System dynamics modelling to formulate policy interventions to optimise antibiotic prescribing in hospitals

Nina J. Zhu*, Raheelah Ahmad, Alison Holmes, Julie Robotham, Reda Lebcir, Rifat Atun

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
JournalJournal of the Operational Research Society
Early online date7 Aug 2020
DOIs
Publication statusE-pub ahead of print - 7 Aug 2020

Bibliographical note

Funding Information: This research was funded by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Healthcare Associated Infections and Antimicrobial Resistance at Imperial College London in partnership with Public Health England (PHE), in collaboration with, Imperial Healthcare Partners, University of Cambridge and University of Warwick. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research, the Department of Health and Social Care or Public Health England.

Dr Nina Zhu, Dr Raheelah Ahmad, Dr Reda Lebcir, and Prof Alison Holmes gratefully acknowledge the support of ESRC as part of the Antimicrobial Cross Council initiative supported by the seven UK research councils, and also the support of the Global Challenges Research Fund. Co-author Prof Alison Holmes is a National Institute for Health Research (NIHR) Senior Investigator. Co-author Dr Raheelah Ahmad is supported by a National Institute for Health Research (NIHR) Fellowship in knowledge mobilisation at the NIHR HPRU in Healthcare Associated Infection and Antimicrobial Resistance. The grant number is KMRF-2015-04-007.

No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Open Access: This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Publisher Copyright: © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Citation: Nina J. Zhu, Raheelah Ahmad, Alison Holmes, Julie V. Robotham, Reda Lebcir & Rifat Atun (2020) System dynamics modelling to formulate policy interventions to optimise antibiotic prescribing in hospitals, Journal of the Operational Research Society,

DOI: 10.1080/01605682.2020.1796537

Keywords

  • System dynamics
  • antimicrobial resistance
  • antimicrobial stewardship
  • decision-making
  • systems thinking

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