GENERATION: Improving the efficiency and equity of tumour-agnostic clinical trial recruitment using automated electronic record based tools
Closing date: 17/11/2025
Leeds-Manchester Clinical Research Training Fellowship: GENERATION: Improving the efficiency and equity of tumour-agnostic clinical trial recruitment using automated electronic record based tools
Lead Supervisors: Dr Katie Spencer
Co-Supervisors: Michelle Collinson, Prof. Corinne Faivre-Finn, Dr Kieran Zucker
Applications Deadline: 12:00pm Monday 17th November 2025
Interviews: Week commencing 12th January 2026
Start date: September 2026
Project Keywords: informatics; clinical trials; research equity
Research Opportunity: Clinical Research Training Fellowship leading to the award of PhD
Project Outline
The delivery of clinical trials in the NHS is a policy priority, with increased trial efficiency and equity critical. The challenge of equitable patient identification is compounded in tumour-agnostic trials (for example, in bone metastases), where trial eligibility does not align with a diagnostically siloed clinical service. However, increasing digital maturity yields potential for automated electronic health-record based tools to overcome these challenges, increasing both the efficiency and equity of trial recruitment. The implementation of technology in the NHS is often challenging though due to system, staffing and financial constraints. As such, it is crucial that the value of any innovation is well defined and barriers and enablers to implementation carefully assessed to ensure appropriate and optimal implementation.
The treatment of bone metastases is a research priority due to their increasing incidence and consequent increasing morbidity, mortality and costs. Here, we will capitalise on synergies between Leeds and Manchester in clinical trials, data-analytics, digital innovation and patient-reported outcomes to refine an existing algorithm that uses structured data to identify patients with bone metastases for possible trial recruitment. The current algorithm will be refined using unstructured data, analysed with large-language models, in addition to patient-reported outcomes. The barriers and enablers to NHS implementation will be assessed through literature review, qualitative interviews and engagement with multi-disciplinary professionals from three NHS organisations.
Ultimately, by bringing together key strengths from Manchester and Leeds this project will deliver a refined algorithm for the identification of patients with bone metastases, assess potential for implementation/scale-up in the NHS and provide a framework for assessing the cost-benefit of this and future tools. With supervision from an excellent multi-disciplinary team, it will enable the candidate to develop their research skills with particular focus on the intersection between routine health data and clinical trials and the implementation of health technology.
Applications for this project are now open. Please complete your application on The University of Manchester website.
About Dr Katie Spencer (project Lead Supervisor)
Dr Katie Spencer is an Academic Consultant Clinical Oncologist based in the Academic Unit of Health Economics, University of Leeds and Leeds Cancer Centre. She developed expertise in health economic methodologies and the use of routine cancer data during her Medical Research Council-funded Doctoral Training Fellowship and was subsequently appointed as the National Disease Registration Service’s clinical lead for radiotherapy data. Her research is strongly collaborative and focuses on using routinely collected healthcare data to improve the value and equity of cancer care.
Key information
Before submitting an application, please ensure you have read the information below about the funding arrangements and eligibility for our Clinical Research Training Fellowships.
We also encourage you to get in contact with the lead supervisor to discuss the project and any particulars.
Further information is available on the Clinical Research Training Fellowships webpage.
The clinical fellowships are usually tenable for three years, although in certain circumstances they may be four years duration. The Fellowship covers:
- running expenses,
- an appropriate salary in line with the applicant’s current salary and grade
- full coverage of University PhD fees at the UK/home fee rate.
International Candidates
The University of Manchester aims to support the most outstanding applicants from outside the UK.
We are able to offer a limited number of bursaries to high-performing EU and international candidates, covering PhD fees only. Bursaries do not include financial support for visa/health surcharges.
We assess each EU and international candidate’s suitability for a bursary at the application and interview stages.
You must be a post-registration clinician, and ideally have a specialist post in a related subject, to apply for our Clinical Research Training Fellowships.
Applicants must hold an MBChB degree or an equivalent medical qualification. You should also hold, or be about to obtain, a minimum upper second class (or equivalent) undergraduate degree in a relevant subject. A related master’s degree would be an advantage.
It is generally expected that CRTFs will return to a training programme in the UK upon completion of their research degree.
International applicants (including EU nationals) must ensure they meet the academic eligibility criteria (including English Language) before contacting potential supervisors to express an interest in their project. Eligibility information can be found on the University’s Country Specific information page.
Applications for this programme are now open. Please submit your application on The University of Manchester application portal.
Key dates
- Applications open: Monday 6th October 2025
- Application deadline: 12:00pm Monday 17th November 2025
- Interviews: Week commencing 12th January 2026
- Start date: September 2026
Useful Links
Submit your application
Interested in applying for this opportunity? Submit your application on The University of Manchester application portal.
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