Dr Michael Allen
Senior Modeller
M.Allen@exeter.ac.uk
6080
+44 (0) 1392 726080
South Cloisters
South Cloisters, University of Exeter, St Luke's Campus, Heavitree Road, Exeter, EX1 2LU, UK
Overview
My career has taken me from new medicine R&D through to organization of healthcare services. I currently focus on application of clinical pathway simulation and machine learning.
Anexample project may be found here: https://samuel-book.github.io/samuel-1/introduction/intro.html
Qualifications
Ph.D. (University of Manchester), B.Sc. (Pharmacology, University of Bath)
Research
Research interests
Example recent papers:
Allen, M. and Thornton, S. (2013), Providing one-to-one care in labour. Analysis of ‘Birthrate Plus’ labour ward staffing in real and simulated labour ward environments. BJOG: An International Journal of Obstetrics & Gynaecology, 120: 100–107.
Clarey, A., Allen, M., Brace-McDonnell, S. & Cooke, M. (2013) Ambulance handovers – can a dedicated ED nurse solve the delay in ambulance turnaround times? Emergency Medicine Journal (in press, on oline http://emj.bmj.com/content/early/2013/04/30/emermed-2012-202258.short).
Research projects
Many of projects have fast turn around (3-4 months) in order to fit with the rapid changing healthcare environments. At this time the projects Michael is involved with include:
• Organization and capacity of neonatal care in the South West
• Orthopedic surgery waiting times
• Organization of mental health services
• Primary care: models of patient access
Grants/Funding
NIHR - The right cot, at the right time, at the right place. Use of Neonatal Survey(data and computer simulation technology to improve design and organisation of neonatal care networks (http://www.nets.nihr.ac.uk/projects/hsdr/10101148).
Publications
Journal articles
Peultier AC, Allen M, Redekop K, Peters J, Omer E, Severens J (In Press). EXPLORING THE COST-EFFECTIVENESS OF MECHANICAL THROMBECTOMY BEYOND SIX HOURS FOLLOWING ADVANCED IMAGING IN THE UK. Stroke
Spencer A, Pitt M, Allen M (In Press). The heterogeneous effects of neonatal care: a model of endogenous demand for multiple treatment options based on geographical access to care. Health Economics
Monks T, Harper A, Allen M, Collins L, Mayne A (2023). Forecasting the daily demand for emergency medical ambulances in England and Wales: a benchmark model and external validation. BMC Medical Informatics and Decision Making, 23(1). Abstract.
Zhelev Z, Peters J, Rogers M, Allen M, Kijauskaite G, Seedat F, Wilkinson E, Hyde C (2023). Test accuracy of artificial intelligence-based grading of fundus images in diabetic retinopathy screening: a systematic review. J Med Screen, 30(3), 97-112. Abstract. Author URL.
James C, Allen M, James M, Everson R (2023). Using machine learning and clinical registry data to uncover variation in clinical decision making. Intelligence-Based Medicine, 7, 100098-100098.
Pearn K, Allen M, Laws A, Monks T, Everson R, James M (2023). What would other emergency stroke teams do? Using explainable machine learning to understand variation in thrombolysis practice. European Stroke Journal, 8(4), 956-965. Abstract.
Allen M, Pearn K, Ford GA, White P, Rudd AG, McMeekin P, Stein K, James M (2022). National implementation of reperfusion for acute ischaemic stroke in England: How should services be configured? a modelling study. European Stroke Journal, 7(1), 28-40. Abstract.
Allen M, James C, Frost J, Liabo K, Pearn K, Monks T, Everson R, Stein K, James M (2022). Use of Clinical Pathway Simulation and Machine Learning to Identify Key Levers for Maximizing the Benefit of Intravenous Thrombolysis in Acute Stroke. Stroke, 53(9), 2758-2767. Abstract. Author URL.
Allen M, James C, Frost J, Liabo K, Pearn K, Monks T, Zhelev Z, Logan S, Everson R, James M, et al (2022). Using simulation and machine learning to maximise the benefit of intravenous thrombolysis in acute stroke in England and Wales: the SAMueL modelling and qualitative study. Health and Social Care Delivery Research, 10(31), 1-148. Abstract.
