Category: Projects

Feasibility of Analysis of Lab Result Patterns for Patient Results

This feasibility study, in partnership with Vensa Health, is answering the following questions: How can we accurately automate interpretation and dissemination of lab results to save clinician time, while keeping the clinical control in their hands in way that’s intuitive and flexible? Can collective best practices be gathered and help improve clinical decision making based …

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 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. It is vital …

Interpretable Machine Learning

The “black box” metaphor is commonly used to refer to the lack of understanding of how modern Machine Learning (ML) systems make decisions. Researchers are working actively to remedy this situation which is especially problematic in Healthcare where legal accountability and ethics have to be taken into account in the decision-making process. Consequently, the industry …

Missing Health Data Imputation

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. Finding ways to deal with missing data is at the heart of the project Value added by Multiple …

A Public Health Calculator Website for Kiwis

Calibrated for Kiwis As a small country in the South Pacific, our uniqueness is often overlooked by health science researchers. Medical assessment tools are created and used based on research conducted in other countries with larger populations. While we have many similarities with these populations, we are not an identical match. Which is why one …

Automated De-identification

Separating patients from their records 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. The information an EHR contains, such as symptoms, treatment plans, response to medication, can be extracted using …

message map

Automated Message Mapping

This project involves automation of systems for mapping target fields to and from HL7 messages to enable automation of this previously laborious, manual process. PI: Junjae Lee, Orion Health Junjae Leehttps://precisiondrivenhealth.com/researchers/junjae-lee/

Smart Search: Clinical Document Semantic Search

A ‘Google’ for Electronic Health Records  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 …

Smart Medication Reconciliation

Automating for accuracy Patients’ regular medicines are often inaccurately prescribed when they transfer from hospital to home; a major source of medication errors and iatrogenic harm. Medicines reconciliation (MedRec) is an evidence-based process where clinicians manually compare a patient’s regular medicines with what is prescribed in hospital then reconciling any inaccuracies. While MedRec is effective …

See How They Grow

In partnership with Cure Kids and the National Science Challenge A Better Start, PDH supports 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”. In this project, Ms Humphrey and her team will design, develop, and …