Evidence map›Paper›PMID 39439990›Full record

ArticleJournal of the Royal Society of New Zealand2023

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

Daniel Wilson, Frith Tweedie, Juliet Rumball-Smith, Kevin Ross, Alex Kazemi, Vince Galvin, Gillian Dobbie, Tim Dare, Pieta Brown, Judy Blakey

Abstract read
In one paragraph

Article in Journal of the Royal Society of New Zealand, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Debiasing large language models: research opportunities.Journal of the Royal Society of New Zealand · 2025
    Article
  2. Article
  3. Artificial Intelligence in New Zealand: applications and innovation.Journal of the Royal Society of New Zealand · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Daniel WilsonSchool of Computer Science, Waipapa Taumata Rau/University of Auckland, Auckland, New Zealand.ORCID https://orcid.org/0000-0002-3593-6234
Frith TweedieAuror Ltd., Auckland, New Zealand.
Juliet Rumball-SmithPrecision Driven Health, Auckland, New Zealand.
Kevin RossPrecision Driven Health, Auckland, New Zealand.
Alex KazemiCritical Care Complex, Middlemore Hospital, Auckland, New Zealand.
Vince GalvinStatistics New Zealand, Wellington, New Zealand.
Gillian DobbieSchool of Computer Science, Waipapa Taumata Rau/University of Auckland, Auckland, New Zealand.
Tim DareDepartment of Philosophy, Waipapa Taumata Rau/University of Auckland, Auckland, New Zealand.
Pieta BrownOrion Health, Auckland, New Zealand.
Judy BlakeyPrecision Driven Health Independent Advisory Group, Auckland, New Zealand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aotearoa New Zealand's response to the COVID-19 pandemic has included the use of algorithms that could aid decision making. Te Pokapū Hātepe o Aotearoa, 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. A critical aspect of the Hub was the formation of an appropriate governance group to ensure that algorithms being deployed underwent cross-disciplinary scrutiny prior to being made available for quick and safe implementation. This framework necessarily canvassed a broad range of perspectives, including from data science, clinical, Māori, consumer, ethical, public health, privacy, legal and governmental perspectives. To our knowledge, this is the first implementation of national algorithm governance of this type, building upon broad local and global discussion of guidelines in recent years. This paper describes the experiences and lessons learned through this process from the perspective of 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.

Indexed as

Algorithmsartificial intelligenceclinical decision supportCOVID-19governance frameworkhealthcare data or healthcare algorithmsprediction modelsrisk models

Identifiers

PMID39439990
PMCPMC11459790

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.