For some time, a major challenge in New Zealand has been the pressing need to redress differing health outcomes between Māori and non-Māori. Much of the difference in mortality rates between Māori and non-Māori lies in the comparatively rapid decline in cardiovascular death among non-Māori which has not been shared by Māori.1

Underpinning the continuing epidemic of cardiovascular mortality among Māori are sustained high levels of major risk factors,2 along with relatively lower intervention rates3 for Māori than would be expected for their clinical need. Despite the results from some promising initiatives,4 improvements in health outcomes for Māori have been more anticipated than realised. Alarmingly now, after decades of disparity, the differences in mortality between Māori and non-Māori appear to be widening.1

To address the challenge of improving health outcomes for Māori, a spectrum of activity has been advocated.5 Critical among them is the pressing need to ensure that all Māori patients are able to have an evaluation of their risk factors in primary care and receive the best available preventive treatment. To support this activity, evidence-based guidelines have been developed in New Zealand to guide risk assessment and treatment for both Māori and non-Māori patients.6

If fully implemented, the potential of these guidelines to improve health outcomes is immense—some 55% of future cardiovascular disease events could be prevented.6 One of the key benefits from the guidelines would come from their ability to close the gap between optimal treatment and actual prescribing rates in primary care where research suggests that about two-thirds of patients with vascular disease may not be receiving the medications that can effectively improve their survival.7

To help realise the full benefit for Māori, the guidelines recommend that their risk assessment should be provided a decade earlier than non-Māori.6 The decision support software studied in this issue of the Journal provides the ability to undertake risk assessment with patients in primary care and then also present them with specific management advice that directly employs the recommendations from the cardiovascular guidelines.8

Evidence-based medicine (EBM)9 and information technology10 have separately been attributed with great potential to improve the quality of health care. Electronic decision support now fuses the promise of evidence-based material with the possibilities of information technology. The ability of information technology to link complex sets of information within the electronic health record gives decision support its unique platform.

By enabling EBM to operate at a systems level, decision support also maximises the opportunity for all practitioners to put into practice the best available evidence. Decision support bypasses many of the problems associated with paper-based guidelines. No longer will the use of guidelines depend on necessity that practitioners will take the extra time required to locate them in a busy consultation or possess the additional skills needed to effectively apply them.11

For many GPs, the issue has been simply one of information overload—they have felt overwhelmed by the amount of information available to them. Decision support ensures that the best evidence can be automatically available at the time of clinical decision making. Indeed, making the best evidence available at the time of clinical decision making has been shown to be effective at improving practitioner performance across a whole spectrum of clinical activity including disease prevention, diagnosis, management, and detailed aspects of prescribing.12

Just expecting a decision support tool though to improve practitioner performance and patient outcomes is not enough. Like any intervention, it needs to be subjected to rigorous evaluation to assess its uptake and impact on practitioner performance and patient care—adverse findings have been recorded from the use of some decision support tools, especially among those systems that have been unpopular with clinical staff.12

The results from the current study give promise to the potential of decision support to improve cardiovascular health for both Māori and non-Māori. The report follows on from previous data that suggests that decision support is an acceptable tool that can improve practitioner performance.13 Decision support is as likely to increase the documentation of cardiovascular disease risk assessment and risk factors for Māori as non-Māori.14

With the current report, information is now available about one of the largest cohorts of Māori and non-Māori patients ever assembled in New Zealand. In addition to assisting with direct patient care, the large size of the database now suggests that information from the risk assessments (when fed back to a central repository and combined with mortality and morbidity information from other sources) could be used to customise the risk assessment information to generate a New Zealand-specific profile of cardiovascular prognosis among both Māori and non-Māori populations.

New developments such as decision support offer an opportunity to bridge the quality chasm and narrow the disparity in ethnic outcomes. Risk assessment and management is a key linchpin at the interface between personal and population health care.

However, before the promise offered by this tool may be fully realised, several potential obstacles at different levels of the health system should be addressed:

To coordinate and maximise all these efforts, both primary and secondary care organisations (especially District Health Boards) need to facilitate quality improvement plans that educate providers and patients to the importance of addressing risk factors, while promoting systems that assess risk for every patient and identify those patients in need of effective treatments.

The widespread adoption of decision support tools across the whole country would create a world class database of information about risk and management that would be New Zealand-specific. In particular, the universal delivery of evidence-based information to all patients (especially identifying those most at risk) would advance the pressing need to bridge disparities in mortality between Māori and non-Māori.

Author information: Phil Hider, Senior Lecturer in Clinical Epidemiology, University of Otago, Christchurch

Correspondence: Dr Phil Hider, Senior Lecturer in Clinical Epidemiology, University of Otago, PO Box 4345, Christchurch. Fax (03) 364 3697; email phil.hider@chmeds.ac.nz