Lessons learned from developing a COVID-19 algorithm governance framework

Lessons learned from developing a COVID-19 algorithm governance framework

The use of algorithms to potentially aid decision making was part of Aotearoa New Zealand’s response to the COVID-19 pandemic, with Te Pokapū Hātepe o Aotearoa, the New Zealand Algorithm Hub housing many of these algorithms.

Now, an open access paper, Lessons learned from developing a COVID-19 algorithm governance framework in Aotearoa New Zealand, has been published by the Journal of the Royal Society of New Zealand, capturing the lessons learned by the New Zealand Algorithm Hub’s governance group.

The New Zealand Algorithm Hub was established to evaluate and host COVID-19 related models and algorithms, and provide a central and secure infrastructure to support the country’s pandemic response.

Algorithms are decision-making tools, which manage assets such as data, and create opportunities and risks.

The opportunities are realised through the outcomes that algorithms can create for populations, but the risks – from issues with data sources to algorithm training – can undo this. Good governance is key to mitigating these risks.

To ensure good governance was in place for the New Zealand Algorithm Hub, a multi-disciplinary governance group was formed. This allowed for the algorithms being deployed to be properly scrutinised before being made available for quick and safe implementation. 

The group was comprised of:

  • Dr Daniel Wilson – Māori, data science (Waipapa Taumata Rau/University of Auckland)
  • Frith Tweedie – Legal, privacy (Auror)
  • Dr Alex Kazemi – Clinical (Critical Care Complex, Middlemore Hospital)
  • Professor Gillian Dobbie – Data science (Waipapa Taumata Rau/University of Auckland)
  • Pieta Brown – Data science (Orion Health)
  • Dr Judy Blakey – Health Consumer
  • Dr Juliet Rumball-Smith – Public health, government (Manatū Hauora, Ministry of Health)
  • Dr Kevin Ross – Data Science (Precision Driven Health)
  • Professor Tim Dare – Ethics (Waipapa Taumata Rau/University of Auckland)
  • Vince Galvin – Government, data science (Statistics New Zealand)

The governance group allowed for a broad range of perspectives to be considered, including from data science, clinical, Māori, consumer, ethical, public health, privacy, legal and governmental perspectives.

The open access paper, which you can read here describes the experiences and lessons learned by governance group members, emphasising the role of robust governance processes in building a high-trust platform that enables rapid translation of algorithms from research to practice.

Governance group member and paper co-author Alex Kazemi of Middlemore Hospital’s Critical Care Complex says: “It was a privilege to work with a broad and very knowledgeable group of people from diverse backgrounds to record what we learned on the governance of a pandemic algorithm platform for Aotearoa New Zealand.

“Through discussion, we learned a lot about the governance of healthcare algorithms for a complex and rapidly changing environment, whilst looking at the process of which algorithms to include and how they might or might not work.”

“As far as the group is aware, this is the first implementation of national algorithm governance of this type, building upon broad local and global discussion of guidelines in recent years. Our hope is that this paper will be of interest to the medical community, and of use to anyone facing similar challenges in the future who can hopefully learn from our experiences.”

“As far as the group is aware, this is the first implementation of national algorithm governance of this type, building upon broad local and global discussion of guidelines in recent years. Our hope is that this paper will be of interest to the medical community, and of use to anyone facing similar challenges in the future who can hopefully learn from our experiences.”

You can read Lessons learned from developing a COVID-19 algorithm governance framework in Aotearoa New Zealand here.