Title: Predictive Machine Learning and Digital Health for Improving Patient Outcomes
Funder: Department of Health and Social Care

Abstract: Research question: Construct a real-time AI platform connected to hospital data streams for interconnected use in the Emergency Department, main hospital, and at discharge. Demonstrate patient benefit and improvements in hospital provision via the development of novel interconnected exemplar tools. Background: Ubiquitous acquisition of physiology, blood tests, scans, diagnoses, prescriptions, and equipment-location data results in very large repositories of data that mostly go unused in routine care. AI-based systems can exploit such real-time streaming data, also using repositories of historical data and data from unobtrusive patient-worn wearables, to improve patient care through the predictive assessment of health status. Available to this Research Professorship are some of the world's largest (non-imaging) datasets of their kind, exceeding 3 million admissions. Conventional methods of data analysis are inappropriate for handling such massively-multivariate and -multimodal data; recent advances in hospital-oriented AI are required to construct robust system at this new, large scale of data. Aims and objectives: Seven exemplar predictive tools will be created for use, focusing on (1) improving ED flow and patient care, (2) improving the treatment of deteriorating patients in the main hospital to reduce 'avoidable deaths', (3) supporting the patient-discharge process. For each of the seven tools, objectives are co-design of candidate models with clinicians and patients; blinded studies (2 months duration) of the prototypes via NHS partners; evaluation and revision of models with clinicians and patients; unblinded studies (6 months duration) of the revised prototypes; dissemination of results. Study durations and sizes are indicative and will be designed with collaborators during the programme. Methods: Novel 'end-to-end' deep learning architectures will be compared against conventional AI methods (XGBoost, etc.) and medical statistical benchmarks (logistic regression, Cox models as appropriate). The complexity of datasets comprising several million patient admissions, each being longitudinal and massively multivariate, requires new, explanatory methods to construct models optimally; such methods include 'continual' and 'sequential' learning, using single complex models across multiple interconnected tasks, and in estimating the relative 'informativeness' of individual examples within the dataset. Timelines for delivery: Exemplars will be developed and prototyped (years 1-2), deployed for study (years 2-3), iterated (years 3-4), and redeployed for study (years 3-5), with IP registration and applications for regulatory approvals during the programme. Anticipated impact: Varying by tool, with the main aim of demonstrating that a platform of interconnected predictive tools can both improve patient outcomes and improve the 'flow' of patients within hospitals. Impact metrics will be decided upon and tracked with clinical partners and reviewed regularly, including model efficacy (with respect to comparators), and subsequent changes in patient-care metrics (e.g., accuracy of discharge predictions, and any effect on length-of-stay / bed-occupancy / patient flow). Subsequent scale-up with industrial partners within and after the programme. Co-funding opportunities to build on the developed platform with clinical collaborators in various specialties, and in Primary Care. Dissemination via high-impact scientific and medical journals and conferences; annual workshops and public exhibitions; outreach via media, schools, and governmental-advisory roles, supported by senior appointment as a Research Chair in 'AI for Healthcare'.

Lay abstract: When patients are in hospital, ever more information about them is collected - such as the results of blood tests, bodily measurements or vital signs (such as blood pressure), diagnoses about any previous healthcare conditions that patients have had, the medicines that they have been prescribed, and so on. Much of this information is collected so that doctors can look into patient's health records to help them decide how best to treat patients. However, there is also a very great opportunity to use these large quantities of data that have been collected, in anonymised and ethically-approved ways, for medical research to help future patients -h; including the development of artificial intelligence (AI) systems that are carefully designed with doctors, AI scientists, and patients themselves.AI systems are particularly helpful for using information that is routinely collected to make predictions about what a patient might need in hospital. For example, if a patient comes to the A&E (Accident & Emergency) Department, lots of information is collected about them when they arrive -h; and where any previous visits to the hospital will also have similar information stored about them. An AI system is well-suited for using such information to identify immediately and accurately, for instance, whether or not a patient has a particular disease (such as COVID-19), rather than having to wait several hours for the results of the test ('PCR') that would confirm it, so that the patient can be treated immediately; the same system can then help doctors decide which patients are likely to go on to have severe types of the disease; the same system can be used to get beds and ventilators ready for the patient in advance.This Research Professorship aims to create an 'always-on' AI system for the hospital, working week-by-week with doctors and patients, that can use 'all of the information, all of the time' to identify ways in which predictions about patient health can be used to improve the overall running of the hospital, and to improve the care that individual patients receive. A part of the work will be to ask some patients to use comfortable wearable devices (such as an Apple Watch) to collect additional data to help care for them. The research will use the latest advances in AI that have been developed by Prof Clifton specifically for use with healthcare information in hospitals. Now that the volume of information in hospitals has become so great, these new methods of AI are needed to build tools that work sufficiently well to be used safely in helping doctors make decisions for the care of patients. Importantly, the AI systems act as advisors to the skilled doctor (and not replacements for them), by being able to show the doctors recommendations based on the patient information -h; so that the doctor can make the final decision about how best to treat a patient. This Research Professorship builds on Prof Clifton's successful track record of designing tools with doctors that are used in hospitals -h; including tools now used for standard care. The emphasis in this work is to include patients, carers, and the public at every stage of the work, to help design technology that best improves patient care, and which demonstrates that AI has a key role to play in these improvements. Unusually for such technology-design processes, patients and doctors will be involved every week to ensure that the AI is fit-for-purpose; and where the results will be the subject of carefully-controlled hospital studies to ensure that they work effectively for patient benefit.

Research activity codes: 6.3 MEDICAL DEVICES;7.3 MANAGEMENT AND DECISION MAKING
Health categories: GENERIC HEALTH RELEVANCE