Postgrad LIVE! Study Fairs

Southampton | Bristol

Coventry University Featured PhD Programmes
Engineering and Physical Sciences Research Council Featured PhD Programmes
University of Warwick Featured PhD Programmes
FindA University Ltd Featured PhD Programmes
University of Warwick Featured PhD Programmes

Clinical outcome modelling

This project is no longer listed in the FindAPhD
database and may not be available.

Click here to search the FindAPhD database
for PhD studentship opportunities
  • Full or part time
    Prof J Briggs
    Prof D Prytherch
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Applications are invited for a fully-funded three year PhD to commence in October 2019.

The PhD will be based in the School of Computing and will be supervised by Professor Jim Briggs and Professor David Prytherch.

The work on this project will aim to:
-develop a new mortality prediction tool that can be used to identify changes in a hospital’s clinical outcomes and to compare the performance of hospitals
-build on our unique dataset of vital signs data and laboratory results, coupled with information drawn from other clinical information systems



Project description

The Centre for Healthcare Modelling and Informatics (CHMI) is a long-established health informatics research and innovation group. In collaboration with Portsmouth Hospitals and others, our work in clinical outcome modelling has supported the development of the VitalPAC vital signs collection system and the National Early Warning Score (NEWS) recommended by the Royal College of Physicians, among many other projects.

The aim of the project is to develop a new mortality prediction tool that can be used to identify changes in a hospital’s clinical outcomes and to compare the performance of hospitals. A hospital where significantly more patients die than expected could be underperforming. The expected mortality is based on the number of patients and the seriousness of their condition (obviously, very sick people are more likely to die than those who are not). Current tools used for this (e.g. HSMR and SHMI) are based on the administrative data that records the diagnoses associated with a patient’s hospital stay. They adjust for various factors such as the patient’s age, sex and their existing medical problems, however, the data can be "gamed" and the quality of the data is variable – for example, it is often recorded long after the patient has left the hospital and only analysed much later.

This project would start from the position that clinical performance should be measured by clinical data. Actual data, recorded as part of the normal delivery of care (such as the results of laboratory tests on blood and vital signs taken at the start of a patient’s stay) are less prone to error or gaming, and should provide an objective (physiological) ruler with which to measure a patient’s degree of sickness. More and more such data is now available in NHS hospitals. Several aspects of the problem are open to investigation including how the tool could be calibrated initially and adjusted according to changing practice.

This project will build on our unique dataset of vital signs data and laboratory results, coupled with information drawn from other clinical information systems. Data from a second hospital may be available for comparison purposes.

A student undertaking this work could expect to find employment in healthcare data analytics (either in the NHS or in industry) or health service management, as well as in academia.



Entry Requirements

General admissions criteria
You’ll need a good first degree from an internationally recognised university (minimum upper second class
or equivalent, depending on your chosen course) or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or Qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.



How to Apply
We’d encourage you to contact Professor Jim Briggs ([Email Address Removed]) to discuss your interest before you apply, quoting the project code.

When you are ready to apply, you can use our online application form and select ‘Computing’ as the subject area. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.


If you want to be considered for this funded PhD opportunity you must quote project code CCTS4570219 when applying.

Funding Notes

Candidates applying for this project may be eligible to compete for one of a small number of bursaries available. The bursary is available to UK and EU students only and covers tuition fees and an annual maintenance grant in line with the RCUK rate (£14,777 for 2018/19). The Faculty of Technology may fund project costs/consumables up to £1,500 p.a.

Related Subjects



FindAPhD. Copyright 2005-2019
All rights reserved.