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Objective: Successive suicide prevention frameworks and action plans in Australia and internationally have called for improvements to mental health services and enhancement of workforce capacity. However, there is debate regarding the priorities for resource allocation and the optimal combination of mental health services to best prevent suicidal behaviour. This study investigates the potential impacts of service capacity improvements on the incidence of suicidal behaviour in the Australian context. Methods: A system dynamics model was developed to investigate the optimal combination of (1) secondary (acute) mental health service capacity, (2) non-secondary (non-acute) mental health service capacity and (3) resources to re-engage those lost to services on the incidence of suicidal behaviour over the period 2018–2028 for the Greater Western Sydney (Australia) population catchment. The model captured population and behavioural dynamics and mental health service referral pathways and was validated using population survey and administrative data, evidence syntheses and an expert stakeholder group. Results: Findings suggest that 28% of attempted suicide and 29% of suicides could be averted over the forecast period based on a combination of increases in (1) hospital staffing (with training in trauma-informed care), (2) non-secondary health service capacity, (3) expansion of mental health assessment capacity and (4) re-engagement of at least 45% of individuals lost to services. Reduction in the number of available psychiatric beds by 15% had no substantial impact on the incidence of attempted suicide and suicide over the forecast period. Conclusion: This study suggests that more than one-quarter of suicides and attempted suicides in the Greater Western Sydney population catchment could potentially be averted with a combination of increases to hospital staffing and non-secondary (non-acute) mental health care. Reductions in tertiary care services (e.g. psychiatric hospital beds) in combination with these increases would not adversely affect subsequent incidence of suicidal behaviour. [ABSTRACT FROM AUTHOR]

The persistent prevalence of childhood overweight and obesity raises significant concerns about the impact on health, society and the economy. Responding to a target announced in September 2015 by the New South Wales (Australia) Premier to reduce childhood overweight and obesity by five percentage points by 2025, a system dynamics model was developed to support Government and stakeholders responsible for meeting the target. A participatory model building process, drawing cross‐sectorial expertise, was undertaken to estimate the individual and combined impact of interventions on meeting the target. The model demonstrated that it is theoretically possible to meet the target by implementing a comprehensive combination of policies and programmes. When limited to existing and enhanced population health interventions, the modelled result did not reach the target. The project provides an example of how participatory simulation modelling can combine a broad range of interventions together into likely scenarios and usefully inform government decision‐making. © 2018 The authors. Systems Research and Behavioral Science published by International Federation for Systems Research and John Wiley & Sons Ltd [ABSTRACT FROM AUTHOR]

When modelling and simulating healthcare related processes, free-text data is often the only possible source of information. This data may contain vocabulary variations such as mistyped, misspelled and/or abbreviated words. This paper describes a semi-automated approach to free-text normalisation based on a combination of commonly used techniques and local expertise of medical oncology nurses. The approach emphasises the effectiveness of the vocabulary creation process through an interactive software application. When local knowledge is successfully captured, normalisation of large data sets can be done very rapidly with a high accuracy rate achieved. Furthermore, the techniques for localised normalisation can have significant benefits to free-text parsing accuracy when data is aggregated from multiple sites (hospitals). This research may lead to increased understanding of issues associated with chemotherapy related free-text data which in turn may impact patient treatment safety. [ABSTRACT FROM AUTHOR]

This article focuses on a framework that will investigate the integration of two disparate methodologies: patient journey modelling and visual multi-agent simulation, and its impact on the speed and quality of knowledge translation to healthcare stakeholders. Literature describes patient journey modelling and visual simulation as discrete activities. This paper suggests that their combination and their impact on translating knowledge to practitioners are greater than the sum of the two technologies. The test-bed is ambulatory care and the goal is to determine if this approach can improve health services delivery, workflow, and patient outcomes and satisfaction. The multidisciplinary research team is comprised of expertise in patient journey modelling, simulation, and knowledge translation. [ABSTRACT FROM AUTHOR]

This work describes how the use of a visual patient journey modeling tool significantly improved the engagement of clinical staff and their group understanding of the complex IT concepts associated with event-driven computer simulation. Such group understanding and engagement is acknowledged as critical to the successful implementation of a wide range of quality improvement initiatives [1, 2]. The specific example described in this paper relates to the improved utilisation of chemotherapy unit resources. [ABSTRACT FROM AUTHOR]