CHIMERA: Collaborative Healthcare Innovation through Mathematics, EngineeRing and AI

Lead Research Organisation: University College London
Department Name: Mechanical Engineering


Hospitals collect a wealth of physiological data that provide information on patient health. Full use of this data is significantly limited by its complexity and by a limited mechanistic understanding of the relationship between internal physiology and external measurement. Addressing this challenge requires multidisciplinary collaboration between mathematicians developing new biomechanical models, clinicians who measure and interpret the data to treat patients, and statistical and computational scientists to bridge the two-way translation between model output and real-life data. CHIMERA is designed to foster such collaboration to generate new understanding of physiology, new methods for relating physiology to real time data, and, finally, to translate these into practice, improving outcomes for patients by supporting clinical decision making.

CHIMERA will start by focusing on the most critically ill patients within hospital intensive care units: such patients have by far the most monitoring data and are most likely to benefit from improved understanding of what that data can tell us about their underlying physical state. Each year about 20,000 children and 300,000 adults in the UK need intensive care. These critically ill patients are continuously monitored at the bedside, including measurements of heart rate, breathing rate, blood pressure and other vital sign data. However, the wealth of these physiological data are not currently used to inform clinical decision making and clinicians can only really use real-time snapshots of the physiology to guide their decisions.

CHIMERA will address this unmet opportunity to use individual patient physiological data to support clinical decision making, with the potential to impact on patient management across the UK and beyond. This will be achieved through a multidisciplinary Hub which brings together experts in mathematics, statistics, data science and machine learning, with unique, high volume and rich data sets from both adult and paediatric Intensive Care Units provided through embedded Project Partnerships with Great Ormond Street Hospital (GOSH) and University College London Hospital (UCLH). CHIMERA will deliver new mathematical frameworks to learn the biophysical relationships that govern the interdependencies between physiological variables, based on data sets for thousands of patients through these project partners. Clinical impact will be achieved through an extensive series of clinically-led, multidisciplinary workshops themed around specific opportunities to improve care, for example identifying deteriorating patients in advance of an adverse event such as heart attack or stroke, or advance warning systems to diagnose sepsis. These workshops will include partnering with the Alan Turing Institute (the national centre for AI and Data Science), will be open to national participation, and will provide a mechanism to fund new projects by making available seed corn funding, PhD studentships and researcher resource for new interdisciplinary teams and partnerships. CHIMERA will build new links with clinical centres, companies and academic units across the UK and internationally, expand to work with a variety of patient monitoring data, and provide dedicated support to nurture new projects, funding bids and collaborations. In this way, we will build CHIMERA to a self-sustaining, multidisciplinary and vibrant Centre for the application of mathematical and data sciences tools in patient care.

Planned Impact


Intensive care units (ICUs) treat the country's most critically ill patients. Intensive care is characterised by high resource use (both in equipment and staff), uncertainty in outcome and length of stay; and high levels of stress for patients and their families. Patients are intensively monitored, with almost all patients receiving continuous bedside monitoring for their heart rate, blood pressure, temperature, and how Oxygen and Carbon Dioxide are being used by the body. Such vital signs provide doctors and nurses crucial information about how well the patient's body is recovering from critical illness, particularly given patients are usually sedated and cannot provide direct feedback. Currently, clinicians can only really use real-time snapshots of the physiology to guide their decisions, with existing automatic monitor alarms being generally crude and unsophisticated.

CHIMERA's main clinical impact will be new mathematical and data science techniques that can support the decisions of the clinical team. Our models will provide clinicians with a better idea of how well the patient's body is recovering by using clues hidden in the wealth of physiological data collected for that patient since their admission to ICU. The possibilities for improving care in this way are practically endless but some of our foci include:
- ensuring that patients are weaned off organ support only when their bodies are ready to support themselves, but not longer than necessary;
- spotting deterioration before it becomes obvious through some adverse event such as a heart attack or stroke.
- diagnosing sepsis (a treatable but very dangerous condition) more quickly than can be done currently
- enabling better treatment for rare or complex conditions where a patient's physiology is very different from the norm making the monitor data harder to interpret (e.g. a severe heart defect).

In the longer term our work should lead to new clinical tools that could increase survival after ICU and reduce the time needed for patients to recover. With ICU stays typically costing £2000 - £5000 a day, any systematic reductions in length of stay could generate significant savings for the NHS. Meanwhile, we will be leveraging the enormous amount of data currently collected for each patient in ICU but that is currently not used in decision making.

During the lifetime of this hub, the impact will be more indirect as we increase our understanding of what the physiological data is telling us about how well someone's body is working, and discover promising avenues for new larger projects to develop clinical support tools. However, we will explore relationships with private sector partners (e.g. Microsoft, Google DeepMind) alongside our clinical partners in years 3 and 4 on how to translate our research into practice.


There has been a great deal of media coverage about the potential of data science and AI to improve health services, both in the UK and abroad. This has led to unrealistic expectations for what data science can actually achieve, and a public debate over how sensitive patient data should be used by researchers and by whom - particularly whether private companies such as Google, DeepMind should have access to NHS data.

CHIMERA will address both issues. Firstly, through our programme of public and patient engagement we will explain what we are researching, its potential to improve care as well as its limitations and what it cannot do. Secondly, we will engage the public and patients on how such new large datasets should be managed and governed - for instance, how do we balance the opportunities from private sector partners with risks to patient data and patient trust? How can we safely scale up the Hub's activities while ensuring transparent governance and accountability? Who owns the data and its anonymised derivatives, how should they be used and how will we communicate their use?


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