Mike Allen

Mike Allen#

Senior Research Fellow in Applied Healthcare Modelling and Data Science

Publications#

SAMueL-2 Preprints (Due for final publication 2024/25):#

Allen, M., Pearn, K., Jarvie, R., Laws, A., Frost, J., Farmer, L., McMeekin, P., Pope, C., Lang, I., Pratt-Boyden, K., Everson, R., & James, M. (2024). Stroke Audit Machine Learning (SAMueL-2). Zenodo. https://doi.org/10.5281/zenodo.12798409

Pearn, K., Allen, M., Laws, A., & James, M. (2024). Are the patients who would benefit from thrombolysis the same ones as those receiving it? A machine learning study of the UK stroke registry. Zenodo. https://doi.org/10.5281/zenodo.12798299

Pearn, K., Allen, M., Laws, A., & James, M. (2024). Thrombolysis: Are the results from the clinical trial meta-analysis seen in real life outcomes? A machine learning study of the UK stroke registry. Zenodo. https://doi.org/10.5281/zenodo.12798319

Pearn, K., Allen, M., Laws, A., McMeekin, P., & James, M. (2024). Identifying levers for improving thrombolysis use and outcomes – combining clinical pathway simulation and machine learning applied to the UK stroke registry. Zenodo. https://doi.org/10.5281/zenodo.13252978

2024#

McMeekin, P., McCarthy, S., McCarthy, A., Porteous, J., Allen, M., Laws, A., White, P., James, M., Ford, G.A., Shaw, L., Price, C.I., (2024). A lifetime economic model of mortality and secondary care use for patients discharged from hospital following acute stroke. International Journal of Stroke https://doi.org/10.1177/17474930241284447

Russon, C.L., Allen, M.J., Pulsford, R.M., Saunby, M., Vaughan, N., Cocks, M., Hesketh, K.L., Low, J., Andrews, R.C., (2024). A User-Friendly Web Tool for Custom Analysis of Continuous Glucose Monitoring Data. Journal of Diabetes Science and Technology. https://doi.org/10.1177/19322968241274322

Abigail Alton, Darren Flynn, David Burgess, Gary A Ford, Chris Price, Martin James, Peter McMeekin, Michael Allen, Lisa Shaw, Phil White (2024) Stroke Survivor Views on Ambulance Redirection as a Strategy to Increase Access to Thrombectomy in England. British Paramedic Journal 9: 1–9. https://doi.org/10.29045/14784726.2024.6.9.1.1

2023#

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. SAGE Publications. dx.doi.org/10.1177/23969873231189040

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. Cold Spring Harbor Laboratory. dx.doi.org/10.1101/2023.04.24.23289017

2022#

James, C., Allen, M., James, M., Everson, R. (2022). Using machine learning and clinical registry data to uncover variation in clinical decision making. Cold Spring Harbor Laboratory. dx.doi.org/10.1101/2022.10.06.22280684

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. Ovid Technologies (Wolters Kluwer Health). dx.doi.org/10.1161/STROKEAHA.121.038454

Allen, M., James, C., Frost, J., Liabo, K., Pearn, K., Monks, T., Zhelev, Z., Logan, S., Everson, R., James, M., Stein, K. (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. National Institute for Health and Care Research. dx.doi.org/10.3310/GVZL5699

2021#

Allen, M., Pearn, K., Ford, G., White, P., Rudd, A., McMeekin, P., Stein, K., James, M. (2021). National implementation of reperfusion for acute ischaemic stroke in England: How should services be configured? A modelling study. SAGE Publications. dx.doi.org/10.1177/23969873211063323

Allen, M., Pearn, K., Ford, G., 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. Center for Open Science. dx.doi.org/10.31219/osf.io/yg3x8

2020#

White, P., Ford, G., 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. SAGE Publications. dx.doi.org/10.1177/2396987320971126

Allen, M., Pearn, K., Stein, K., James, M. (2020). Estimation of stroke outcomes based on time to thrombolysis and thrombectomy. Cold Spring Harbor Laboratory. dx.doi.org/10.1101/2020.07.18.20156653

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. dx.doi.org/10.1136/bmjopen-2019-034830

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. Cold Spring Harbor Laboratory. dx.doi.org/10.1101/2020.04.22.20075457

Allen, M., Salmon, A. (2020). Synthesising artificial patient-level data for Open Science - an evaluation of five methods. Cold Spring Harbor Laboratory. dx.doi.org/10.1101/2020.10.09.20210138

2019#

Mújica‐Mota, R., Landa, P., Pitt, M., Allen, M., Spencer, A. (2019). The heterogeneous causal effects of neonatal care: a model of endogenous demand for multiple treatment options based on geographical access to care. Wiley. dx.doi.org/10.1002/hec.3970

McMeekin, P., Flynn, D., Allen, M., Coughlan, D., Ford, G., Lumley, H., Balami, J., James, M., Stein, K., Burgess, D., White, P. (2019). Estimating the effectiveness and cost-effectiveness of establishing additional endovascular Thrombectomy stroke Centres in England: a discrete event simulation. Springer Science and Business Media LLC. dx.doi.org/10.1186/s12913-019-4678-9

Peultier, A., Redekop, W., Allen, M., Peters, J., Eker, O., Severens, J. (2019). Exploring the Cost-Effectiveness of Mechanical Thrombectomy Beyond 6 Hours Following Advanced Imaging in the United Kingdom. Ovid Technologies (Wolters Kluwer Health). dx.doi.org/10.1161/STROKEAHA.119.026816

Allen, M., Pearn, K., Monks, T., Bray, B., 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. dx.doi.org/10.1136/bmjopen-2018-028296

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 Media SA. dx.doi.org/10.3389/fneur.2019.00150

