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An example of governance for AI in health services from Aotearoa New Zealand.

ABSTRACT: Artificial Intelligence (AI) is undergoing rapid development, meaning that potential risks in application are not able to be fully understood. Multiple international principles and guidance documents have been published to guide the implementation of AI tools in various industries, including healthcare practice. In Aotearoa New Zealand (NZ) we recognised that the challenge went beyond simply adapting existing risk frameworks and governance guidance to our specific health service context and population. We also deemed prioritising the voice of Māori (the indigenous people of Aotearoa NZ) a necessary aspect of honouring Te Tiriti (the Treaty of Waitangi), as well as prioritising the needs of healthcare service users and their families. Here we report on the development and establishment of comprehensive and effective governance over the development and implementation of AI tools within a health service in Aotearoa NZ. The implementation of the framework in practice includes testing with real-world proposals and ongoing iteration and refinement of our processes.

SUBMITTER: Whittaker R

PROVIDER: S-EPMC10474148 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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An example of governance for AI in health services from Aotearoa New Zealand.

Whittaker R R Dobson R R Jin C K CK Style R R Jayathissa P P Hiini K K Ross K K Kawamura K K Muir P P

NPJ digital medicine 20230901 1

Artificial Intelligence (AI) is undergoing rapid development, meaning that potential risks in application are not able to be fully understood. Multiple international principles and guidance documents have been published to guide the implementation of AI tools in various industries, including healthcare practice. In Aotearoa New Zealand (NZ) we recognised that the challenge went beyond simply adapting existing risk frameworks and governance guidance to our specific health service context and popu ...[more]

PMID: 37658119

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Project description:IntroductionIn Aotearoa New Zealand, Māori and Pacific People experience worse health outcomes compared with other New Zealanders. No population-based eye health survey has been conducted, and eye health services do not generate routine monitoring reports, so the extent of eye health inequality is unknown. This information is required to plan equitable eye health services. In this scoping review, we aimed to summarise the nature and extent of the evidence reporting vision impairment, its main causes and access to eye health services by ethnicity in New Zealand.MethodsThis scoping review was reported according to Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. An information specialist conducted a search on MEDLINE and Embase databases in October 2022. Included studies reported outcomes among any population group resident in New Zealand or attendees at New Zealand health facilities. Data screening, full-text review and data extraction were performed independently by two authors. We summarised the characteristics of studies and outcomes, and the results were synthesised narratively.ResultsOur search identified 2711 reports, of which 53 (from 47 studies) were included. We mapped 72 outcomes, many of which were access-related (n=32), published since 2000 (n=28) and largely focused on diabetic retinopathy (n=21) or cataract (n=13) in facility-based settings (n=18). Over two-thirds of reported outcomes were disaggregated by at least two ethnicities. When outcomes were disaggregated by ethnicity, Māori and Pacific People were consistently included, and experienced worse access and outcomes compared with other New Zealanders.ConclusionThe findings of this review highlight the presence of ethnic disparity in access to diabetic retinopathy and cataract services. Closing the evidence gap identified for refractive error, glaucoma and macular degeneration service coverage could be a priority for future research. Furthermore, future research can be strengthened to enable consistent monitoring of eye health service coverage over time.

Project description:The marine-biodiversity assessment of New Zealand (Aotearoa as known to Māori) is confined to the 200 nautical-mile boundary of the Exclusive Economic Zone, which, at 4.2 million km(2), is one of the largest in the world. It spans 30 degrees of latitude and includes a high diversity of seafloor relief, including a trench 10 km deep. Much of this region remains unexplored biologically, especially the 50% of the EEZ deeper than 2,000 m. Knowledge of the marine biota is based on more than 200 years of marine exploration in the region. The major oceanographic data repository is the National Institute of Water and Atmospheric Research (NIWA), which is involved in several Census of Marine Life field projects and is the location of the Southwestern Pacific Regional OBIS Node; NIWA is also data manager and custodian for fisheries research data owned by the Ministry of Fisheries. Related data sources cover alien species, environmental measures, and historical information. Museum collections in New Zealand hold more than 800,000 registered lots representing several million specimens. During the past decade, 220 taxonomic specialists (85 marine) from 18 countries have been engaged in a project to review New Zealand's entire biodiversity. The above-mentioned marine information sources, published literature, and reports were scrutinized to give the results summarized here for the first time (current to 2010), including data on endemism and invasive species. There are 17,135 living species in the EEZ. This diversity includes 4,315 known undescribed species in collections. Species diversity for the most intensively studied phylum-level taxa (Porifera, Cnidaria, Mollusca, Brachiopoda, Bryozoa, Kinorhyncha, Echinodermata, Chordata) is more or less equivalent to that in the ERMS (European Register of Marine Species) region, which is 5.5 times larger in area than the New Zealand EEZ. The implication is that, when all other New Zealand phyla are equally well studied, total marine diversity in the EEZ may be expected to equal that in the ERMS region. This equivalence invites testable hypotheses to explain it. There are 177 naturalized alien species in New Zealand coastal waters, mostly in ports and harbours. Marine-taxonomic expertise in New Zealand covers a broad number of taxa but is, proportionately, at or near its lowest level since the Second World War. Nevertheless, collections are well supported by funding and are continually added to. Threats and protection measures concerning New Zealand's marine biodiversity are commented on, along with potential and priorities for future research.

Project description:For the past 20 years, the New Zealand Deprivation Index (NZDep) has been the universal measure of area-based social circumstances for New Zealand (NZ) and often the key social determinant used in population health and social research. This paper presents the first theoretical and methodological shift in the measurement of area deprivation in New Zealand since the 1990s and describes the development of the New Zealand Index of Multiple Deprivation (IMD). We briefly describe the development of Data Zones, an intermediary geographical scale, before outlining the development of the New Zealand Index of Multiple Deprivation (IMD), which uses routine datasets and methods comparable to current international deprivation indices. We identified 28 indicators of deprivation from national health, social development, taxation, education, police databases, geospatial data providers and the 2013 Census, all of which represented seven Domains of deprivation: Employment; Income; Crime; Housing; Health; Education; and Geographical Access. The IMD is the combination of these seven Domains. The Domains may be used individually or in combination, to explore the geography of deprivation and its association with a given health or social outcome. Geographic variations in the distribution of the IMD and its Domains were found among the District Health Boards in NZ, suggesting that factors underpinning overall deprivation are inconsistent across the country. With the exception of the Access Domain, the IMD and its Domains were statistically and moderately-to-strongly associated with both smoking rates and household poverty. The IMD provides a more nuanced view of area deprivation circumstances in Aotearoa NZ. Our vision is for the IMD and the Data Zones to be widely used to inform research, policy and resource allocation projects, providing a better measurement of area deprivation in NZ, improved outcomes for Māori, and a more consistent approach to reporting and monitoring the social climate of NZ.

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An example of governance for AI in health services from Aotearoa New Zealand.

Publications

An example of governance for AI in health services from Aotearoa New Zealand.

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