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A Research Hospital:implementing research innovation for healthcare improvement

Bringing healthcare futures into the present

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A Research Hospital: implementing research innovation for healthcare improvement

ForewordThe hospitals which form UCLH combine excellent specialist healthcare with ground-breaking biomedical and clinical research. In the next few years we want to take this ambition even further: we want to transform ourselves from a hospital conducting a lot of research into a real research hospital. What does this mean? We want to be a hospital that focuses on gathering new knowledge, creating innovation and improvement, and implementing evidence-driven changes to medicine, healthcare delivery and organisation. Together with our patients, staff and colleagues at surrounding academic institutions (such as UCL, the Alan Turing Institute and others), we want to improve diagnostic and therapeutic management of a wide range of diseases by translating findings from novel biomedical discoveries into better patient care. At the same time, we also want to optimise our operational performance through data-driven innovation at the hospitals. We hope this endeavour will bring a lot of new potential, modernisation, increased effectiveness and safety, and more efficiency to our patients, our staff, our hospital and its partners, and the NHS in the years to come.

Professor Marcel Levi Chief Executive UCLH

“… we want to improve diagnostic and therapeutic management of a wide range of diseases by translating findings from novel biomedical discoveries into better patient care.”

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University College London Hospitals

PrefaceFew would have believed even 20 years ago that we would now be using smart phones to book restaurants, check in for flights, check the weather for the week ahead, do our banking transactions, monitor our health and wellbeing, schedule our lives, amongst many other applications… and of course make phone calls occasionally. It is a fact of life that we can do many complex tasks on a smart phone whilst at the same time many healthcare workers in the NHS are still writing paper notes, charts and request forms, very much as they were at the inception of the NHS 70 years ago. Moreover, we often still can’t see data in the GP records or the results of tests the GP might have ordered the week before. When reviewing patients, the complexity of the tests and data now available to doctors takes much longer than the typical consultation to assimilate, likewise the vast quantities of information being generated by research relevant to the condition being treated is beyond the capability of a human to absorb and process on a continuous basis. Furthermore, for some tests such as biopsy results and the results of imaging there are delays, often of days and sometimes of weeks before they are reviewed and reported. This is not a criticism, it is the consequence that reporting requires physical interpretation by a specialist pathologist or radiologist. We are now on the cusp of a revolution in healthcare driven by the increasing digitisation of information, advanced analytical techniques such as artificial intelligence (AI), complemented by access to supercomputing that will allow huge amounts data to be analysed and interpreted in ways and with the speed that would have seemed in the realms of science fiction not so long ago. It is now possible to visualise an NHS in which clinical data and imaging are immediately analysed in an automated way, using AI, to process and interpret information, to aid clinical decision making by providing instantaneous estimates of probable diagnoses, recommended further tests and treatment options. The key here is that these analyses are assisting the diagnostic process, not replacing the important role of health care professionals in delivering the treatments and care. But they will make the whole process faster, more efficient and more able to use the richness of the totality of the data to provide more personalised and bespoke clinical care, grounded in the best up-to-the minute evidence available.

Another opportunity is to use these new analytical methods to improve the operational efficiency of hospitals and other services in the NHS. Many organisations outside of the NHS are already using advanced analytical methods such as AI, to plan their services, monitor and react to changes in demand for their services, deploy staff and resources in an agile way to match predictable variation in demand, and for scheduling. These requirements are little different to those of the NHS in responding to demand for changes in patient flows to emergency care, to effectively deploy its staff and

resources and manage its scheduling of appointments etc. The NHS already makes use of the enormous amounts of data it collects every day, analysing the data to monitor and report on the effectiveness of its delivery of services. The step change visualised here, is not in the collection of data, we do that already, but in the methods used to analyse and intelligently interpret that data in real time to transform the efficiency of the operation of the hospital as a whole and the care of patients as individuals. This is going to happen across the world and change healthcare for the better, the only question that remains is whether the NHS wants to lead or follow? With outstanding hospitals and staff, partnership with world leading universities and institutes specialising in data science and AI, burgeoning activity in the UK life-sciences industry in AI, the NHS is well placed to lead.

Professor Bryan Williams Chair of Medicine, UCL Director of Research and the NIHR Biomedical Research Centre UCLH

“We are now on the cusp of a revolution in healthcare driven by the increasing digitisation of information, advanced analytical techniques such as artificial intelligence (AI), complemented by access to supercomputing that will allow huge amounts data to be analysed and interpreted in ways and with the speed that would have seemed in the realms of science fiction not so long ago.”

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The NHS at 70 – what’s next?The establishment, evolution and resilience of the NHS has been an extraordinary achievement by any measure. Many innovations within the NHS over the past 70 years, have transformed patient safety and outcomes, both at home and abroad. Much has changed, but for those who work within it, or use it for their healthcare, some aspects of the NHS have not evolved as fast as they might have done and challenges remain. These challenges have many guises; operational efficiency, expedient access, fragmented healthcare, variation in patient outcomes and some would argue, somewhat ironically, a resistance to adopting innovation.

Healthcare and the demands on it are also changing, perhaps faster than at any time since the inception of the NHS. This has been driven by fast-paced developments in biomedical science, genomics, advanced technologies, information technology, advanced data analytical techniques (including artificial intelligence and machine learning), robotics, remote patient monitoring, wearable devices, and public access to web-based health information, all of which increase awareness and expectations.

Patients are also changing, our populations are more diverse and living longer and getting older, in part due to the success of the NHS. Patients now survive diseases that were previously incurable and often acquire multiple co-morbidities as they age. Travel and globalisation means we are exposed to, and may import infectious diseases previously beyond reach, and we are witnessing the emergence of new threats to health such as antimicrobial resistance and dangerous pandemic viruses, as well as recognising the importance of mental as well as physical health, and the increasing impact of ageing, frailty and dementia as new priorities for the NHS and society in general.