2018#

Allen, M., Pearn, K., James, M., Ford, G., White, P., Rudd, A., McMeekin, P., Stein, K. (2018). Maximising access to thrombectomy services for stroke in England: A modelling study. SAGE Publications. dx.doi.org/10.1177/2396987318785421

Villeneuve, E., Landa, P., Allen, M., Spencer, A., Prosser, S., Gibson, A., Kelsey, K., Mujica-Mota, R., Manktelow, B., Modi, N., Thornton, S., Pitt, M. (2018). A framework to address key issues of neonatal service configuration in England: the NeoNet multimethods study. National Institute for Health and Care Research. dx.doi.org/10.3310/hsdr06350

2017#

Monks, T., van der Zee, D., Lahr, M., Allen, M., Pearn, K., James, M., Buskens, E., Luijckx, G. (2017). A framework to accelerate simulation studies of hyperacute stroke systems. Elsevier BV. dx.doi.org/10.1016/j.orhc.2017.09.002

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. Springer Science and Business Media LLC. dx.doi.org/10.1186/s12913-017-2349-2

2016#

Craig, P., Rahm-Hallberg, I., Britten, N., Borglin, G., Meyer, G., Köpke, S., Noyes, J., Chandler, J., Levati, S., Sales, A., Thabane, L., Giangregorio, L., Feeley, N., Cossette, S., Taylor, R., Hill, J., Richards, D., Kuyken, W., von Essen, L., Williams, A., Hemming, K., Lilford, R., Girling, A., Taljaard, M., Dimairo, M., Petticrew, M., Baird, J., Moore, G., Odendaal, W., Atkins, S., Lutge, E., Leon, N., Lewin, S., Payne, K., van Achterberg, T., Sermeus, W., Pitt, M., Monks, T. (2016). Researching Complex Interventions in Health: The State of the Art. Springer Science and Business Media LLC. dx.doi.org/10.1186/s12913-016-1274-0

Monks, T., Worthington, D., Allen, M., Pitt, M., Stein, K., James, M. (2016). A modelling tool for capacity planning in acute and community stroke services. Springer Science and Business Media LLC. dx.doi.org/10.1186/s12913-016-1789-4

2015#

Monks, T., Pearn, K., Allen, M. (2015). Simulation of stroke care systems. IEEE. dx.doi.org/10.1109/WSC.2015.7408262

2013#

Clarey, A., Allen, M., Brace-McDonnell, S., Cooke, M. (2013). Ambulance handovers: can a dedicated ED nurse solve the delay in ambulance turnaround times?. BMJ. dx.doi.org/10.1136/emermed-2012-202258

Pearson, M., Monks, T., Gibson, A., Allen, M., Komashie, A., Fordyce, A., Harris-Golesworthy, F., Pitt, M., Brailsford, S., Stein, K. (2013). Involving patients and the public in healthcare operational research—The challenges and opportunities. Elsevier BV. dx.doi.org/10.1016/j.orhc.2013.09.001

2012#

Allen, M., Thornton, S. (2012). Providing one‐to‐one care in labour. Analysis of ‘Birthrate Plus’ labour ward staffing in real and simulated labour ward environments. Wiley. dx.doi.org/10.1111/j.1471-0528.2012.03483.x

2009#

Allen, M., Wigglesworth, M. (2009). Innovation Leading the Way: Application of Lean Manufacturing to Sample Management. Elsevier BV. dx.doi.org/10.1177/1087057109336587

Funding#

HDR-UK British Heart Foundation Stroke Data Science Catalyst: BHF_SC001 £21k. What are the long-term consequences of stroke on the patient and to the NHS, and what contributes to variation? April - December 2024.

NIHR Health Services and Delivery Research Reference NIHR153982 £539k: Modelling the resource requirements for implementation of mobile stroke units across the National Health Service, their cost-effectiveness, and their effect on equity of access to emergency stroke care. https://fundingawards.nihr.ac.uk/award/NIHR153982. April 2023 to September 2024

NIHR Health Services and Delivery Research Reference NIHR134326: £589K Stroke Audit Machine Learning (SAMueL-2). April 2022 to March 2024. https://fundingawards.nihr.ac.uk/award/NIHR134326

NIHR Programme Grants for Applied Research NIHR202361: £1.983M OPTimising IMplementation of Ischaemic Stroke Thrombectomy (OptImIST). April 2021 to March 2026. https://fundingawards.nihr.ac.uk/award/NIHR202361

NIHR Programme Development Grant NIHR201692: £132K Enhancing and disseminating the outputs of the Promoting Effective and Rapid Stroke Care (PEARS) NIHR PGfAR Programme Grant & facilitating thrombectomy implementation in England. Dec 2020 to Nov 2021. https://fundingawards.nihr.ac.uk/award/NIHR201692

NIHR HS&DR Project: 17/99/89: £330K Use of simulation and machine learning to identify key levers for maximising the disability benefit of intravenous thrombolysis in acute stroke pathways. Feb 2019 - August 2021 https://fundingawards.nihr.ac.uk/award/17/99/89

NIHR HS&DR 14/19/08: £336,577. NeoNet: The right cot, at the right time, at the right place 2. Providing a national demand/capacity model for neonatal care in England. Pitt MA, Allen M, Spencer A, Gibson A, Prosser S, Manktelow B, Thornton S, Modi N. (2014-2017) https://fundingawards.nihr.ac.uk/award/14/19/08

NIHR SDO 10/1011/48: £133,468. 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. Pitt MA, Gibson A, Allen M, Manktelow BN, Allwood ACL, Spencer A, Prosser S. (2011-2014) https://fundingawards.nihr.ac.uk/award/10/1011/48