- Projects and programs initiated by the research community, our partners, or other organisations
- Scholarship and fellowships which allow students and postdoctoral researchers to explore precision health research through the partnership
- Events to bring together the health data science community
- Publication of research, opinions and guidelines
Check below for a full list of all our projects.
Student Research Projects
Precision Driven Health funds several types of student research projects. Details of the projects and their successes are shown below.
Our previous two-year research project on Surgical Outcome Calculators examined surgical outcomes in NZ patients to help produce a surgical risk prediction tool: nzRISK, which produces a risk score for that patient at one month, one year and two years after surgery. NZ clinicians are now using the tool, which has produced tremendous interest and value.
The primary goal of this project is to provide a decision support system for triaging referrals to make this task more efficient.
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.
Triaging GP referrals is a time-consuming task for doctors in New Zealand. It is common for doctors to spend more than ten hours per week on triaging electronic referrals to various risk categories or direct to investigation tests.
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.
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.
In New Zealand, Telehealth provided health care during the COVID-19 lockdown, but its use has declined back to pre-COVID levels.
Medical student Nick James has developed a neural network model that can find identifying details in patient records, so the details can be removed making the records available for research purposes.
One method of detecting Melanoma is through the usage of machine learning on medical images.
Machine readable clinical guidelines and pathways There has recently been rapid growth in the use of clinical guidelines and pathways for the assessment, diagnosis and management of medical conditions. Summer of Research Project by David Bassett, supervised by Dr....
To better allocate resources and plan for surgeries, Counties Manukau District Health Board (CMDHB) require data-driven analysis of their surgical scheduling. This will help them to understand the effect of re-allocating surgical sessions between surgical specialties.
Protecting patient confidentiality while using medical data from electronic health records (EHRs) for research is crucial. De-identification of EHR data provides the opportunity to use the data for research without the risk of breaching patient privacy, and avoids the need for individual patient consent.
Auckland Council has a range of geospatial and infrastructure data available, which could potentially be linked with health data to provide insights. The purpose of this project was to investigate the data held by the council and describe how it could be used to inform healthcare.
Familial Hypercholesterolaemia (FH) is a genetic condition which causes an increased risk of cardiovascular disease and premature death (3-4 times the risk of early death). It is estimated to affect 1:300 people in the general population of New Zealand.
Atrial fibrillation (AF) is the most common sustained heart rhythm disturbance. At present, 25% of the New Zealand population who are 40 years old or more will experience AF in their lifetime. AF increases morbidity and mortality.
Computer models can help design specialist health clinics, giving patients the better care and using clinic resources efficiently. Ellen Gibbs created a simulation for a clinic dedicated to treating lymphoedema.
As demand for doctors increases, patients may be able to decrease pressure on the health system and save themselves money by getting a preliminary diagnosis from an automatic question-and-answer system.
Clinical data, collected by healthcare providers when treating patients, is incredibly valuable for medical research – but good data is hard to get.
Creating a model of how patients and staff use and provide services at a health clinic can help that clinic operate more efficiently.
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 and save money.
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?
Efficient communication between healthcare professionals about a patient’s health is crucial to delivering the best possible care.
It takes time and money to train specialist medical algorithms, and medical-specific data is hard to obtain for research, but the payoff may well be worth it.
Automatically identifying drug names, dosages and effects in health records could help researchers find relationships between certain medications and negative effects on patients.
Vensa has embarked on this project to enable the analysis of laboratory result patterns that aims for faster and safer dissemination of results to patients.
Heart disease is the leading cause of preventable mortality in Aotearoa, New Zealand. It is a disease which disproportionately affects certain groups such as Māori – who have higher rates of ischaemic heart disease (or coronary heart disease) and stroke but have lower access to health care.
The “black box” metaphor is commonly used to refer to the lack of understanding of how modern Machine Learning (ML) systems make decisions.
Medical professionals analysing electronic health data often have to deal with data sets that are incomplete. If this data is not handled correctly it can result in negative outcomes such as bias, complications and ultimately invalid conclusions.
As a small country in the South Pacific, our uniqueness is often overlooked by health science researchers.
The idea that a patient’s Electronic Health Record (EHR) has a secondary value, beyond the immediate treatment of a single individual, is becoming more prevalent as medical research incorporates machine learning (ML) practices.
This project involves automation of systems for mapping target fields to and from HL7 messages to enable automation of this previously laborious, manual process.