Allen M, Pearn K, Monks T (2021). Developing an OpenAI Gym-compatible framework and simulation environment. for testing Deep Reinforcement Learning agents solving the Ambulance Location. Problem. Abstract. Author URL.
Monks T, Allen M, Harper A, Mayne A, Collins L (2021). Forecasting the daily demand for emergency medical ambulances in England and Wales:. A benchmark model and external validation. Abstract.
Allen M, Pearn K, Ford GA, White P, Rudd A, McMeekin P, Stein K, James M (2021). Implementing the NHS England Long Term Plan for stroke: how should reperfusion services be configured? a modelling study. Abstract.
Allen M, Bhanji A, Willemsen J, Dudfield S, Logan S, Monks T (2020). A simulation modelling toolkit for organising outpatient dialysis services during the COVID-19 pandemic. PLOS ONE, 15(8), e0237628-e0237628.
Allen M, Pearn K, Stein K, James M (2020). Estimation of stroke outcomes based on time to thrombolysis and thrombectomy. Abstract.
Allen M, Villeneuve E, Pitt M, Thornton S (2020). How can consultant-led childbirth care at time of delivery be maximised? a modelling study. BMJ Open, 10(7), e034830-e034830. Abstract.
Allen M, Monks T (2020). Integrating Deep Reinforcement Learning Networks with Health System. Simulations. Abstract. Author URL.
Allen M, Bhanji A, Willemsen J, Dudfield S, Logan S, Monks T (2020). Organising outpatient dialysis services during the COVID-19 pandemic. A simulation and mathematical modelling study. Abstract.
White PM, Ford GA, James M, Allen M (2020). Regarding thrombectomy centre volumes and maximising access to thrombectomy services for stroke in England: a modelling study and mechanical thrombectomy for acute ischaemic stroke: an implementation guide for the UK. European Stroke Journal, 5(4), 451-452.
Allen M, Salmon A (2020). Synthesising artificial patient-level data for Open Science - an evaluation of five methods. Abstract.
Allen M, Pearn K, Monks T, Bray BD, Everson R, Salmon A, James M, Stein K (2019). Can clinical audits be enhanced by pathway simulation and machine learning? an example from the acute stroke pathway. BMJ Open, 9(9). Abstract. Author URL.
McMeekin P, Flynn D, Allen M, Coughlan D, Ford GA, Lumley H, Balami JS, James MA, Stein K, Burgess D, et al (2019). Estimating the effectiveness and cost-effectiveness of establishing additional endovascular Thrombectomy stroke Centres in England: a discrete event simulation. BMC Health Serv Res, 19(1). Abstract. Author URL.
Allen M, Pearn K, James M, Ford GA, White P, Rudd AG, McMeekin P, Stein K (2019). Maximising access to thrombectomy services for stroke in England: a modelling study. European Stroke Journal, 4(1), 39-49. Abstract.
Allen M, Pearn K, Villeneuve E, James M, Stein K (2019). Planning and Providing Acute Stroke Care in England: the Effect of Planning Footprint Size. FRONTIERS IN NEUROLOGY, 10 Author URL.
Villeneuve E, Landa P, Allen M, Spencer AE, Prosser S, Gibson A, Kelsey K, Mujica Mota R, Manktelow B, Modi N, et al (2018). A framework to address key issues of neonatal service configuration in England: the NeoNet multimethods study. NIHR Health Technology Assessment, 6 Abstract.
Monks T, van der Zee DJ, Lahr MMH, Allen M, Pearn K, James MA, Buskens E, Luijckx GJ (2017). A framework to accelerate simulation studies of hyperacute stroke systems. Operations Research for Health Care, 15, 57-67. Abstract.
Allen M, Pearn K, Villeneuve E, Monks T, Stein K, James M (2017). Feasibility of a hyper-acute stroke unit model of care across England: a modelling analysis. BMJ Open, 7(12). Abstract. Author URL.
Swancutt D, Joel-Edgar S, Allen M, Thomas D, Brant H, Benger J, Byng R, Pinkney J (2017). Not all waits are equal: an exploratory investigation of emergency care patient pathways. BMC Health Services Research, 17(1). Abstract.
