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GW4 BioMed MRC DTP PhD studentship: Using artificial intelligence to identify risks to patient safety from poor quality medicines guidance for health professionals

  • Full or part time
  • Application Deadline
    Monday, November 25, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

This project is one of a number that are in competition for funding from the ‘GW4 BioMed MRC Doctoral Training Partnership’ which is offering up to 18 studentships for entry in September 2020.

The DTP brings together the Universities of Bath, Bristol, Cardiff and Exeter to develop the next generation of biomedical researchers. Students will have access to the combined research strengths, training expertise and resources of the four research-intensive universities.


Lead supervisor: Dr Matthew Jones, Department of Pharmacy and Pharmacology, University of Bath
Co-supervisors: Dr Anita McGrogan (Bath), Dr Hélène De Ribaupierre (Cardiff) and Dr Hannah Family (Bristol)


In this project, medicines errors caused by poorly written professional guidance will be investigated by developing an artificial intelligence algorithm to analyse a database of NHS error reports. There will be a placement involving medicines safety analysis in the NHS. The student will gain skills and training in artificial intelligence and statistics for large data sets, and knowledge of NHS medicines safety.
There are 237 million medication errors in England per year. Approximately 28% cause harm to patients and cost the NHS £98 million. There are many causes of errors and all need to be addressed to increase safety.

One recognised cause in many settings is poorly written guidance for health professionals leading to difficulty finding relevant, unambiguous information. Our research has identified tools that improve guidance and increase safety. This project will generate the data needed to target these tools at high-risk areas: the frequency of medication errors caused by poor quality guidance; the types of guidance frequently implicated and the types and severity of the associated errors.

A systematic review and meta-analysis will address these knowledge gaps using international literature with sub-group analysis of NHS data. A second study will analyse the National Reporting and Learning System (NRLS), a database containing thousands of reports of NHS medication errors. This is a rich data source but it is not coded to identify errors caused by guidance, so analysis of free text is required. Given the scale of the NRLS, automation is needed. Natural language processing (NLP, a subfield of Artificial Intelligence and Machine Learning (ML)) has been used to analyse incident reports in safety-critical industries but is not widely used in medicines safety. Therefore, this project will develop NLP and ML algorithms to identify errors related to poor quality information. The best approach will be used to analyse the NRLS dataset and address the research questions.

To help the student understand the context in which medication errors occur and how the NHS uses incident report data to improve care, they will undertake a three-month placement in at least one NHS organisation. During this time, they will work with a Medication Safety Officer to investigate incidents involving medicines and implement quality improvement initiatives. They will also shadow health professionals, to help them understand the pressures and influences that create the environment in which errors occur.

The student will be able to access a wide range of training opportunities through each university, including regular research seminars and journal clubs. There will be training in data science and AI available from the new Data Science Academy at Cardiff University and a range of short courses in medical statistics and evidence synthesis from Bristol Medical School.


Applicants for a studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an area appropriate to the skills requirements of the project.

IMPORTANT: In order to apply for this project, you should apply using the DTP’s online application form:

You do NOT need to apply to the University of Bath at this stage – only those applicants who are successful in obtaining an offer of funding form the DTP will be required to submit an application to study at Bath.

More information on the application process may be found here:


Funding Notes

A full studentship will cover UK/EU tuition fees, a Research and Training Support Grant of £2-5k per annum and a stipend (£15,009 per annum for 2019/20, updated each year) for 3.5 years.

UK and EU applicants who have been residing in the UK since September 2017 will be eligible for a full award; a limited number of studentships may be available to EU applicants not meeting the residency requirement. Applicants who are classed as Overseas for tuition fee purposes are not eligible for funding.

More information on eligibility may be found here: View Website

How good is research at University of Bath in Allied Health Professions, Dentistry, Nursing and Pharmacy?

FTE Category A staff submitted: 54.20

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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