Jo-An M Atkinson1, Robert Wells2, Andrew Page3, Amanda Dominello2, Mary Haines4, Andrew Wilson5. 1. The Australian Prevention Partnership Centre, Sydney, NSW, Australia; The Sax Institute, Sydney, NSW, Australia; School of Public Health, University of Sydney, NSW, Australia Jo-an.atkinson@saxinstitute.org.au. 2. The Sax Institute, Sydney, NSW, Australia. 3. School of Science and Health, University of Western Sydney, NSW, Australia. 4. The Sax Institute, Sydney, NSW, Australia; School of Public Health, University of Sydney, NSW, Australia. 5. The Australian Prevention Partnership Centre, Sydney, NSW, Australia; The Sax Institute, Sydney, NSW, Australia; Menzies Centre for Health Policy, University of Sydney, NSW, Australia.
Abstract
INTRODUCTION: The value of systems science modelling methods in the health sector is increasingly being recognised. Of particular promise is the potential of these methods to improve operational aspects of healthcare capacity and delivery, analyse policy options for health system reform and guide investments to address complex public health problems. Because it lends itself to a participatory approach, system dynamics modelling has been a particularly appealing method that aims to align stakeholder understanding of the underlying causes of a problem and achieve consensus for action. The aim of this review is to determine the effectiveness of system dynamics modelling for health policy, and explore the range and nature of its application. METHOD: A systematic search was conducted to identify articles published up to April 2015 from the PubMed, Web of Knowledge, Embase, ScienceDirect and Google Scholar databases. The grey literature was also searched. Papers eligible for inclusion were those that described applications of system dynamics modelling to support health policy at any level of government. RESULTS AND DISCUSSION: Six papers were identified, comprising eight case studies of the application of system dynamics modelling to support health policy. No analytic studies were found that examined the effectiveness of this type of modelling. Only three examples engaged multidisciplinary stakeholders in collective model building. Stakeholder participation in model building reportedly facilitated development of a common 'mental map' of the health problem, resulting in consensus about optimal policy strategy and garnering support for collaborative action. The paucity of relevant papers indicates that, although the volume of descriptive literature advocating the value of system dynamics modelling is considerable, its practical application to inform health policy making is yet to be routinely applied and rigorously evaluated. CONCLUSION: Advances in software are allowing the participatory model building approach to be extended to more sophisticated multimethod modelling that provides policy makers with more powerful tools to support the design of targeted, effective and equitable policy responses for complex health problems. Building capacity and investing in communication to promote these modelling methods, as well as documenting and evaluating their applications, will be vital to supporting uptake by policy makers.
INTRODUCTION: The value of systems science modelling methods in the health sector is increasingly being recognised. Of particular promise is the potential of these methods to improve operational aspects of healthcare capacity and delivery, analyse policy options for health system reform and guide investments to address complex public health problems. Because it lends itself to a participatory approach, system dynamics modelling has been a particularly appealing method that aims to align stakeholder understanding of the underlying causes of a problem and achieve consensus for action. The aim of this review is to determine the effectiveness of system dynamics modelling for health policy, and explore the range and nature of its application. METHOD: A systematic search was conducted to identify articles published up to April 2015 from the PubMed, Web of Knowledge, Embase, ScienceDirect and Google Scholar databases. The grey literature was also searched. Papers eligible for inclusion were those that described applications of system dynamics modelling to support health policy at any level of government. RESULTS AND DISCUSSION: Six papers were identified, comprising eight case studies of the application of system dynamics modelling to support health policy. No analytic studies were found that examined the effectiveness of this type of modelling. Only three examples engaged multidisciplinary stakeholders in collective model building. Stakeholder participation in model building reportedly facilitated development of a common 'mental map' of the health problem, resulting in consensus about optimal policy strategy and garnering support for collaborative action. The paucity of relevant papers indicates that, although the volume of descriptive literature advocating the value of system dynamics modelling is considerable, its practical application to inform health policy making is yet to be routinely applied and rigorously evaluated. CONCLUSION: Advances in software are allowing the participatory model building approach to be extended to more sophisticated multimethod modelling that provides policy makers with more powerful tools to support the design of targeted, effective and equitable policy responses for complex health problems. Building capacity and investing in communication to promote these modelling methods, as well as documenting and evaluating their applications, will be vital to supporting uptake by policy makers.
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Authors: Louise Freebairn; Lucie Rychetnik; Jo-An Atkinson; Paul Kelly; Geoff McDonnell; Nick Roberts; Christine Whittall; Sally Redman Journal: Health Res Policy Syst Date: 2017-10-02
Authors: Jo-An Atkinson; Dylan Knowles; John Wiggers; Michael Livingston; Robin Room; Ante Prodan; Geoff McDonnell; Eloise O'Donnell; Sandra Jones; Paul S Haber; David Muscatello; Nadine Ezard; Nghi Phung; Louise Freebairn; Devon Indig; Lucie Rychetnik; Jaithri Ananthapavan; Sonia Wutzke Journal: Int J Public Health Date: 2017-10-19 Impact factor: 3.380