Using data science to optimise nursing shift patterns in acute hospitals
A fully funded studentship awarded by the Economic and Social Research Council (ESRC) South Coast Doctoral Training Partnership (SCDTP) commencing in 2021/22 Academic Year.
Would you like to make a difference to the NHS nursing workforce and patients? We are looking for a PhD student to develop and apply innovative modelling techniques to NHS workforce and patient data. You will use operational research and advanced analytic methods to identify new strategies to design nursing shift patterns that help reduce costs, absenteeism, and adverse events for patients including falls and medication errors. You will access a large dataset collected over seven UK hospitals, as well as benefit from the supervision of an interdisciplinary team of experts in shift work, workforce organisation and operational research.
Dr Chiara Dall’Ora, School of Health Sciences, C.Dallora@soton.ac.uk expert in nurses’ shift patterns and large datasets; Professor Peter Griffiths, NIHR ARC Wessex Workforce Theme Lead, firstname.lastname@example.org expert in nursing workforce and large datasets; Dr Carlos Lamas Fernandez, Business School, C.Lamas-Fernandez@soton.ac.uk operational researcher with expertise in scheduling and optimisation methods
Strong quantitative analysis skills with some knowledge of statistics/health economics/operational research. Existing database or programming skills would be valuable.
South Coast DTP Funding provides an annual maintenance grant (tax free) of £15285 (2020/21 UKRI rate), plus payment of programme fees. Other funding available for SCDTP funded students can be found on the SCDTP website (www.southcoastdtp.ac.uk).
Funding is provided for 3 years full-time PhD study (pro-rata for part-time students). Applications for 1+3 funding for students completing a Master's year prior to the commencement of PhD study are also welcome (details available at www.southcoastdtp.ac.uk).
We aim to be an equal opportunities employer and welcome applications from all sections of the community.