Category: Predictive Analytics

Countering bias in the health system

This week Dr Kevin Ross, General Manager of Precision Driven Health and Haze White, Māori Health Researcher, spoke to Radio New Zealand about countering ethnic bias in the health system to ensure the findings of NZ health data research is meaningful for all groups of the population. The key topic of discussion was the reality …

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 …

Calculating Risk Over the Long Term

The practice of medicine is fast becoming a data science, and nowhere is this more apparent than with one of Precision Driven Health’s foundation projects – Epidemiology and the estimation of long-term surgical mortality. Principal investigator Dr Doug Campbell1 from Auckland District Health Board and his team are combining large national datasets of surgical operation …

Ensuring the Right Dosage

Ensuring patients are given the correct medication, in the right dosage, with proper instruction, is absolutely critical. However, the job of medication reconciliation is currently an onerous task that is tedious and time-consuming, even with the best software. Somebody needs to manually check every medication a patient is taking, to ensure that instructions are clear …

Harnessing Data to Investigate Surgical Outcomes

The NMDS is a set of patient health information collected on everyone who visits a health organisation in New Zealand. The information is anonymised and predominantly used for small scale administration and auditing. For this study, we utilised the records of all patients in NZ who had undergone surgery in 2013-2014 as our data set. …

No Data Left Behind

Humans are inconsistent creatures, especially when it comes to our health. During periods of serious illness, we are constantly in need of medical attention, and then we get well and might not see a health professional for years. This creates an extremely uneven Electronic Health Record (EHR), which is actually very complex to begin with, …

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 …

Clinical Risk Assessments and Calculators

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? Initially this project will focus to validate the current clinical risk of readmission …