Our research efforts are also changing. Whereas biomedical research was previously caricatured as laboratory boffins remote from patient care, biomedicine is increasingly at the leading edge of patient care. The development of new therapies, diagnostics and analytical techniques are no

longer the sole preserve of big pharma and big industry, instead they are increasingly driven by our world-leading universities, small-medium-enterprises (SMEs), engineers, mathematicians and clinicians, working in partnership with a common purpose, to tackle fundamental questions and develop novel solutions to improve the operational efficiency and resilience of our health care system and thereby improve patient safety, patient experience and patient outcomes.

With the unprecedented pace of change outlined above, the NHS after 70 years of consolidation, has a choice to make. Do we use the capacity of our world-leading universities, the energy and spirit of our UK SMEs, the extraordinary talent and dedication of our health care teams, and the opportunities new developments in biomedicine and data analytics provide, to lead the transformation of modern medicine for our patients in the NHS? Or do we wait until the inevitable changes are developed elsewhere and try to retrofit them, in a piecemeal and incremental way and at greater cost? Do we make our hospitals more receptive to potentially disruptive innovation and change like never before, or do we resist until change becomes inevitable and imposed? Do we lead with the ambition and foresight that created the NHS 70 years ago, or do we follow? The answer will determine whether we are a hospital in which the rigour and ethos of research and innovation is baked into its culture – “a research hospital”. A hospital that is sufficiently skilled to deliver potentially life-changing new medicines for the first time ever to its patients. A hospital that is sufficiently bold to question itself, adapt and adopt new ways of doing things. A hospital that is enabled to learn from all of the information and data it generates and is agile and receptive to the change it prompts – “a learning hospital”. The alternative is to simply watch the change from the side-lines, wondering if and when it will ever happen in the NHS. How we respond now, to the inevitable biotechnological, digital, robotic and data revolution, will determine whether the NHS is at the forefront or in the slipstream of innovation for the next 70 years.

The launch of the NHS July 5th 1947 The 70th anniversary of the NHS

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What is a research hospital?All medicine was innovation, once. Constantly questioning our practice, and adapting it in response, is constitutive of medicine itself. Whether they participate in it or not, patients benefit from being treated at a hospital engaged in research, because the organisational culture and philosophy it implies has broad impact on the performance of the hospital as whole. Adopting successful innovations in clinical practice is inevitably facilitated by the knowledge, skills and infrastructure devoted to creating them. It boosts morale and attracts the best talent not merely to excel, but to redefine what excellence should be.

Currently, the concept of a research hospital could be conventionally understood as a hospital that does research, its success measured by three traditional indices:

• The proportion of patients enrolled in research

• The transformation in clinical practice resulting from the research

• The imaginative depth and impact of the innovations it develops

These indices remain important, and we shall seek to excel in all three. But, if the purpose of research is to improve care through responsive change and innovation our definition of research is necessarily broader. We need to reconsider the artificial distinction between research and service, and broaden the concept of what research is really about. Research is not just about new discovery, it is also about understanding and improving clinical service delivery. A research hospital should use all of its information, every single data point collected in the course of clinical care, in order to optimise the system as a whole to improve its performance through a relentless focus on innovation informed by its own data – optimising patient care and operational efficiency to improve the sustainability of the system. Learning from every individual case and every element of data and embedding the resultant knowledge into care delivery through immediate, constantly optimising systems. A “Re - search” hospital is a “learning hospital” – it learns from what it does, providing decision support for clinicians and management by replacing “I think” with “I know”. A research hospital is an organisation in which research and analytics are baked into its culture, integral and fundamental to everything it does do – valued by staff not because staff are told to value it, but because they can see the value in their every-day practice and the benefit it delivers to patients.

Improvement in this context, does not arise merely from enhanced knowledge of disease mechanisms. It requires the use of that knowledge, both in general and local to our specific patients, to transform their care – both clinical care and the efficiency of the operational framework delivering it – within a process of continual optimisation.

Such optimisation should be informed, not by study of a few patients, but by a fully-inclusive performance evaluation of all of the patients we treat; not at irregular, long intervals, but as a continuous process; not through simplistic, reductive models, but by rich, comprehensive models sensitive to the individuality of each patient.

Its focus must be directed solely by consideration of those aspects of the patient care pathway where optimisation will yield the greatest impact on patient outcomes. For example, if the obstacle to best care is logistical, which is often true of intensely time-pressured clinical contexts such as emergency care, then this ought to be the focus of greatest attention.

Conversely, aspects of seemingly purely operational concern, e.g. length-of-stay or re-admission rates, may also be better use to help illuminate disease mechanisms that in many cases will lie outside of our intuitive field of hypothesis-driven exploration, or may draw attention to hitherto neglected aspects of a disease that have a major impact on patient experience. Thus, operational, disease mechanisms, patient experience, safety and outcomes, are not mutually exclusive, they are interdependent.

Most of all, a research hospital worthy of the name does not merely learn, but continually adapts itself in response, qualifying its decisions with analytical models that summarise the best evidence for its chosen actions, either for a clinician treating an individual patient, or the functionality of the hospital as a whole. Furthermore, it is a hospital whose goals are constitutionally shaped by its patients, not merely in surface appearance.

Until a few years ago, such a hospital could only be a dream. But the rapid advance of digitisation coupled with the rise of deep analytical techniques such as AI and machine learning, now make it a tangible reality.

