University of Hertfordshire

From the same journal

From the same journal

By the same authors

Documents

  • Nidhee Jadeja
  • Nina J. Zhu
  • Mohamed Lebcir
  • Franco Sassi
  • Alison Holmes
  • Raheelah Ahmad
  • Behzad Behdani (Editor)
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Original languageEnglish
Article numbere0263299
Number of pages20
JournalPLoS ONE
Volume17
Issue2
Early online date10 Feb 2022
DOIs
Publication statusE-pub ahead of print - 10 Feb 2022

Abstract

Background: Decision-makers for public policy are increasingly utilising systems approaches such as system dynamics (SD) modelling, which test alternative interventions or policies for their potential impact while accounting for complexity. These approaches, however, have not consistently included an economic efficiency analysis dimension. This systematic review aims to examine how, and in what ways, system dynamics modelling approaches incorporate economic efficiency analyses to inform decision-making on innovations (improvements in products, services, or processes) in the public sector, with a particular interest in health. Methods and findings: Relevant studies (n = 29) were identified through a systematic search and screening of four electronic databases and backward citation search, and analysed for key characteristics and themes related to the analytical methods applied. Economic efficiency analysis approaches within SD broadly fell into two categories: as embedded sub-models or as cost calculations based on the outputs of the SD model. Embdedded sub-models within a dynamic SD framework can reveal a clear allocation of costs and benefits to periods of time, whereas cost calculations based on the SD model outputs can be useful for high-level resource allocation decisions. Conclusions: This systematic review reveals that SD modelling is not currently used to its full potential to evaluate the technical or allocative efficiency of public sector innovations, particularly in health. The limited reporting on the experience or methodological challenges of applying allocated efficiency analyses with SD, particularly with dynamic embedded models, hampers common learning lessons to draw from and build on. Further application and comprehensive reporting of this approach would be welcome to develop the methodology further.

Notes

© 2022 Jadeja et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.

ID: 26813476