Te Pū Waiora
This collection of stories reflects Māori leadership and engagement in health data science.
Pū – origin, source, cause, root, base, foundation, heart, centre, precise
Waiora – health and soundness
Learnings from COVID-19 to deliver more equitable solutions
What lessons have we learnt from the COVID-19 pandemic around building data capability? How can these lessons be applied to deliver more equitable health outcomes for New Zealanders? These are some of a number of questions addressed in an article recently published by the Journal of the Royal Society of New Zealand.
Supporting Māori into a career in health data science
The field of health data science is growing rapidly in Aotearoa New Zealand. But while demand for data scientists is high, there’s a shortage of Māori data scientists coming through the training pipeline.
Multiple imputation for better data
We explore a method called multiple imputation which can be used to address a common problem in healthcare – missing data. While we work towards improving the quality of data collected, multiple imputation is an interim step in helping to reduce bias in our data, models and analyses, helping to contribute towards a more equitable health system.
How to counter bias when creating a model
By continuing to strive to mitigate bias wherever possible, models can continue to develop and serve as a useful tool in building a more equitable health system, and advancing healthcare for all.
Hack Aotearoa 2023 – mahitahi in action
Hack Aotearoa 2023 will take place in Auckland from 17-19 March 2023 and will cover topics spanning Aotearoa New Zealand’s health reforms and innovations in models of care through digital health, through to Māori data and digital innovation and Pacific data insights.
Supporting Māori researcher development
Māori have a higher incidence of health needs that haven’t been met by existing programmes, but a partnership between Te Whānau o Waipareira and Precision Driven Health (PDH) is aiming to harness the power of data to play a part in addressing these needs.
Co-designing with Te Whānau o Waipareira
When it comes to supporting whānau in West Auckland, Te Whānau o Waipareira’s frontline kaimahi (workers) know what works better than anyone.
Kāhu AI and MoleMap partner with Te Hau Ora O Ngāpuhi
Skin cancer is one of the more common forms of cancer worldwide. It’s particularly prevalent in New Zealand, though, which has the highest rate in the world with nearly 400 New Zealanders dying each year from skin cancer.
Prioritising referrals by need
Heart disease is the leading cause of preventable mortality in Aotearoa New Zealand. It’s also a condition that disproportionately affects certain groups, such as Māori and Pacific peoples.
Incorporating lived experience into research
Putting data and care decisions into the hands of consumers has an empowering effect beyond better decision making. This particularly applies for our disabled citizens.
Whanāu Like Us
Technology has come a long way since Te Whānau o Waipareira began supporting positive life changes for whānau (family) in West Auckland.
Enabling personalised self-care in Te Tai Tokerau
Self-care is a great way to empower patients to take control of their health – and soon patients in Te Tai Tokerau (Far North) will have an opportunity to put self-care to the test in their own homes in an attempt to prevent long-term health conditions.
De-Identifying Data Balancing Utility vs Privacy
In Aotearoa New Zealand, organisations have clear obligations when collecting, storing, using and disclosing personal information. Privacy is paramount – and that’s certainly the case for organisations in the health sector.
Data Analysis Saves Lives – HOPE (AAA)
Māori are nearly three-times more likely to have Abdominal Aortic Aneurysms (AAA) – a condition described as “the silent killer” – than non-Māori.
nzRISK: Understanding Surgical Risk based on New Zealand’s Unique Population
Deciding whether to have major surgery can be difficult for patients and clinicians alike. The benefits and risks need to be weighed up through a shared decision-making process. This is particularly true for high-risk populations.