University College Hospital

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Optimisation and personalisationA logical question is why hospitals are not already optimised in the way we have described? The answer is that the rules that govern their operations are insufficiently fine-grained, the totality of the information we collect is not analysed in the ways that are now possible and instead our assessments of performance are described by relatively simple, often coarse, population-level descriptors of the patients we serve. When information about a patient pathway has few parameters to adjust, there is nothing to optimise. This has been true both of research dominated by large-scale studies focused on inferences about the “average” patient not a specific patient, and operational delivery and clinical decision making, that has inevitably been built on the same principles.

We can do better if we focus care on the individual patient, by capturing their characteristics in much richer detail, within “high-dimensional” mathematical models with many more variables, we can dramatically improve our performance in three distinct ways:

• We can use rapid analytical processes to evaluate all of the information we have, complemented by up-to-the-minute analysis of the best evidence to define the individual needs of each patient, enabling state of the art personalisation of care.

• We can improve the operational efficiency of the hospital, deploying resource where it is needed most, and anticipating rather than reacting variation in patient flows and demand and to individual patient needs.

• We can use this information to illuminate disease mechanisms otherwise obscured by great variation from one patient to another.

To achieve this does not require any modification to current clinical pathways, the introduction of novel investigational methods, or the measurement of any new variables: rather, we simply need to better use the information we already have and deploy novel, machine-learning assisted methods within the existing analytic frameworks already operating in every NHS hospital, including our own. But it does require a fundamental shift in research and organisational thinking, one that puts the individual patient at the centre of both routine care and research-driven innovation.

“We can do better if we focus care on the individual patient, by capturing their characteristics in much richer detail.”

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A Research Hospital – our approachOur vision is to introduce innovation throughout the hospital – not merely within areas previously isolated for research – rendering the entire hospital intelligent. This vision has one overriding aim: to prioritise the adoption of innovation that brings the greatest benefit to patients fastest. That naturally divides into three. The first is optimising what we do already with deeper and smarter analysis of the information we already have: i.e. optimising operational delivery; the second is discovering what we should be doing in the future, new ways of improving operational efficiency, patient safety and outcomes – clinical innovation. The third is the more traditional domain of research as we know it, i.e. evaluating these new innovations to make sure that they do deliver the anticipated improvements. To do this, we need to be bold

because it is inevitable that some of the innovations flowing from this process of discovery will be disruptive in that they will challenge popular dogma about the ways things should be done, they will necessitate change in the shape, roles and responsibilities of the healthcare professionals, they may even change the nature and structure of our hospitals.

Our initial focus will be on UCLH objectives defined in response to the NHS five year forward plan. The UK government argues this needs “acceleration of useful health innovation”, with a particular focus on quality: “The definition of quality in health care, enshrined in law, includes three key aspects: patient safety, clinical effectiveness and patient experience. A high-quality health service exhibits all three.”

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Patients in a Research HospitalA key benefit of being a patient in a research hospital is to be better informed by evidence. In fact, the Academy of Medical Sciences [https://acmedsci.ac.uk/file-download/44970096] stated: “patients should be able to access reliable evidence, and this should be presented in an intelligible form that allows them to use it in their own decision-making”. In addition, it has been suggested that patients in research hospitals have better outcomes [www.nihr.ac.uk/news/research-active-trusts-have-better-patient-outcomes-study-shows/2715].

However, the causal field between research engagement and healthcare change is wide, specifically in terms of how the mechanisms associated with better treatment are linked or aligned to best practice guidelines. A few key factors include the provision of care by individual clinicians and the organisation as a whole, an organisation’s ‘absorptive capacity’ to use new knowledge, the level of interest in – and favourable attitude to – research, the likelihood of

introducing innovations, and the integration of research staff and equipment with standard care.

A ‘research hospital’ from a patient and public perspective [http://futurehospital.rcpjournal.org/content/3/2/139.abstract?cited-by=yes&legid=futurehosp;3/2/139&related-urls=yes&legid=futurehosp;3/2/139], involves patients being able to say the following about their hospital:

1. What my hospital does in research is visible to me – is it transparent?

2. I can choose to contribute to research as part of my care

3. My experience is valued as part of the research being done there

4. Research is clearly viewed as a mark of quality by those who work there

5. My contribution is acknowledged and the results of research are made available to me

https://acmedsci.ac.uk/file-download/44970096

https://acmedsci.ac.uk/file-download/44970096

http://www.nihr.ac.uk/news/research-active-trusts-have-better-patient-outcomes-study-shows/2715



http://futurehospital.rcpjournal.org/content/3/2/139.abstract?cited-by=yes&legid=futurehosp;3/2/139&related-urls=yes&legid=futurehosp;3/2/139



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A Research Hospital – implementationOur activities will be structured into three inter-related domains:

1. Translating novel biomedical discoveries into patient care: We are well placed to continue our burgeoning activity in this domain by building on the success of our National Institute for Health Research (NIHR) Biomedical Research Centre (BRC). Our NIHR BRC is founded on a strong partnership between UCLH and UCL – a world leading University. NIHR Biomedical Research Centres conduct translational research to transform scientific breakthroughs into life-saving treatments for patients. The principal focus of the NIHR UCLH BRC is to work with scientists at UCL to continue to pull through local discovery science and accelerate the delivery of novel innovations in diagnostics (genomics, omics, imaging), disease biomarkers to detect diseases earlier, and advanced therapies such as new drugs, devices, cellular, gene and immunotherapies. This ensures that our patients get first access to medical innovation. Our patients are now amongst the first patients in the world to receive the latest treatments for many intractable diseases that can ensure survival when all other treatment options have been exhausted. An added advantage of embedding this research infrastructure in the hospital is that it provides state of the art facilities, infrastructure and skilled staff that are adept at delivering complex leading-edge medicines and other innovations to our patients. Moreover, the international profile, this research hospital model brings, encourages collaboration with medical and biotechnology companies in the UK and internationally to foster home-grown medical innovation and the growth of the life-sciences economy in the UK.

