Category: Projects

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.  In Aotearoa New Zealand, Māori experience different outcomes compared to the rest of the population across nearly all areas of …

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.  To address this, an innovative data analysis project helped identify and save patients with AAA, demonstrating the potential of using data and machine learning to prioritise screening for those most at risk. …

cute girl sitting in between her grandparents

Patients Like This

It is important to provide patients with reliable, accurate and personalised information about their health conditions, to engage them in their health. Often, this is done by doctors, based on their textbook knowledge and prior clinical experience in communicating with and managing similar patients under their direct care. Whilst this usually works well for experienced …

A Deep Learning Platform for GP Referral Triage

Countering bias in the health system Heart disease is the leading cause of preventable mortality in Aotearoa, New Zealand. It is a disease that disproportionately affects certain groups such as Māori – who have higher rate of ischaemic heart disease (or coronary heart disease) and stroke but have lower access to health care. Intervention must …

Greater Engagement of Mental Wellbeing Solutions Through Personalisation

Mental health problems are a worldwide pandemic. Over 50-80% of New Zealanders will experience mental distress in their lifetime, costing an estimated $12B or 5% of GDP annually. While evidence indicates early detection and intervention is the key to improving outcomes, there is often a considerable delay from the onset of symptoms to the time …

Telehealth as a Model for Health Care Delivery for Underserved Populations

In New Zealand, Telehealth provided health care during the COVID-19 lockdown, but its use has declined back to pre-COVID levels. Despite the fact that Telehealth is equivalent to in-person care, the lack of guidelines for design and implementation may result in risks to patient safety. Telehealth has not been widely adopted in New Zealand and …

Application of deep learning techniques in a de-identification system

Summer of Research project by Yicheng Shi, University of Auckland, supervised by Quentin Thurier (Orion Health). Clinical data, collected by healthcare providers when treating patients, is incredibly valuable for medical research – but good data is hard to get. This information can help researchers improve healthcare. For example, by studying a patient’s medical history, researchers …

Using modelling to improve efficiency at Dargaville Medical Centre

Summer of Research project by Lucy McSweeney, University of Auckland, supervised by Dr Michael O’Sullivan Jnr (University of Auckland) Creating a model of how patients and staff use and provide services at a health clinic can help that clinic operate more efficiently. Dargaville Medical Centre (DMC) is a large General Practice in Northland with 12 …

Financial Evaluation of EDDI (Early Detection Decision Information)

Summer of Research project by James Zhang, University of Auckland, supervised by Dr Michael O’Sullivan Jnr (University of Auckland). New software could flag issues early during operations, helping surgeons and anaesthetists intervene before serious complications arise. We need to find out how much of a difference this software makes, and whether it will prevent complications …

Interpretable image-based machine learning models in healthcare

Summer of Research project by Harper Shen, University of Auckland, supervised by Quentin Thurier (Orion Health) and Dr Yun Sing Koh (University of Auckland). Neural networks can be great at solving problems, but they sometimes give wrong answers. Would you trust an algorithm that got life-saving information right 80% of the time? It is easier …