability.

Furthermore, all of our AI models undergo rigorous evaluation, auditing, and certification by our Notified Body, and our results are submitted for peer review to ensure suitable external scrutiny and best practice.

9. Beyond early detection, how do you see human digital twins evolving in terms of preventive care, personalised medicine, and long-term disease management?

The future we envision is one where everyone has access to their own HDT that serves as both an entry point and triage tool in healthcare, easing the strains on healthcare services and suggesting personalised recommendations for the best course of action for the individual based on the information gathered, all under the oversight of clinical teams across community care, pharmacies, general practice, and secondary care. This could include personalised recommendations for preventative care and long-term disease management, such as prescription reminders based on real-time and real-world insights into current health conditions and environmental factors.

10. In terms of scalability, what infrastructure and policy frameworks are required to bring human digital twins from proof-of-concept to widespread adoption across diverse healthcare systems worldwide?

There are several key factors to consider. First and foremost, MEMORI is classified as a Software-as-a-Medical Device and is therefore highly regulated by health authorities such as the Medicines and Healthcare products Regulatory Agency (MHRA) in the UK, the Food and Drug Administration (FDA) in the US, and the EU Medical Device Regulation (EU: MDR) in Europe. As of April 2025, MEMORI is a CE-marked Class IIb medical device; the first real-time infection-prediction tool cleared at this level in Europe, while maintaining compliance with section 251 regulations on the use of patient data. These AI advancements are more than futuristic ideas, and we take our responsibility to patients seriously.

As our technology is embedded within existing EPR providers and leveraging their established infrastructure, we can ensure seamless integration and scalability for clinicians and the health system.

In order to bring this advancement to clinicians and patients in the real world, it needs to be sustainable. Our reimbursement model is focused on ensuring our platform provides actual value to clinicians and patients, and we operate on a shared benefit agreement approach.

11. Could you share examples or case studies where human digital twins have already demonstrated significant impact in clinical decision-making, patient outcomes, or healthcare efficiency?

So far, there are no large-scale case studies of complete HDTs in daily clinical practice. However, adjacent systems like multimodal risk stratification platforms, early warning systems, and precision prescribing platforms are some examples where AI is having a significant impact on earlier disease detection, reduced clinical workload, and better decision-making.

12. With constant advancements in genomic medicine, wearable technology, and real-world evidence, how do you see these fields converging with human digital twins to create a more precise and individualised healthcare ecosystem?

These are fantastic developments, and we are extremely excited about the opportunities they present. The key to success will be integrating these advancements into our HDT, enabling seamless support for clinicians’ decision-making at the point of care, directly within the EPR. Ultimately, this will empower better, earlier, and more personalised clinical decisions.

13. Looking ahead, what do you consider the greatest opportunity – and the greatest barrier – in ensuring that true human digital twins become a standard of care rather than a futuristic concept?

We are already delivering on this vision, delivering the future of healthcare today, with several partnerships with the NHS and private hospitals.

The greatest challenges and opportunities go hand-in-hand; initiatives that promote better and easier data sharing, enhance interoperability between EPR vendors, and address the significant cost pressures facing healthcare to deliver improved care more efficiently will provide a more seamless overall experience.

MEMORI has the potential to support 55,000 clinical decisions across millions of unique patients. Our goal is to continue building on the work we have initiated with our pilot programmes; delivering bespoke clinical decision support and providing personalised recommendations to clinicians, easing the burden on healthcare services and empowering the system to shift from a reactive to a proactive model of care.

View the video below or Listen on Spotify:

Watch the video below or Listen on Spotify:

Authors: Benedikt Thungen (Sanome), Dr Andrew Creagh (Sanome, University of Oxford), Dr Michael Bedford (EKHUFT), Dr Daniele Soria (University of Kent), Cedric Odje (DebioPharm), Justin Vibert (Heal Capital), Dr Keith Grimes (Curistica), Dr Cihat Cengiz (Dieter von Holzbrink Ventures), Dr Philip Ashworth (PatientSource)

To view the full paper, enter your email below:

Preview:

In the ever-changing landscape of technology and business, the term ‘Co-pilot’ has evolved from its original meaning in the aviation space, where it described the second in command assisting the pilot and providing situational awareness.

The concept ‘Co-pilot’ has seamlessly integrated into the business world, represented by its recent adoption in various sectors as a symbol of a smart assistant or companion. For example, Microsoft’s introduction of their AI Copilot, designed to enhance user efficiency through features like sentence auto-completion or smart inboxes.

This growth in the term ‘Co-pilot’ is largely fuelled by the recent advancements in AI, particularly in generative AI technologies. The excitement around these ‘AI Co-pilot’ hinges on their potential to significantly boost productivity and efficiency within organisations.

The healthcare sector, facing the growing challenge of escalating patient demand, complexity of care and an increasingly overstretched, cognitively overloaded and burnt-out clinical workforce, is no exception to this trend. Faced with a shortfall of over 14.5 million healthcare workers by 2030 [1] and the ageing baby boomer generation approaching the age where they require substantial healthcare, global healthcare systems are under immense strain. This issue was highlighted during the COVID-19 pandemic.

As a result, there are two ways to address this, (A) make the healthcare system more efficient so that more patients can be treated with the same resources – “supply” or (B) reduce demand through earlier intervention & prevention – “demand”.

To view the full paper, enter your email below: