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New Zealand, an island country of fivemillionpeople in the Pacific, presents aglobally-relevantcase study inthe 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 Zealands diverse population comprises a majority ofEuropean descent, but major cohorts of the indigenous Mori 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 healthtechnology.

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Adopting models from international studies presentsa challenge for a societywith 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.

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New Zealand hasinvestedin health data science collaborations,particularlythrougha public-private partnership called Precision Driven Health (PDH). PDH puts clinicians, data scientists and software developers together to develop new models and toolsto translate data into better decisions.Some of the technology and governance models developedthrough these collaborations havebeencritical 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 fromTePnahaMatatini, auniversity-ledcenterofresearchexcellence, in developing, automating andcommunicatingthefindings of modeling initiatives.

This led to a world-firstnational platform, called the New Zealand Algorithm Hub. The hub hosts models that have been reviewed for appropriate use in the response to COVID-19 andmakes them freely available fordecision-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 aroundwhichalgorithms to deploy. Having had very few COVID-19 cases in New Zealandmeant that it was not straightforward to assess whether analgorithmmight be suitable for this unique population.

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A governance group was formed with stakeholders from consumer, legal, Mori, clinical, ethical and data science expertise, amongothers. This group developed a robust process to assesssuitability, inviting the community to describe howalgorithmswere 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, howdo we avoid automating these? A careful review was necessary of the data thatcontributedto model development; any knownissues 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 Zealands COVID-19 response reflects a set of national values where the vulnerable have been protected;all of society has had to sacrificefora benefit which is disproportionatelybeneficialto older and otherwise vulnerable citizens. The sense of national achievement in being able tolive freely within tightly restricted borders has meant that it is important to protect those gains and avoidcomplacency.

The algorithm hub, with validated models and secure governance, is an example ofpositive recognition of bias motivating the New Zealand data science community to act to eliminate not just a virus, butultimately a long-term equity gap in health outcomes for people.

Kevin Ross, Ph.D., is director of research at Orion Health and CEO of Precision Driven Health.

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Industry VoicesBuilding ethical algorithms to confront biases: Lessons from Aotearoa New Zealand - FierceHealthcare