Imperial College London
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ProfessorRifatAtun

Faculty of Medicine, Department of Infectious Disease

Visiting Professor
 
 
 
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Contact

 

+44 (0)20 7594 9160r.atun Website

 
 
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Location

 

289aBusiness School BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Zhu:2020:10.1080/01605682.2020.1796537,
author = {Zhu, J and Ahmad, R and Holmes, A and Robotham, J and Lebcir, R and Atun, R},
doi = {10.1080/01605682.2020.1796537},
journal = {Journal of the Operational Research Society},
pages = {2490--2502},
title = {System dynamics modelling to formulate policy interventions to optimise antibiotic prescribing in hospitals},
url = {http://dx.doi.org/10.1080/01605682.2020.1796537},
volume = {72},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Multiple strategies have been used in the National Health System (NHS) in England to reduce inappropriate antibiotic prescribing and consumption in order to tackle antimicrobial resistance. These strategies have included, among others, restricting dispensing, introduction of prescribing guidelines, use of clinical audit, and performance reviews as well as strategies aimed at changing the prescribing behaviour of clinicians. However, behavioural interventions have had limited effect in optimising doctors’ antibiotic prescribing practices. This study examines the determinants of decision-making for antibiotic prescribing in hospitals in the NHS. A system dynamics model was constructed to capture structural and behavioural influences to simulate doctors’ prescribing practices. Data from the literature, patient records, healthcare professional interviews and survey responses were used to parameterise the model. The scenario simulation shows maximum improvements in guideline compliance are achieved when compliance among senior staff is increased, combined with fast laboratory turnaround of blood cultures, and microbiologist review. Improving guideline compliance of junior staff alone has limited impact. This first use of system dynamics modelling to study antibiotic prescribing decision-making demonstrates the applicability of the methodology for design and evaluation of future policies and interventions.
AU  - Zhu,J
AU  - Ahmad,R
AU  - Holmes,A
AU  - Robotham,J
AU  - Lebcir,R
AU  - Atun,R
DO  - 10.1080/01605682.2020.1796537
EP  - 2502
PY  - 2020///
SN  - 0160-5682
SP  - 2490
TI  - System dynamics modelling to formulate policy interventions to optimise antibiotic prescribing in hospitals
T2  - Journal of the Operational Research Society
UR  - http://dx.doi.org/10.1080/01605682.2020.1796537
UR  - https://www.tandfonline.com/doi/full/10.1080/01605682.2020.1796537
UR  - http://hdl.handle.net/10044/1/80769
VL  - 72
ER  -
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