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Using routinely collected health data to improve health outcomes in older people

Year:
2020
Duration:
42 months
Approved budget:
$352,074.40
Researchers:
Associate Professor Sarah Cullum
,
Dr Daniel Wilson
,
Associate Professor Rita Krishnamurthi
,
Dr 'Etuini Ma'u
,
Professor Gillian Dobbie
,
Dr Gary Cheung
,
Assistant Professor Claudia Rivera-Rodriguez
,
Dr John Hopkins
,
Dr Yu-Min Lin
,
Ms Rosie Whittington
Health issue:
Neurological (CNS)
Proposal type:
Project
Lay summary
Dementia in New Zealand (NZ) is predicted to triple by 2050. Māori and Pasifika may be at higher risk of dementia. There has never been a national NZ dementia prevalence study so we need to explore alternative sources of data that may provide us with proxy measures of the extent and impact of dementia in diverse NZ communities. The aim of our proposed research is to explore the possibility of using artificial intelligence with routinely collected health and social care data to identify risk factors that, if targeted, will make the greatest difference to the health of older people, particularly in Māori and Pasifika. We will use machine learning approaches to identify people at high risk of dementia and data linkage to monitor health and social care outcomes, equity and costs. We will address the address ethical implications of the research and its findings with stakeholders to provide consensus guidelines.