Ten minutes can be an eternity for medical professionals that need to make split-second decisions to save lives. If they miss a single piece of vital information it could prove critical – but equally, if they waste precious time searching for data, that too can be damaging to the patient’s health.
Patients’ regular medicines are often inaccurately prescribed when they transfer from hospital to home; a major source of medication errors and iatrogenic harm.
In partnership with Cure Kids and the National Science Challenge A Better Start, PDH supported the work led by The University of Auckland’s Gayl Humphrey on the project “See how they grow: Developing and trialling an interactive Child Growth Chart for New Zealand children”.
Recent studies have indicated that the current risk calculators are out of date and not specific to local contexts and population. This project is testing the hypothesis: Can we build risk calculators that are more accurate for the local population and context?
Many patients in New Zealand don’t receive the benefits from their medications due to poor medication behaviour, referred to as medication adherence.
Data from primary and secondary care has the potential to enhance the accuracy of decision making in healthcare. However, due to the fragmented nature of the health system, this information is often siloed, making it difficult for clinicians to access and share with other providers.
As Electronic Health Records (EHRs) become more ubiquitous, healthcare providers are beginning to appreciate the benefits of using this data for ‘secondary applications’, such as disease diagnosis and readmission prediction, by applying machine learning techniques (in particular, deep neural networks).
Atrial fibrillation is a common form of heart arrhythmia that affects tens of thousands of New Zealanders around the country.
It’s incredible how advanced medical technology has become. Many illnesses that were previously fatal are now treated with ease. Even something as serious as heart failure can be survived, and subsequently treated with an implanted cardiac resynchronization therapy (CRT) device.
Huge advances have been made in cardiovascular medicine in recent decades, but the application of some of these advances in real world practice has been minimal.
The cost of long-term conditions (LTCs), any ongoing health condition that cannot be cured, is becoming a major burden on healthcare providers.
Growth monitoring is an essential part of assessing children’s health care. Tracking how a child is growing can provide awareness of nutritional issues or other health problems. Growth charts are used to compare an individual child’s growth to what is considered normal or healthy.
The path to an elective surgery can contain many difficulties for both patients and clinicians. It is a lengthy, as well as daunting, process for the patient, while delays and cancellations can be extremely costly for the healthcare provider.
Chronic pain (any pain that lasts longer than 12 weeks) can be debilitating. As you can imagine, chronic pain is a rather broad term. It can have many different causes, which makes treatment difficult.
While the discovery of new health information is always a good thing, it can lead to difficulties. Doctors and specialists are required to actively ensure they are informed about the latest innovations and news in their field.
It’s good news when a patient is discharged from hospital, but things can often go wrong in the final moments of their stay. The problem is two-fold: the patient is given too much information, and not enough time to understand it.
Mobile technology has developed rapidly over the last few years. Almost everyone you know has a smartphone. You might even be reading this article on a smartphone.
Since the implementation of the New Zealand National Health Index in 1993, many different electronic health systems have popped up around the country. In his summer of research project Kieran McCullough looked at some of these systems and noticed that often the data that had been collected over the years was left undocumented and underused.
We’ve all been there – stuck in a waiting room for hours, only to be told by the doctor to go home and rest for a few days. There are numerous nurses and other healthcare staff whose days are filled dealing with patients waiting to receive advice from a doctor.
For patients and clinicians, deciding whether to go ahead with major surgery or not can be difficult. Your surgeon and anaesthetist will discuss both the benefits and risks of having surgery to help with the shared decision making process.
Data-driven healthcare will result in a “tsunami” of information from existing and new data sources including patient-generated data from genetic testing, consumer devices such as wearable fitness apps, and social media.
Currently, treatment decisions for elective surgery are currently based on clinicians’ assessments of the benefits the surgery will bring to the patient. This assessment is derived from the clinicians’ personal experience and knowledge of published evidence of effectiveness.
During admission to hospital, patients often show signs of acute physical deterioration before a serious event occurs. Current variation in vital signs charts, early warning scores, skills and knowledge of responders and availability of responders in hospitals means these patients may not receive timely, expert care when they need it, potentially leading to severe consequences.
Making Existing Data Sources AvailablePrincipal Investigator Quentin Thurier, Orion Health New Zealand has excellent health data due to national ID number and high quality data scientists. We seek to link them together by making health data easier to access for...
The Precision Driven Health partnership had an exciting summer of research in 2016-2017 with 11 students completing precision medicine projects.