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The University of Southampton
Health Sciences
Email:
thomas.monks@soton.ac.uk

Dr Thomas Monks 

Principal Research Fellow of Operational Research and Data Science

Dr Thomas Monks's photo

Dr Thomas Monks is a Principal Research Fellow in Operational Research and Data Science at the University of Southampton.

Operational research is the application of mathematical, computer and systematic modelling techniques to aid complex decision making. In health service research OR aims to improve patient outcomes, increase efficiency and improve understanding of system behaviour.

He is also Director of Data Science for the NIHR Collaboration in Leadership and Applied Health Research (CLAHRC) Wessex. Thomas is a methodologist with expertise is in applying computer simulation methods, mathematical modelling and machine learning in health service delivery.

I joined Health Sciences in July 2014 as part of NIHR CLAHRC Wessex.  Prior to this I worked at University of Exeter Medical School as part of their healthcare Operational Research group.  My early career was in computer science, software engineering and operational research in the private and public sectors.  My work focusses on the translation of Data Science and Operational Research tools to health and social care.  I have expertise in application of mathematical modelling and machine learning to help the NHS improve service delivery. 

Broad research specialisms

Qualifications

Background

I joined Health Sciences in July 2014 as part of NIHR CLAHRC Wessex after four years working at University of Exeter Medical School. My work focusses on the application of Operational Research modelling in healthcare. In particular, my research has used modelling and analysis to help the NHS improve stroke thrombolysis services.

Broad research specialisms

Research interests

My research interests are related to the translation and application of data science and operational research in healthcare.

Implementation science: My research here is focussed on understanding the mechanisms the enable or prohibit the successful uptake of mathematical modelling and machine learning to improve service delivery in health and social care services.

Urgent and emergency care: This includes computer simulation, particularly discrete-event simulation of accident and emergency departments, computer simulation stroke services (particularly stroke thrombolysis), machine learning aiming to predict emergency demand and identifying intra-hospital barriers to patient flow.

Community services: My work in community services is concerned with logistics.  Recent work has considered the provision and access of sexual health services in Hampshire, the development of routing and scheduling tools to coordinate how teams of nurses visit patients in their own homes, and the costs and consequences of different modes of delivering point of care testing for LRTI in the primary care.

Safe staffing of hospitals: My work with Professor Peter Griffiths is concerned with mathematical modelling of hospital staffing policies and how these decisions affect patient safety long term.

Real-time decision support systems: The onset of faster computer simulation and collection of real-time service delivery data to support machine learning means that the NHS can begin to adopt predictive and decision support tools for managing its demand.  I am interested in how current developments in data science and operational research can be adapted and applied in healthcare (particularly in U&EC and community settings) and the gaps in current methodology for real-time prediction.

Affiliate research group

Health Work

Research project(s)

Identifying nurse-staffing requirements using the Safer Nursing Care Tool. Modelling the costs and consequences of real world application to address variation in patient need on hospital wards

The study will examine how patients' need for nursing care, varies from day to day. This will allow us to see how well staffing matches need and to explore the costs and consequences of different ways of deploying nurses to meet varying need.

  • Director.  CLAHRC Wessex Data Science Hub.
  • Associate Editor Journal of Simulation (Healthcare)
  • Associate Editor Health Systems
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Articles

Book Chapter

  • Pitt, M., Monks, T., & Allen, M. (Accepted/In press). Systems modelling for improving healthcare. In D. Richards, & I. R. Hallberg (Eds.), Complex Interventions in Health: An Overview of Research Methods Abingdon, GB: Routledge.

Conferences

Creative Media and Artefacts

Dr Thomas Monks
NIHR CLAHRC Wessex Methodological Hub Room AA72 South Academic Block Southampton General Hospital Southampton HANTS SO16 6YD

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