2. Optimising operations: This has two elements; optimising our operational processes and clinical service delivery pathways, including scheduling, patient access and flow through the hospital, monitoring well-being, patient satisfaction and the impact of our innovation on patient outcomes, continuously and at a scale never previously achieved. The second element is optimising clinical decision

support. Using advanced analytical methods to collate and assist in the clinical interpretation of massive amounts of data relevant to a patient’s diagnosis and treatment, from many sources, and providing decision support to clinicians to help deliver optimal, safe and expedient care. This will also involve applying machine learning and AI algorithms to semi-automate processes such as interpreting imaging, genomics, and digital pathology. This uses data that already exists but applies high-dimensional modelling assisted by machine learning to enable care delivery optimised over a wide multiplicity of contributory factors. Importantly, it assists and speeds up these processes but all decisions have “human gating”, this means that everyday process are not automated to the point that clinical interpretation excludes the involvement of a doctor, nurse – they will always be involved at the point of care and the automation provides assistance to enhance and accelerate decision making processes.

3. Clinical trial optimisation: Patients benefit from participation in clinical trials and clinical trials are essential to evaluate the value of new developments in healthcare, whether it be new treatments or new systems of care. Of course, patients have a choice about whether they want to be involved in such trials but we believe that most patients, when provided with the appropriate information and systems that they can trust to safeguard their data, will see the value of this approach to them individually, and society in general. The opportunities provided by advances in genomics and deep analysis of clinical tests, with linkage to patient outcomes will enable the UK to accelerate discovery research for the benefit of patients.

All three initiatives will be underpinned by infrastructure responsive to the many synergies between them, creating a sustainable, secure and replicable framework.

Indeed, we wish to create a blueprint for how hospitals should run, across the NHS, to be reinforced politically. We want to be bold and imaginative, to shape the vision of the ‘hospitals of the future’.

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Patient information, data privacy, security, transparency and trustNHS hospitals continuously use patient information to care for their individual patients and to assess its overall performance in delivering care for patients in general. The NHS nationally uses patient information to generate national health care statistics, evaluate health and disease trends, plan services, identify variations in care and for various other related reasons. This is essential and patients would expect this to happen. However, patients entrust the NHS with very personal information, and this relationship is grounded in trust that the NHS will ensure that this information is secure and only allow access to identifiable personal information for the purposes relevant to their care. In the National Data Guardian for Health and Care Review of Data Security, Consent and Opt-Outs, Dame Fiona Caldecott commented that “everyone who uses health and care services should be able to trust that their personal confidential data is protected. People should be assured that those involved in their care, and in running and improving services, are using such information appropriately and only when absolutely necessary”. Various regulatory frameworks are in place to ensure that data privacy and security are given the highest priority in the NHS, evaluated by CQC inspections, and subjected to harsh sanctions for breaches of personal data security.

There is an important distinction between personal identifiable information and anonymised information. Patients want a choice about how personally identifiable data is used and an opt-out system is being introduced to allow people to opt-out of their personal confidential data being used for purposes beyond their direct care. This

would apply unless there is a mandatory legal requirement or an overriding public interest. However, the majority of uses of patient data beyond direct care do not require the use of personal identifiable data, such as commissioning, regulating, and monitoring operational performance or services, undertaking research, or developing clinical decision support systems. In these circumstances the people undertaking the analyses do not need to know the identity of an individual and anonymised high quality linked person level data will suffice. Effective anonymisation of personal confidential information is now possible to help make use of rich data resources whilst protecting an individual’s privacy. Importantly, the use of appropriately anonymised data is not subject to the patient opt-out.

When a research project is undertaken using identifiable patient information, ethical approval by the Health Research Authority and formal informed patient consent procedures are required.

A major challenge in the new environment of increased digitisation of data is communicating to the public in an accessible way, the huge potential advantages of using the routinely collected healthcare information to provide the substrate for innovation and improve the efficiency of health care delivery and patient outcomes, whilst at the same time never underestimating the importance of data security and transparency of our purpose in maintaining public trust. These objectives will be subject to strong leadership and oversight at the highest level of the organisation.

National Data Guardian for Health and Care Review of Data Security,Consent and Opt-Outs

National Data Guardian

Your Data:

Better Security, Better Choice, Better Care

July 2017

Government response to the National Data Guardian

for Health and Care’s Review of Data Security, Consent

and Opt-Outs and the Care Quality Commission’s

Review ‘Safe Data, Safe Care’

“People should be assured that those involved in their care, and in running and improving services, are using such information appropriately and only when absolutely necessary.”

https://www.google.com/search?client=safari&rls=en&q=National+Data+Guardian+for+Health+and+Care+%7C+Review+of+Data+Security,+Consent+and+Opt-Outs&ie=UTF-8&oe=UTF-8



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Processing information, deep learning, artificial intelligence (AI) and machine learningFew can have escaped the news, some would say hysteria, about the potentially transformative applications of AI and machine learning for so many aspects of our daily lives. Despite scepticism about the value of these approaches in some areas, there is broad consensus that there is huge potential for real benefits in healthcare, some of which are already being realised. The concept of artificial intelligence is not new but the approach has now come to the fore because of the convergence of increased digitisation of large amounts of information, the development of neural networks and advances in supercomputing that now allows vast quantities of information to be processed in a timescale that makes it feasible for use in clinical care.

The House of Lords Select Committee on Artificial Intelligence report “AI in the UK: ready, willing and able? [https://publications.parliament.uk/pa/ld201719/ldselect/ldai/100/100.pdf] said: “The NHS should look to capitalise on AI for the public good, and we outline steps to overcome the barriers and mitigate the risks around widespread use of this technology in medicine.” They added: “Many of the issues presented by AI when deployed in healthcare are representative of wider issues with the use of artificial intelligence, such as the possible benefits to individuals and for the public good, the handling of personal data, public trust, and the need to mitigate potential risks.”

