New Zealand Prime Minister Jacinda Ardern’s ambitious goals to cut child poverty by half and significantly improve child wellbeing offer a powerful opportunity to apply new data-led and intelligent computer system thinking to making New Zealand “the best place in the world to be a child.”
A central focus has already been created through Oranga Tamariki — the Ministry for Children. As NZ ministries develop their portfolio initiatives to support the PM’s goals, there is a real opportunity to employ different strategic approaches, built around a modern, integrated, intelligent approach that empowers social workers, providers, communities and public agencies.
For too long social workers have flown blind, with their knowledge of at-risk kids and the history and insight of decades of case notes and social agency interaction buried in filing boxes. Years of experience and insight lost.
At the same time policy makers have made advances in discrete areas such as education, housing and health, but have struggled to develop an effective operational model focussed on the overall impact of intervention. Much of the data that gives real insights about the root causes of child poverty remains untapped.
Around the world, there have been rapid advances in human services design and delivery, including smart case management, sophisticated risk assessment and notification systems, and data modelling and analysis.
New Zealand has been a global leader in service design. We have come to a point where governments can now meaningfully deploy these strategies as part of a holistic approach to drive an integrated and evidence-based approach to human services and child wellbeing.
At the heart of these new approaches to human service design is the use of intelligent computing systems using advanced analytics, powerful algorithms and self learning applications. These use artificial intelligence applications to capture at scale key data and information and to predict what interventions will be most effective.
In Florida for example, there have been major reductions in juvenile detention and delinquency by better understanding the risk profile of at-risk youth and tailoring interventions accordingly.
In Melbourne, the use of AI or cognitive computing is enabling doctors to better predict what treatments will work best for melanoma patients. AI applications can digest learnings from years of case files and make recommendations as to the best treatment.
This approach can be deployed to capture insights and learnings from multiple disciplines, drawn from children at risk, their families, caseworkers, all the way up to central policy makers.
It also enables a complete view of each child at risk, what their specific needs and vulnerabilities are, and the history of contacts and interventions.