Exploring the Impact of Information Standards and Computerised Health Records Upon Inpatient Medicines Optimisation
PhDs and postgraduate research
Self-funded PhD students only
School of Computing
February and October
Applications accepted all year round
This is a self-funded, 3 year full-time or 6 year part-time PhD studentship, to commence in October or February.
Annual NHS expenditure on medication is over £15 billion, but within ten days of a new prescription a third of patients do not take it as directed. Many patients experience avoidable drug errors due to incomplete information-sharing between care providers.
Your aim in this project is to improve medication adherence for patients with long-term conditions through the use of personal health records. You'll explore how clinically-defined information standards and computerised health records (patient-held, organisational, regional) support medicine optimisation for hospitalised patients.
The work will include:
- exploring the practical experience of healthcare professionals who are implementing Professional Record Standards Body for health and social care (PRSB) standards, and the effect of variations in the computer systems that apply to them
- the use of mixed methods, including: qualitative exploration of user experience through observation and interview (patient and practitioner); quantitative analysis of outcome variables such as patient adherence to prescriptions, metrics of usability and user satisfaction and errors in prescription and administration
- use of the realist evaluation framework to identify the ideal configuration of context, mechanism and outcome in the use of information standards and computerised health records for the optimisation of medicine
- attending a range of Graduate School courses in research methodology
The Royal Pharmaceutical Society has led work with NHS England, the Royal College of GPs, the Royal College of Nursing and the Association of the British Pharmaceutical Industry to develop a framework for medicines optimisation.
Current health policy outlines patient empowerment as an essential driver for change, and highlights computerised personal health records as a way to support that.
However, the evidence base for personal health record benefits remains inconclusive. Evidence of the positive impact of electronic information systems in regards to cost, quality and safety of healthcare, remains contested.
Experts remain split on the existing evidence, between aspirational “believers” and more cautious evaluators. While it seems obvious that having better information about a patient will improve care, the translation of information into better decision-making is not well understood.
Fees and funding
Funding availability: Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).
2021/2022 fees (applicable for October 2021 and February 2022 start)
PhD and MPhil
Home/EU/CI full-time students: £4,407 p/a**
Home/EU/CI part-time students: £2,204 p/a**
International full-time students: £17,600 p/a*
International part-time students: £8,800 p/a*
PhD by Publication
External candidates: £4,407*
Members of staff: £1,720
All fees are subject to annual increase. If you are an EU student starting a programme in 2021/22 please visit this page.
*This is the 2020/21 UK Research and Innovation (UKRI) maximum studentship fee; this fee will increase to the 2021/22 UKRI maximum studentship fee when UKRI announces this rate in Spring 2021.
Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.
- A good honours degree or equivalent in a relevant subject or a master’s degree in an appropriate subject.
- Exceptionally, equivalent professional experience and/or qualifications will be considered. All applicants are subject to interview.
- English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
- The candidate should have a background in Computer Science or Mathematics.
- Prior knowledge of machine learning is desirable.
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How to apply
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