CASE STUDY Northern Region Health Coordination Centre (NRHCC)
Developing a regional view
As the Covid-19 pandemic spread around the world in early 2020, health districts in the Northern region of New Zealand worked rapidly to develop a regional view of hospital capacity and occupancy. The Covid-19 Dashboard was used to support Covid-19 testing, vaccination, managed isolation and community care and is being expanded to other areas such as planned care, pulling from national datasets.
As the level of infection increased, the majority of Covid patients were now in the community, and the focus turned to predicting who might need high-level care. Visualising a clinical risk score As the number of Covid-19 cases quickly grew, the regional
In March 2020 the Northern region was planning for an inundation of Covid-19 cases and for the hospitals to be overwhelmed, says former Head of Analytics at Waitematā, Delwyn Armstrong. This meant the regional analytics teams, which includes Auckland, Counties Manukau,Waitematā and Northland Districts, were initially focused on getting a regional view of hospital capacity and occupancy. Within three weeks, the team had developed a regional data store and soon after they had visibility of real time hospital capacity and occupancy across the region: something never achieved before. Due to the success of New Zealand’s lockdown, the hospital view was not as urgent as predicted, but as testing ramped up the dashboard was used to display a regional view of Covid-19 test results. Armstrong, who is now Director Health Analytics and Insights, TeWhatu Ora – Health New Zealand, says after vaccinations began the dashboard displayed immunisation data and was also used to monitor people in quarantine or managed isolation. When the Delta wave hit, it started to be used in relation to patient care, helping to direct ambulances carrying Covid-19 patients by identifying sites with ‘Covid-ready’ beds to care for them, she says.
dashboard was populated with more and more data.
This enabled the regional team, led by health informatics fellow at the i3 Institute for Innovation and Improvement, Cheng Kai (CK) Jin, to create a machine learning algorithm to predict the risk of hospitalisation. Data around demographics, vaccination status, long term conditions, medications, and test results was used to create the clinical risk score, which was then automatically calculated and displayed in the operational dashboard. This helped clinicians decide who needed to be seen and whether they met criteria for anti-viral drugs and pulse oximeters for monitoring in the community. “The dashboard enabled data driven allocation of health services,” says Armstrong. Data was shared with primary health organisations twice a day, so general practitioners could see all of their patients who had
HEALTHCARE ANALYTICS IN AOTEAROA NEW ZEALAND | A HINZ SPECIAL REPORT | 4
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