Monks T, Worthington D, Allen M, Pitt M, Stein K, James MA (2016). A modelling tool for capacity planning in acute and community stroke services. BMC Health Serv Res, 16(1). Abstract. Author URL.
Pinkney J, Rance S, Benger J, Brant H, Joel-Edgar S, Swancutt D, Westlake D, Pearson M, Thomas D, Holme I, et al (2016). How can frontline expertise and new models of care best contribute to safely reducing avoidable acute admissions? a mixed-methods study of four acute hospitals. Abstract.
Craig P, Rahm-Hallberg I, Britten N, Borglin G, Meyer G, Köpke S, Noyes J, Chandler J, Levati S, Sales A, et al (2016). Researching Complex Interventions in Health: the State of the Art : Exeter, UK. 14-15 October 2015. BMC Health Serv Res, 16 Suppl 1(Suppl 1). Abstract. Author URL.
Allen M, Spencer A, Gibson A, Matthews J, Allwood A, Prosser S, Pitt M (2015). Right cot, right place, right time: improving the design and organisation of neonatal care networks – a computer simulation study. Health Services and Delivery Research, 3(20), 1-128. Abstract.
Clarey A, Allen M, Brace-McDonnell S, Cooke MW (2014). Ambulance handovers: can a dedicated ED nurse solve the delay in ambulance turnaround times?. Emergency Medicine Journal, 31(5), 419-420. Abstract.
Allen M, Thornton S (2014). Providing One-to-One Care in Labor. Analysis of “Birthrate Plus” Labor Ward Staffing in Real and Simulated Labor Ward Environments. Obstetric Anesthesia Digest, 34(1), 35-36.
Pearson M, Monks T, Gibson A, Allen M, Komashie A, Fordyce A, Harris-Golesworthy F, Pitt MA, Brailsford S, Stein K, et al (2013). Involving patients and the public in healthcare operational research-The challenges and opportunities. Operations Research for Health Care, 2 Abstract.
Allen M, Thornton S (2013). Providing one-to-one care in labour. Analysis of 'Birthrate Plus' labour ward staffing in real and simulated labour ward environments. BJOG: an International Journal of Obstetrics and Gynaecology, 120(1), 100-107. Abstract.
Allen M, Thornton S (2013). Providing one-to-one care in labour. Analysis of 'Birthrate Plus' labour ward staffing in real and simulated labour ward environments. BJOG, 120(1), 100-107. Abstract. Author URL.
Allen M, Wigglesworth MJ (2009). Innovation leading the way: application of lean manufacturing to sample management. J Biomol Screen, 14(5), 515-522. Abstract. Author URL.
Blanks AM, Vatish M, Allen MJ, Ladds G, de Wit NCJ, Slater DM, Thornton S (2003). Paracrine oxytocin and estradiol demonstrate a spatial increase in human intrauterine tissues with labor. J Clin Endocrinol Metab, 88(7), 3392-3400. Abstract. Author URL.
Wilson RJ, Allen MJ, Nandi M, Giles H, Thornton S (2001). Spontaneous contractions of myometrium from humans, non-human primate and rodents are sensitive to selective oxytocin receptor antagonism <i>in vitro</i>. BRITISH JOURNAL OF OBSTETRICS AND GYNAECOLOGY, 108(9), 960-966. Author URL.
Chapters
Pitt M, Monks T, Allen M (2015). Systems modelling for improving health care. In (Ed) Complex Interventions in Health: an Overview of Research Methods, 312-325.
Conferences
Russon C, Vaughan N, Pulsford R, Andrews R, Allen M (2022). Glycaemic events during exercise can be effectively predicted with machine learning using only start glucose and duration. European Association for the Study of Diabetes (EASD). 19th - 23rd Sep 2022. Abstract.
Monks T, Pearn K, Allen M (2016). Simulation of stroke care systems. Abstract.
Reports
Allen M, Spencer A, Gibson A, Matthews J, Allwood A, Prosser S, Pitt M, Spencer AE (2015). Right cot, right place, right time: improving the design and organisation of neonatal care networks.