Thinking on its own: AI in the NHS

Eleonora HarwichKate Laycock

#reformhealthJanuary 2018

Ordered to be printed 13 March 2018 and published 16 April 2018

Published by the Authority of the House of Lords

HOUSE OF LORDS

Select Committee on Artificial Intelligence

Report of Session 2017–19

HL Paper 100

AI in the UK:ready, willing and

able?

Life Sciences Industrial Strategy – A report to the Government from the life sciences sector

https://publications.parliament.uk/pa/ld201719/ldselect/ldai/100/100.pdf



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What is artificial intelligence?

AI has been described as the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the development of systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. AI was recently defined in a government industrial strategy white paper as; “Technologies with the ability to perform tasks that would otherwise require human intelligence, such as visual perception, speech recognition, and language translation” [Department for Business, Energy and Industrial Strategy, Industrial Strategy: Building a Britain fit for the future (November 2017), p 37]. The concept of machines being able to mimic human intelligence and the processing power of the human brain has been around for almost as long as the NHS. Indeed, Alan Turing in 1950, in his seminal paper, “Computing Machinery and Intelligence”, conceived the concept of intelligent machines within the context of the rapidly growing field of digital computing. There are many terminologies applied to aspects of AI. AI is based on the development of computer algorithms that follow a series of instructions for performing a calculation or solving a problem. These can mimic the decision-making ability of a human expert by following pre-programmed rules, such as ‘if this occurs, then do that’. The capacity of these systems has been augmented by neural networks a type of machine learning loosely inspired by the structure of the human brain. A neural network is composed of simple processing nodes, or ‘artificial neurons’, which are connected to one another in layers. Deep Learning is a

more recent variation of neural networks, which uses many layers of artificial neurons to solve more difficult problems. It is often used to classify information from images or text. Machine Learning is a form of AI which gives computers the ability to learn from and improve with experience, by being fed with information on the outcome of its actions, without being explicitly programmed. When provided with sufficient data, a machine learning algorithm can learn to make predictions or solve problems. Whilst this may not be easy to comprehend, it is easy to appreciate the many potential applications in healthcare for developing systems to intelligently analyse and interpret complex patient data and investigations, to provide decision support tools for clinical care, assist with scheduling, evaluate operational performance of a hospital, or provide automated analysis of x-rays, CT or MRI scans, or pathology slides to provide fast automated diagnoses.

The development and evaluation of AI to assist with clinical decision-making processes will require processing of large amounts of clinical information. However, it does not depend on the use of private identifiable patient data – the data can be anonymised for this purpose.

AI and machine learning and clinical decision support: The computer receives and analyses complex data from a patient’s emergency hospital admission, compares this with vast quantities of data from prior clinical admissions of similar patients with the same type of illness and identifies patterns consistent with a series of diagnoses, allocating each a different probability of being the real diagnosis.

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This could be used at the bedside to immediately provide the clinician with suggested diagnoses, recommended additional tests and suggested treatment plans. The doctor then makes the decision about how the patient should be treated and the data from that admission and the patient outcome provide new data to refine the clinical decision support algorithm, increasing its precision when the same type of clinical case presents again. This is very similar to the way doctors and other clinical staff have learned from their own experience over many years, but this process is much faster and benefits from the collective experience of all clinical encounters by all staff.

The distinctive difficulty of AI in healthcare is the risk of unpredictable behaviour. Amongst the many ways this risk can be powerfully attenuated is the use of comprehensive testing covering the widest diversity of contingencies – a foundational feature of our approach – and an absolute insistence on human-gating of any clinical decision-making. The machine quantifies evidence; the clinician decides how to act on it. Indeed, such mandatory human-gating is now enshrined in law through the GDPR.

There are already relatively complex tasks where machine learning exceeds humans in their powers of judgment. But such tasks are as yet very narrow, and require a clinician to define them in the first place. Human expertise remains the guide and ultimate arbiter, for it is the benchmark against which AI will always be measured. This fundamentally instrumental role of AI, subservient to our interests, and governed by our objectives ensures we retain responsibility over, and accountability for, our actions, in healthcare and elsewhere. An understandable anxiety of our age is that we may ultimately become dependent on machines undertaking analytical and decision-making processes that lack the flexibility, subtlety and the sensitivity of human beings. There is no intention to replace human experts with unemotional machinery, but rather to enable us to embody our rich, human, intuitive powers of patient-centred decision-making, supported by an AI-enhanced decision-making process. In this way, we see this as augmented intelligence rather than artificial intelligence.

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The new UCLH and Alan Turing Institute partnershipRecognising the enormous potential of AI, in 2017, UCLH began discussions with the Alan Turing Institute about developing a partnership to bring the latest advances in AI into the NHS. The Alan Turing Institute is the national institute for data science and AI of which UCL is one of the founding partner universities [www.turing.ac.uk]. The Alan Turing Institute has recruited some of the sharpest minds in the field of data science and AI, to continue the theoretical development of AI and realise the potential of its application to real world problems. Along with UCLH, the Alan Turing Institute sees the NHS as an important area for application of AI. Our close alignment of purpose and physical proximity of UCLH to the Alan Turing Institute, facilitates this partnership. Our vision is to create teams comprising UCLH health care professionals, clinical academics and managers, with Alan Turing Institute academics, each team focussed different aspects of hospital operations, clinical care and diagnostics, where AI could be applied to transform clinical care, thereafter evaluating these developments and then make them available to the wider NHS. The NIHR UCLH BRC will provide pivotal infrastructure, such as data warehouses and storage, and expertise in data science and advanced analytics through the researchers it supports. This partnership will benefit from and work closely with the Health Data Research-UK [https://hdruk.ac.uk] which is a national research informatics program, involving many of the leading UK universities, with the mission to use cutting edge data science and analytical tools, to harness the potential of medical and biomedical data to tackle some of the most pressing health challenges in the UK.

