New Zealand, an island country of five million people in the Pacific, presents a globally-relevant case study in the application of robust, ethical data science for healthcare decision-making.
With a strong data-enabled health system, the population has successfully navigated several challenging aspects of both the pandemic response of 2020 and wider health data science advancements.
New Zealand’s diverse population comprises a majority of European descent, but major cohorts of the indigenous Māori population, other Pacific Islanders and Asian immigrants all makeup significant numbers. Further, these groups tend to be over-represented in negative health statistics, with an equity gap that has generally increased with advances in health technology.
Adopting models from international studies presents a challenge for a society with such an emphasis on reducing the equity gap. International research has historically included many more people of European origin, meaning that advances in medical practice are more likely to benefit those groups. As more data science technologies are developed, including machine learning and artificial intelligence, the potential to exacerbate rather than reduce inequities is significant.
New Zealand has invested in health data science collaborations, particularly through a public-private partnership called Precision Driven Health (PDH). PDH puts clinicians, data scientists and software developers together to develop new models and tools to translate data into better decisions. Some of the technology and governance models developed through these collaborations have been critical in supporting the national response to the COVID-19 pandemic.
When the New Zealand government, led by Prime Minister Jacinda Ardern, called upon the research community to monitor and model the spread of COVID-19, a new collaboration emerged. PDH data scientists from Orion Health supported academics from Te Pūnaha Matatini, a university-led center of research excellence, in developing, automating and communicating the findings of modeling initiatives.
This led to a world-first national platform, called the New Zealand Algorithm Hub. The hub hosts models that have been reviewed for appropriate use in the response to COVID-19 and makes them freely available for decision-makers to use. Models range from pandemic spread models to risk of hospitalization and mortality, as well as predictive and scheduling models utilized to help reduce backlogs created during the initial lockdown.
One of the key challenges in delivering a platform of this nature is the governance of the decisions around which algorithms to deploy. Having had very few COVID-19 cases in New Zealand meant that it was not straightforward to assess whether an algorithm might be suitable for this unique population.
A governance group was formed with stakeholders from consumer, legal, Māori, clinical, ethical and data science expertise, among others. This group developed a robust process to assess suitability, inviting the community to describe how algorithms were intended to be used, how they potentially could be misused or whether there might be other unintended consequences to manage.
The governance group placed a strong emphasis on the potential for bias to creep in. If historical records favor some people, how do we avoid automating these? A careful review was necessary of the data that contributed to model development; any known issues relating to access or data quality differences between different groups; and what assumptions were to be made when the model would indeed be deployed for a group that had never been part of any control trial.
On one level, New Zealand’s COVID-19 response reflects a set of national values where the vulnerable have been protected; all of society has had to sacrifice for a benefit which is disproportionately beneficial to older and otherwise vulnerable citizens. The sense of national achievement in being able to live freely within tightly restricted borders has meant that it is important to protect those gains and avoid complacency.
The algorithm hub, with validated models and secure governance, is an example of positive recognition of bias motivating the New Zealand data science community to act to eliminate not just a virus, but ultimately a long-term equity gap in health outcomes for people.