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Details of Grant 

EPSRC Reference: EP/V047949/1
Title: Integrating hospital outpatient letters into the healthcare data space
Principal Investigator: Nenadic, Professor G
Other Investigators:
Jani, Dr M Dixon, Professor WG
Researcher Co-Investigators:
Project Partners:
IQVIA Salford Royal NHS Foundation Trust
Department: Computer Science
Organisation: University of Manchester, The
Scheme: Standard Research
Starts: 01 October 2021 Ends: 30 September 2024 Value (£): 767,579
EPSRC Research Topic Classifications:
Artificial Intelligence Computer Graphics & Visual.
Human Communication in ICT Information & Knowledge Mgmt
EPSRC Industrial Sector Classifications:
Healthcare
Related Grants:
Panel History:
Panel DatePanel NameOutcome
17 Feb 2021 HIPs 2020 Panel Meeting Announced
Summary on Grant Application Form
The importance of analysing health data collected as part of clinical care and stored in electronic health records is well-established. This has led to vital research about the occurrence and progression of disease, treatment effectiveness and safety, and health service delivery. The current Covid-19 pandemic has demonstrated the public health need to efficiently use data collected at the point of care to rapidly understand patterns, risk factors and outcomes of emerging diseases. Much of this work comes from primary care electronic health records, where general practitioners (GPs) enter and use structured, coded healthcare data. The picture in hospitals, however, is very different.

One in four people in the UK live with one or more long-term conditions like cardiovascular diseases, chronic respiratory diseases, type 2 diabetes, arthritis and cancer, which account for 70% of the NHS budget. Specialised opinion about management of long-term conditions (LTCs) is provided through hospital outpatient care. Data and insight from outpatient clinics, however, is almost entirely absent. There is, surprisingly, no national system for recording diagnoses in hospital outpatient clinics. Information about key clinical events is instead recorded in outpatient letters, which are primarily used to communicate with patients and GPs. The ways in which letters are written and their sensitive content mean that they are not available for larger-scale "secondary use", i.e. to support clinical practice, research or service improvement. For example, shielding for the current pandemic relied on hospital clinical teams going through patient letters manually to identify those who needed shielding based on free-text information about diagnoses and medications, with clear time constraints and risks to under- and over-shield patients.

Natural language processing (NLP) and text mining develop computer algorithms to automatically extract relevant information from free-text documents. This project will establish a partnership between academia, secondary care and industry to develop a standards-based information management framework to safely unlock information stored in outpatient letters, link it with other health data and demonstrate its impact and benefits through two case studies. We will develop new methods to extract key clinical events from letters and represent their details (e.g. medication used, duration of symptoms) in a computerised form so that it can be easily accessed. In doing so, we will use the NHS-adopted standards so that the outpatient letters can be linked to other hospital databases and do not live in their own silo. The protection of sensitive data that potentially appear in outpatient data is a prime concern, so we will develop clear rules on who and how can access such data, in particular considering that third parties (e.g. industry) may need to access that data for developing their tools. These rules will be developed in a close collaboration between patient representatives, clinicians and specialists to ensure safeguards, public trust and transparency of decision making.

We will demonstrate the potential impact of the proposed methods through two case studies with our clinical and business partners. Our first case study will demonstrate how the proposed models can assist in timely, efficient, dynamic and transparent identification of patients for shielding in a pandemic, or for vaccination prioritisation. In the second case study, we will illustrate how the same information can be used address important gaps in our knowledge about health and care, including, for example, disease prevalence and drug utilisation patterns. All outputs will be developed in a way that can be scaled beyond the single clinical site and single speciality.

Key Findings
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Potential use in non-academic contexts
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Impacts
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Organisation Website: http://www.man.ac.uk