The NIHR is the most integrated health research system in the world, and the largest national clinical research funder in Europe. The NIHR provides world-class research facilities and supports expert investigators and clinicians through its research infrastructure, such as BRCs.

Better use of information to transform healthcare: Large amounts of data are already collected for individual patients during the course of their healthcare. However, it is often stored in different silos and not connected. For example, this data may involve critical information about how a specific genetic mutation in an individual patient might influence their clinical symptoms, could influence their response to the usual treatment for that condition, and ultimately their clinical outcome. The genetic information may provide an important clue as to how their condition should be best treated, perhaps with a treatment that might not have otherwise have been tried because the link between that person’s genetic mutation and their illness had not been recognised. By better linking these various sources of data, we will be able to get a clearer picture of the underlying causes of disease in general and for an individual patient. However, this requires more than just linkage of the data, it also requires the deep analytical capabilities that AI can provide to make sense of vast amounts of data and published information, so that this learning can be brought to an individual patient and augment the decision making around their care.

http://www.turing.ac.uk

https://hdruk.ac.uk

NIHR University College London Hospitals Biomedical Research Centre

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Our Research Hospital in action Clinical Research at UCLH: UCLH is one of the leading hospitals in the UK with respect to the number of active clinical trials and the number of patients recruited into clinical trial per year. Typically, in UCLH will have over 300 clinical trials active and recruit around 15,000 patients into trials, each year. UCLH also one of the leading UK hospitals (often number one) in terms undertaking and recruiting patients into complex early phase trials, which involve the delivery of novel treatments to humans for the first time in the world, ensuring that our patients get first access to the latest developments in medicine. All of this activity takes place in state-of-the art and dedicated NIHR-supported clinical research facilities on our hospital campus, by our highly trained clinical research staff, with expertise in the treatment of patients from the locality and referred from across the UK, many of whom have serious illnesses that have failed to respond to existing treatments. The new treatments bring hope for these patients. UCLH in partnership with UCL also has teams of staff with expertise

in the ethical, regulatory and safety monitoring aspects of research, to ensure that our clinical trial design and conduct are of the highest standards. Through our NIHR BRC, we tackle some of the most difficult challenges in medicine such as; cancers, neurological diseases, dementia, mental health, cardiovascular diseases, deafness and hearing, oral health, immunological diseases and infectious diseases. We are one of the leading centres in Europe in developing and delivering novel genetically engineered cell-based therapies and gene therapies for cancers and rarer genetic diseases. Our research has had, and will continue to have, major impacts on the development and evaluation of new treatments, many of which have been and will continue to be, adopted as new innovations in healthcare in the NHS and beyond.

A research-enabled electronic health records system (EHRS): UCLH is soon to deploy a new EHRS (EPIC) and our BRC, working with UCLH, has created a clinical research informatics unit to ensure that our EHRS is fully research enabled and compatible with all modern safety and regulatory requirements for information privacy and security. A key objective of this workstream is to ensure EHRS provides enhanced access and information for patients wishing to participate in research and importantly, recording when patients have expressed a wish that they do not wish to be approached about participating in research.

“… ensure EHRS provides enhanced access and information for patients wishing to participate in research and importantly recording when patients have expressed a wish that they do not wish to be approached about participating in research.”

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A Research Hospital in actionMany examples of our ground-breaking research impacting on clinical practice could be highlighted but the example below illustrates how sophisticated some of these new treatments have become and why our entire approach to treating diseases like cancer will have to change if these treatments are as successful as the early results suggest they might be.

Developing and delivering innovative new treatments for cancer: Clinical academics at UCLH and UCL, supported by the NIHR BRC, have developed a new therapy that uses a patient’s own circulating immune cells to treat cancer. A specific subset of the patient’s immune cells (T-cells) are extracted from their blood and then genetically engineered to insert a receptor (a chimeric antigen receptor – CAR) on their surface that will recognise cancer cells and attack and kill them – this is known at (CAR-T cell therapy). This approach mimics the way T cells would normally function to recognise and eliminate infections. Trials are now ongoing at UCLH, using these CAR-T cells to treat blood cancers such as leukaemia and early results globally have been very promising. As exciting as this is, the process requires skilled research teams, facilities to undertake genetic engineering of T-cells, and clinical staff to deliver this treatment as well as specialised facilities to care for the patients.

Using information to transform clinical care in the NHS: The amounts of healthcare information collected during routine clinical care are already enormous and this is expanding rapidly with increasingly sophisticated and deeper analysis of routinely collected clinical tests and imaging. It is set to expand further with the addition of information from advances in genetics, protein analysis, digital monitoring of clinical vital signs, and health and wellbeing information provided remotely from wearable devices, smart phones etc. Add to this process data such

as time of arrival in hospital, who treated the patient, when, where and how? How long the patient waited for their care or their appointment? How long they stayed in hospital and how well they recovered? This is critical data for patient care and monitoring the quality of care, patient safety and patient outcomes, as well as the overall performance of staff and the hospital in general. This data is already analysed but to optimally analyse this data to enable the output to directly influence a patients’ care and the real time performance of the hospital, is beyond the capacity of individuals acting alone or collectively and requires supercomputing and AI. Three obvious benefits of this are shown; (i) to better understand disease and use this information to develop new treatments; (ii) to provide fast clinical decision support to clinical staff; and (iii) to provide a deeper understanding of the factors that influence the performance of the hospitals care pathways and seek to improve them.

