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Health care homes early evidence in Wellington

Early evidence suggests a team-based primary health care practice model can reduce emergency department admissions to hospitals.

New research conducted by Auckland University of Technology (AUT), for the Productivity Commission, looks at the implementation in Wellington of a multi-disciplinary team-based model known as “Health Care Homes”. The research, Health Care Homes: Early Evidence in Wellington, was published today by the Productivity Commission.

Described as a “whole of practice transformation”, the Health Care Home model involves health professionals working together in new ways and using tools such as an online patient portal and GP telephone triage to tailor services to patients’ needs.

“The main finding of the research is a statistically significant drop in Emergency Department (ED) admissions for patients at practices that implemented the Health Care Homes model” says Dr Gail Pacheco, Professor of Economics and Director of the New Zealand Work Research Institute at AUT.

“A drop in ED admissions is a positive signal for health outcomes of the affected population, as well as beneficial for managing public healthcare costs.”
Professor Pacheco says “future research would need to focus on the longer-term impacts of the Health Care Homes model and also include a wider range of practice level data, such as waiting times, staff to patient ratios, use of online services, staff turnover and patient experience data.”

The research was undertaken as part of the Productivity Commission’s inquiry into Measuring and Improving State Sector Productivity, which was completed in August this year.

It uses administrative data on 235,485 enrolled patients from 55 Compass Health Primary Health Organisation general practices, linked with National Minimum Data Set[1] records from Capital and Coast District Health Board. Of the 55 general practices, 11 implemented the Health Care Homes change process between July 2016 and October 2017. This means they operated the new model for three to 15 months during the period of the study (which used data from 2014 – 2017).

Productivity Commission Inquiry Director, Judy Kavanagh, says “the health sector is facing unprecedented cost and demand pressures caused by a range of factors, including new health treatments and an ageing population. Primary health care is an important lever to address these pressures, because of its role in prevention and early intervention and in better coordination of other health services.”

She added that “it has always been thought of as too difficult to measure the impact and productivity of primary health care services. This research shows that innovation in general practice services can reduce patients’ use of hospital services, and that it is possible to measure these impacts using linked data”.

1. The research uses individual level data from 2014-2017 from 235,485 individuals who are enrolled in 55 general practices that are members of Compass PHO. It was matched with data from the National Minimum Data Set (NMDS) by Capital and Coast District Health Board staff.

2. The working paper was authored by Dr Kabir Dasgupta and Professor Gail Pacheco from the New Zealand Work Institute, Auckland University of Technology (AUT) under contract to the Productivity Commission, and it has been published by the Commission.

3. The Productivity Commission completed its inquiry into state sector productivity in August 2018. Two final reports were published – one on improving state sector productivity, the other on measuring state productivity. Both are available at www.productivity.govt.nz/statesectorprod

4. The New Zealand Productivity Commission - an independent Crown entity - was established in April 2011 and completes in-depth inquiry reports on topics selected by the Government, carries out productivity-related research, and promotes understanding of productivity issues.


[1] The National Minimum Data Set – This is the Ministry of Health’s national collection of public and private hospital discharge information, including coded clinical data for inpatients and day patients.

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