The Health Research Council of New Zealand and Precision Driven Health (PDH) have awarded two Postdoctoral Fellowships to the value of $452,422, as part of a jointly funded call for research in precision health.
Precision health is an emerging model of healthcare that combines all information unique to an individual and identifies prevention and treatment strategies that will be effective for them based on genetic, environmental and lifestyle factors. This includes applying new data science techniques to understanding the massive volume of data on an individual that is being captured by health information systems, consumer devices, social networks, genetic testing and other sources.
Empowering individuals with their information, as well as presenting the right information to individuals and their healthcare team at the right time, are of key importance in precision health.
The Precision Driven Health partnership (PDH), established in March 2016, is one of the most ambitious data science research initiatives to be undertaken in New Zealand. PDH is an award-winning research partnership between New Zealand’s health IT sector, health providers and universities, aimed at improving health outcomes through data science. Its work seeks to personalise health by integrating new data sources, developing predictive models, optimise decision making and empower people with new tools.
Founded in 2016, the partnership is investing $38m over seven years in collaborations between academia, industry and government. The partnership unites the health IT sector with healthcare providers and universities to create health and commercial opportunities for New Zealanders.
Funding Research is focused on applying new data science techniques to understand the massive volume of data about an individual captured by health information systems, consumer devices, social networks, genetic testing, and other sources. To date, PDH has supported more than 75 projects in health data science, including summer and postgraduate scholarships, a joint PDH-HRC Postdoctoral Fellowship scheme, and multi-million dollar discovery and transformation programmes in several areas, including de-identification and deep learning.
PDH researchers are also exploring vital concepts such as bias in machine learning, the ethics of AI use, and consent paradigms for enabling the next wave of health data research. Find out more at www.precisiondrivenhealth.com, or on LinkedIn and Twitter.
Mr Thomas Adams, from the University of Auckland, is exploring improved surgical scheduling software. The aim is to develop software that schedules elective surgical sessions quickly and in a way that reduces the chances of the sessions running overtime. The software would use novel machine-learning techniques that incorporate historical surgery data to estimate the probability that sessions run overtime. Further research into improved prediction of surgery durations, for use in scheduling sessions, that utilises individual patient data would also be performed.
Dr Zhenqiang Wu, also from the University of Auckland, is looking at developing a decision support system at ED triage for predicting health outcomes Emergency department overcrowding is a major global healthcare issue. The consequences are well-established, usually affecting patients (poor outcomes), staff (stressed) and healthcare system (long length of stay). Without increases in the number of EDs and staff, an effective way is to optimise the use of existing resources.
This study intends to develop a decision support system at ED triage time, to predict hospital admission and longer ED length of stay by using a wide range of routinely collected big data (DHBs Health Records System and MoH database). This system has the potential to meet the ED health target of a ‘shorter stay’ and ‘lower hospital admission rates’ by accurately identifying high-risk patients at an early stage of ED and making more effective interventions for them. If so, this decision support system can be widely used by ED triage assessors in the near future, with the potential to improve the quality of acute care.
