PhD Studentship - Automated Clinical Coding using Large Language Models

Project Supervisors: Dr. Muhammad Faisal, Dr. Olufisayo Olakotan, Dr. Tom Lawton

Project Description:

Are you passionate about the intersection of technology and healthcare? Join us in transforming clinical coding through cutting-edge research in natural language processing (NLP) and large language models (LLMs)[1,2]. Clinical coding, the process of converting medical information from patient health records into structured codes for statistical analysis, is crucial yet often tedious and error-prone. Manual coding in the UK has an average accuracy of around 83%, with significant variability[3]. This PhD project offers the exciting opportunity to revolutionise this field by automating clinical coding, thereby enhancing both efficiency and accuracy.

In this project, you will explore the potential of LLMs for automated clinical coding, collaborating closely with healthcare professionals to identify and address specific challenges. Your research will focus on refining these advanced models and testing their real-world applications to improve coding accuracy and streamline clinical workflows. Additionally, you will investigate how enhanced coding practices can strengthen the reliability of key essential metrics such as mortality indices. By the end of your PhD, you will have developed robust, scalable solutions ready for integration into healthcare systems, providing actionable insights and improving the quality of clinical data coding.

Based within the Centre for Digital Innovations in Health & Social Care, you will join an active research centre and will be collaborating with researchers as part of a programme of research concerned with designing and evaluating technologies to improve decision making and reduce documentation burden. You will be benefit from full access to the researcher development sessions organised by the Centre, including writing retreats, away days, training, and associated events. You will also spend time at the Wolfson Centre for Applied Health Research, a collaboration between the Universities of Bradford and Leeds and Bradford Teaching Hospitals NHS Foundation Trust, where you will get to network with researchers from the NIHR Yorkshire & Humber Patient Safety Research Collaboration and the NIHR Yorkshire & Humber Applied Research Collaboration. You will also have access to researcher development sessions organised by the University and the White Rose Doctoral Training Partnership.

The studentship is for up to 4 years to allow you to spend time at the beginning of the PhD at Bradford Teaching Hospitals NHS Trust to become familiar with the data and refine your research questions and return at the end of the PhD to support the Trust in translating your research into practice.

Eligibility:

You should hold a first-degree equivalent to at least a UK upper second-class honours degree, or suitable postgraduate degree, in computer science, data science, machine learning, or relevant subject. You will be enthusiastic, organised and motivated with a commitment to improving healthcare services. Pre-knowledge of NLP is desirable but not necessary.

Funding notes:

This PhD scholarship is available for home students only. The scholarship will attract an annual tax-free stipend of £19,237 and will cover the tuition fees.

Funding for: UK Students

Enquiries email name and address:

For informal enquiries, please contact research@bradford.ac.uk

How to apply:

Potential candidates should apply directly online through the online application portal.

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