Ongoing CRIS projects: dementia


Patients with emergency admission to hospital are increasingly older and have a variety of underlying medical conditions.  They are also at higher risk of adverse outcomes such as falls and disability this is known as frailty as indicated by increased vulnerability and dependency on others. We know that these patients are at high risk of future dementia, particularly those who have new or worsened confusion (so-called “delirium”). However, not all hospitalised patients develop dementia and some go on to recover fully but at present, we do not know which patients will do well or badly. This makes it difficult to provide individualised care. In the current study, we will use existing electronic datasets from the Oxford University Hospitals NHS Foundation Trust (OUHFT) and the Oxford Health NHS Foundation Trust (OHFT) to identify the factors which predict future dementia in patients admitted to hospital.

Using OUHFT electronic patient records on patients aged >65 years, we have created the Oxford Cognitive Comorbidity and Ageing Research database (ORCHARD).  ORCHARD contains a wealth of information on clinical diagnoses, “observations” (measurements of blood pressure, breathing rate, temperature and heart rate), illness severity, blood testing, and X-rays or brain and body scans together with markers of frailty including confusion, poor nutrition and risk of pressure sores. To find out which ORCHARD patients go on to develop dementia on follow-up after admission to the OUHFT, we shall link ORCHARD with the OHFT Clinical Record Interactive Search (CRIS) database containing mental health outcomes. We shall then do data analysis to find out the factors that are linked to high dementia risk. Ultimately, we hope to be able to identify a few key risk factors that together can be used to stratify patients into low, medium and high risk groups. This information could then be used to help provide better care and information to patients and to select patients for follow-up in memory clinic or for trials to prevent dementia or further deterioration in thinking and memory.

Demon project


The use of artificial intelligence in brain imaging can help us to understand the underlying causes of mental illness and dementia, and support clinicians in to provide better treatment. Alzheimer’s disease represents a substantial and looming public health crisis, affecting roughly half a million people and one in fourteen older adults in the UK. Older adults with AD tend to have complicated health problems, and roughly half have three or more chronic conditions. They are at elevated risk for avoidable hospitalisations for chronic conditions, hospital readmission, urinary tract infection, inflammation, and other problems, which may be preventable with timely and effective care.

Data from routinely collected brain images could help guide care and support the early identification of patient needs. Clinical neuroimaging (“brain scans”) are collections of images showing the structure of a person’s brain obtained from Computed Tomography (commonly called CT or CAT scans) or Magnetic Resonance Imaging (often referred to as MRI scans).  They require expert review where a radiologist combines what they’ve been told about the patient (symptoms, signs and clinical history) with their interpretation of the images.  The result is a written clinical report which is delivered to the requesting clinician that describes the radiologist’s interpretation of the images in the specific clinical context.  In mental health, these written clinical reports are very often stored in the patient’s electronic health record to help the current (and future) clinicians make treatment decisions.  The “raw” imaging data itself ie. the scans themselves is not stored in the EHR because it is usually very large and always requires interpretation by an expert radiologist.

Though the written reports of brain scans may be contained in patients’ clinical notes, the usefulness of this readily available written information has not been tested. Natural language processing (NLP) refers to a group of methods for analysis of text content, which could be used to automatically explore untapped, yet critical data on relating to brain imaging found in clinical notes.


In this feasibility project we will explore the amount and usefulness of information on brain scans recorded in clinical notes for AD patients in Oxford Health. We will use NLP tools to search clinical notes in the CRIS database for imaging information.

We will also explore the availability of actual scan reports, which are sometimes attached to patient records. If we find evidence that there is enough useful information on brain scans in electronic patient records, we will use this feasibility study as justification to seek funding for a larger project to develop methods of automatically extracting this scan data using more advance NLP algorithms.

Page last reviewed: 20 March, 2024