“… the process requires skilled research teams, facilities to undertake genetic engineering of T-cells, and clinical staff to deliver this treatment as well as specialised facilities to care for the patients.”


Automated analysis of digital images and digital pathologyImaging, whether conventional X-ray images or CT-scan or MRI, ultimately represents a digital image comprising pixels of varying shades and/or colour. In the same way that a human can be taught to recognise patterns consistent with normal and abnormal findings, so can a machine. At a minimum, automated analysis of images could recognise the boundaries of normality and highlight scans that only have abnormal features. This kind of approach is already becoming reality. Imagine a situation within an instant of imaging being performed, an initial report on any abnormalities is available with the precision and learned expertise in analysis that would be beyond the expertise of most doctors in assessing imaging results without the report of a radiologist. The immediacy of a result could revolutionise the performance of hospitals and in some circumstances could be life-saving. Moreover, machines can undertake these analyses 24/7, without any oversight or dip in performance due to fatigue. At UCLH and UCL, we have recently analysed over 100,000 digital images of MRI scans of the brain which have been used to develop an automated tool to provide detailed reports on MRI brain images for better quantification of the effects of treatment on the brain for diseases such as dementia and in providing high quality brain image reporting for routine clinical care.

Likewise, for pathology tests. Today microscope slides are loaded onto a microscope and viewed by a pathologists to establish a diagnosis of disease in a particular tissue or spot abnormal cancer cells on cervical smears for example. Techniques are being tested and validated so these processes can also be automated and machines used to analyse digital pathology images in a more systematic, consistent and uniform way than humanly possible, producing a result before the pathologist would have been able to load the slide onto the microscope.

Image guided precision surgery and robotics

Bringing together, engineers, high dimensional analytics for image analysis and 3-D reconstructions, based on our work these approaches are being used at UCLH, alongside robotics, to provide very high levels of precision in guiding surgeons as they perform complex surgical procedures on the human brain for patients with brain tumours or refractory epilepsy, or for highly precise complex corrective surgery on the human fetus in utero.

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“… provide very high levels of precision in guiding surgeons as they perform complex surgical procedures on the human brain for patients with brain tumours… ”


Bringing advanced analytical methods into hospital operationsThe industrial sector has long used mathematical analytical approaches to constantly study activity, people movement, pinch points in service delivery to smooth out capacity, align the workforce to the demand and even to design factories and cities. Whether it is an online system handling table bookings for a restaurant, an airline selling tickets for a flight, businesses selling goods online, banking systems etc., all are subject to detailed analysis using AI to provide constant efficiency gains to the system, even adding in data on weather trends and other external factors that might influence capacity, demand, scheduling and flow. So much

of this is fundamental to the operational efficiency of the NHS but the NHS doesn’t use these systems to design its services, schedule it elective activity, pre-emptively flex capacity to match anticipated demand using processes that are robustly informed by real-time data analysis and predictive modelling.

“… the NHS doesn’t use these systems to design its services, schedule it elective activity, pre-emptively flex capacity to match anticipated demand using processes that are robustly informed by real-time data analysis and predictive modelling.”

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Optimising hospital schedulingThe problem

Suboptimal scheduling resulting in failed patient attendance costs the NHS £1bn every year, together with unquantifiable social cost of delayed treatment and investigation. The established approach is to send reminders – by telephone or text messaging – but implemented blindly, such interventions generally require >33 calls to prevent a single non-attendance. A better approach is first, to predict – at the level of individual appointments – the probability of attendance, enabling focused reminding, and second, to prescribe the best match of patient and appointment slot before the appointment is even made. Since the circumstances that cause non-attendance are complex and often highly individual, they are best modelled by combining fully-inclusive analysis of historical data with sophisticated machine learning techniques capable of illuminating a wide field of causal factors. A simple equation will not do.

Proof-of-concept solution

Surveying two years of MRI appointments at UCLH, we applied a state-of-the-art machine learning technique to gauge the complexity of the problem, and to create a highly accurate model of attendance. Optimum predictive performance was seen only after inclusion of 84 different routinely recorded features of each appointment – e.g. the nature of the test, its timing, and the clinical context, etc – establishing the need for sophisticated machine learning in this task (figure above). The excellent predictive performance of this complex model is demonstrated by an average area under the curve of the receiver operating characteristic of 0.85 (figure above right). The decision point corresponding to 50% specificity yields a sensitivity >95%.

Proposed development

This proof-of-concept paves the way for creating a fully-integrated, hospital-wide device, making use of the same approach to optimise scheduling within other domains such as outpatients.

Crucially, the algorithm can make its prediction at the point of booking, opening our intervention window to many months before the expected DNA. Not only can we regulate workflow of call operators, who are currently overstretched at times of high demand, we can expand and test intervention strategies beyond our current single call system, potentially reducing DNA rates to far below 10%.

By analysing the complex relation between patient characteristics and appointment features, we can then extend our system to prescribing the optimal appointment for a given patient. To take a trivial example, a patient living far away will generally find it easier to attend a later rather than an earlier appointment. Far more complex interactions could be successfully modelled, allowing us to match individual patients to appointment slots most likely to result in successful attendance.

Naturally, the system is proposed to operate subsidiarily to the explicit preferences of patients, which already guide the choice of appointment at the point of booking where possible.

Potential impact

Conservatively assuming only a 30% success rate for a targeted reminder call, our model could dramatically enhance the efficiency of telephone reminding, from ~66 calls for every 2 DNAs saved to only ~13 calls for every 2 DNAs saved. Widening the field of modelled factors, and adding prescriptive matching of patient to appointment slot could have a transformative impact on attendance while reducing the resource currently devoted to reminding. The efficiency of the hospital aside, removing unnecessary investigational delays will be key to meeting highly time-sensitive targets such as cancer investigation waiting times.

