Cast your mind back to the once ubiquitous paper-based roadmaps (‘Refidex’; “Gregorys or Melways). Every car had one. A book of several hundred maps that represented the only way to figure out how to get from A to B, and a source of inordinate amounts of frustration, particularly if you were trying to get somewhere quickly. As the map on one page ended, you were prompted to go to page 53 to continue following a particular road, then back to page 25, then on to page 32, only to find that the road your finger was following across these pieces of the map wasn’t going to take you where you needed to go! Thank goodness for the rise of the digital street map and satellite navigation systems that now harness continuous streams of data to calculate and re-assess in real time the most efficient routes to your destination, without having to resort to the vagaries of our cognitive capacities.
The engineering, business, and finance sectors know only too well the value of advanced computing and how to harness it to get efficiently to their destination. When product viability and profits are at stake, complexity and uncertainty need to be managed, and outcomes optimised. It goes without saying that business performance is significantly enhanced by being able to weigh up the different route options and successfully navigate potential roadblocks. Complex systems modelling and simulation is in essence the satellite navigation system of these sectors.
Originating in the 1950’s at MIT’s Sloan School of Management, and routinely practiced by academics and consultants, complex systems modelling has long been used to analyse and understand complex business dynamics — namely, how the structure and operation of a business, how customer, competitor and supplier behaviour, and how government policies all interact to impact business performance and profits.
Systems modelling and simulation is deployed to facilitate scenario testing to inform strategy. For example, modelling is used to set achievable short- and longer-term business goals, determine which products and services will deliver the best returns on investment and in which markets, help establish and revisit the basis for product price, service and quality, improve operational efficiency and reduce supply chain instability, and underpin the evolution of a company’s roadmap in response to economic, environmental or behavioural perturbations to ensure that the destination is still reached. The business, engineering, and finance sectors recognise the inadequacies of relying on cognitive vagaries and leaps of faith in setting strategic directions and achieving their agenda.
Historically, the design of public health and social policy in Australia is largely devoid of engagement with these sophisticated complex-systems modelling tools. This remains so despite the scale of spending on health being in excess of $180 billion per year, representing 10% of GDP (2016-2017). Rather than engaging with these modelling tools, health system reform, strategy design, action plans, and operational decision-making continue to rely on antiquated methods that analyse and reveal only individual pieces of the roadmap. Lack of engagement in modelling that can bring the pieces of the puzzle together to help us look forward to anticipate and mitigate adverse trajectories confines us to delayed actions, wrong turns, and the lack of agility required to be responsive to a rapidly changing world. The design and subsequent implementation of ‘strategic’ actions plans is then endless trial and error, with ongoing evaluation based largely on contract compliance rather than continued learning.
So why have we not evolved from the Refidex approach? It’s not because the problems are any less complex, nor is there less at stake. It is difficult to believe that it is because our business-orientated government isn’t aware that these tools exist and the value they deliver. Perhaps it represents a failure on the part of the public health academic community, where epistemological entrenchment has precluded us from teaching or applying the use of more sophisticated analytic methods. Perhaps it is advocacy’s over-reliance on a moral argument (‘love will be enough’) to motivate government investment to improve population health and wellbeing. Whatever the reason, the pervading culture of ‘comprehensive’ action plans that deliver laundry lists of possible responses that are never adequately funded, and the absence of any real accountability for a lack of effective outcomes. Currently, this all plays out in frantic levels of hamster wheel activity and daily press conferences with linked ‘announceables’.
The response to the COVID19 crisis represented a momentary glimmer of hope. This was when the Australian government, under pressure to avoid being directly responsible for large numbers of COVID-19 deaths, engaged seriously with complex systems modelling and made a significant investment in collaborative efforts of the Doherty and Burnett Institutes to rapidly deploy their systems modelling expertise to look forward, understand the likely scale of the problem, and inform decision-making for rapid action. Complex systems modelling provided the government with a critical tool for managing complexity and weighing alternative responses in the midst of the confusion of an evolving crisis, successfully avoiding tens of thousands of unnecessary deaths of Australians.
But it seems that this is where government engagement with complex systems modelling to inform decision-making has ended. Despite compelling early indicators of the unprecedented impact that COVID-19 and subsequent recession will have on population-based mental health outcomes, mental health services, and suicide risk, the door to any serious or sustained investment in the application of systems modelling to inform proactive national and regional responses remains shut.
While the National Mental Health Pandemic Response Plan recognises the role that data and unspecified ‘modelling’ may play, it simply lists 10 priorities, 3 enabling factors, 7 principles and 77 actions. There are no indicators of impact or accountability. There is no timeline, implementation plan, funding commitment or structural reform and no estimates of the scale of impact these actions are likely to have. It appears that the only desired outcome was that all jurisdictions could express support.
Having seen the immediate benefits of rapid deployment of complex systems modelling to help secure real impact on COVID-19, one has to ask why, for mental health in Australia, the antiquated business as usual approach to decision-making that has failed so many for so long (and the subject of a royal commission) is being stubbornly retained.
Jo-An Atkinson, PhD, is Head of Systems Modelling & Simulation, Brain and Mind Centre, The University of Sydney and Computer Simulation and Advanced Research Technologies, Sydney, Australia; Ian B. Hickie, MD is from the Brain and Mind Centre, University of Sydney, Sydney, Australia; Kenny Lawson collaborates with the Brain and Mind Centre, University of Sydney, and Computer Simulation and Advanced Research Technologies, Sydney, Australia.