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“Widening the field of modelled factors, and adding prescriptive matching of patient to appointment slot could have a transformative impact on attendance while reducing the resource currently devoted to reminding.”

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Managing patient flowsThe problem

Patients should receive care in the environment best suited to needs that are determined – and evolve – during treatment. This necessarily implies movement of patients from one environment to another as they proceed through the care pathway. In stroke, for example, treatment typically begins at central units specialised for the acute setting, later progressing to peripheral units specialised for longer term care and rehabilitation. Since patients differ widely in their patterns of response and recovery, managing flow from one node to another within these distributed networks is far from easy. Crucially, reactive management – responding to changes in flow rather than anticipating them – is bound to be suboptimal, for excess or deficient capacity has to occur before mechanisms to correct it are triggered. In the absence of robust, accurate systems for predicting patient flows, reactive management is the only kind we can implement, resulting in resource inefficiencies and suboptimal care.

Proof-of-concept

Focusing on stroke as a major, time-pressured, distributed care pathway, and working with an industry partner, we have developed a proof-of-concept application, StrokeNET, that enables secure, controlled exchange of real-time information between stroke units, both within-hospital networks and – securely – across hospital trusts. Each unit receives comprehensive, multi-field information about the patient complement of each site, facilitating rapid and accurate decisions about patient flows. When used with StrokePad, our natively tablet-based electronic patient record for stroke patients, the system is immediately responsive to incoming information digitized at the point-of-care, facilitating highly time-pressured transfers for interventional neuroradiology or neurosurgery. We have demonstrated operation of the system within the same network, and across an N3 connection, covering possible installation scenarios within the NHS.


We propose installing StrokeNET at UCLH, involving partners within the regional stroke network. With the fundamental data infrastructure in place, predictive algorithms will be introduced, adding information about predicted as well as real-time current flows. We will extend the approach to other clinical pathways, integrating flow-predictive algorithms across the hospital enabled by our transition to a new general electronic healthcare record.

Potential impact

As medicine becomes increasingly fractionated into specialist sub-units, and care thereby distributed across specialist nodes, managing patient flows will become increasingly critical to delivering high quality care. Embedding predictive systems within the fabric of the hospital will enable us to anticipate flows, pre-empting fluctuations so as to maintain smooth, uninterrupted transfer from one node of the care pathway to the next.

“Embedding predictive systems within the fabric of the hospital will enable us to anticipate flows, pre-empting fluctuations so as to maintain smooth, uninterrupted transfer from one node of the care pathway to the next.”

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Acute cognitive impairment in hospitalThe problem

Acute cognitive decline (confusion or delirium) in the context of diverse medical illness is very common, affecting up to 50% of elderly patients in hospital, at an annual cost of $182 billion in the US alone. Reflecting a constitutional vulnerability of the brain to physiological insults arising from pathology elsewhere in the body, delirium is preventable in an estimated 30-40% of patients, provided the predisposing vulnerability is identified early. Crucially, such prediction needs to encompass the wide field of potential causal factors, including the imaging-determined state of the brain, necessitating the use of high-dimensional modelling. Patients with delirium are often too unwell to be investigated with magnetic resonance imaging without risk, requiring management decisions to be based on less easily interpretable, CT-based imaging.

Proof-of-concept solution

Though delirium must be distinguished from an array of similar disorders that may present in the same way, quantifying the degree of degenerative change in the brain – a measure of its vulnerability to delirium – is key. Applying machine learning-based analysis of 1036 scans from patients with acute cognitive dysfunction admitted to UCH and phenotyped by a consultant geriatrician, we have demonstrated we can automatically identify those with dementia or at risk of delirium from the CT alone, with a receiver operatic characteristic curve with an area under the curve of 0.84 (sd = 0.02) (see below). The addition of clinical and blood test variables can be expected to substantially improve on this already excellent performance.


A tool for delirium risk stratification, automatically generating a probabilistic index on admission, will allow delirium to be anticipated and potentially prevented through optimising manipuable causal factors.

Identifying actionable mechanisms in delirium will reveal the complex causal field of factors necessary to understand the natural heterogeneity of the disorder, thereby guiding specific preventative interventions.

We can implement a system for clinical pathway optimisation, making the care pathway adaptive to the risk of delirium, and identifying modifiable features that minimize it at the level of the operating unit.

Potential impact

Common, potentially devastating, preventable, and multifaceted in its causation, delirium is optimally placed to benefit from high-dimensional modelling. Based on investigations that already form a routine part of care, such modelling can substantially improve outcomes at the cost only of a framework for applying novel computational methods to existing clinical data. Minimal and highly efficient alterations to the care pathway can thus translate into substantial impact.

All of these developments require the application of AI approaches, supercomputing and access to large amounts of digital clinical data, linked to diagnoses, to develop and continuously refine the precision of the automated diagnostic processes. The NHS has more comprehensive data of this type, than any other health care system in the world, and is very strongly placed to lead in developing these tools for modern medicine. Importantly the personal data needed to develop these powerful tools does not need to be identifiable and should be anonymised to ensure the privacy and confidentiality of the patients.

The National Institute for Health Research University College London Hospitals Biomedical Research Centre is a partnership between University College London Hospitals NHS Foundation Trust and UCL (University College London) and is part of the National Institute for Health Research.

www.uclhospitals.brc.nihr.ac.uk

@UCLHresearch

www.uclh.nhs.uk

www.ucl.ac.uk

1st Floor, Maple House 149 Tottenham Court Road London W1T 7NF

Telephone: 020 7679 6639

May 2018

Design: Susan Rentoul Design

http://www.uclhospitals.brc.nihr.ac.uk

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http://www.uclh.nhs.uk

http://www.ucl.ac.uk

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