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Objectives: To review the literature on different models of clinical governance and to explore their relevance to Australian primary health care, and their potential contributions on quality and safety. Data sources: 25 electronic databases, scanning reference lists of articles and consultation with experts in the field. We searched publications in English after 1999, but a search of the German language literature for a specific model type was also undertaken. The grey literature was explored through a hand search of the medical trade press and websites of relevant national and international clearing houses and professional or industry bodies. 11 software packages commonly used in Australian general practice were reviewed for any potential contribution to clinical governance. Study selection: 19 high‐quality studies that assessed outcomes were included. Data extraction: All abstracts were screened by one researcher, and 10% were screened by a second researcher to crosscheck screening quality. Studies were reviewed and coded by four reviewers, with all studies being rated using standard critical appraisal tools such as the Strengthening the Reporting of Observational Studies in Epidemiology checklist. Two researchers reviewed the Australian general practice software. Interviews were conducted with 16 informants representing service, regional primary health care, national and international perspectives. Data synthesis: Most evidence supports governance models which use targeted, peer‐led feedback on the clinician's own practice. Strategies most used in clinical governance models were audit, performance against indicators, and peer‐led reflection on evidence or performance. Conclusions: The evidence base for clinical governance is fragmented, and focuses mainly on process rather than outcomes. Few publications address models that enhance safety, efficiency, sustainability and the economics of primary health care. Locally relevant clinical indicators, the use of computerised medical record systems, regional primary health care organisations that have the capacity to support the uptake of clinical governance at the practice level, and learning from the Aboriginal community‐controlled sector will help integrate clinical governance into primary care.

Variability in the use of CPAC tools meant that at the time of the study they did not provide a transparent and equitable method of determining access to surgery. This highlights the difficulties in developing and implementing CPAC and suggests that further development is difficult in the absence of evidence to identify patients who will benefit the most from surgery.

BackgroundThe science of complex systems has been proposed as a way of understanding health services and the demand for them, but there is little quantitative evidence to support this. We analysed patterns of healthcare use in different urgent care settings to see if they showed two characteristic statistical features of complex systems: heavy-tailed distributions (including the inverse power law) and generative burst patterns.MethodsWe conducted three linked studies. In study 1 we analysed the distribution of number of contacts per patient with an urgent care service in two settings: emergency department (ED) and primary care out-of-hours (PCOOH) services. We hypothesised that these distributions should be heavy-tailed (inverse power law or log-normal) in keeping with typical complex systems. In study 2 we analysed the distribution of bursts of contact with urgent care services by individuals: correlated bursts of activity occur in complex systems and represent a mechanism by which overall heavy-tailed distributions arise. In study 3 we replicated the approach of study 1 using data systematically identified from published sources.ResultsStudy 1 involved data from a PCOOH service in Scotland (725,000) adults, 1.1 million contacts) and an ED in New Zealand (60,000 adults, 98,000 contacts). The total number of contacts per individual in each dataset was statistically indistinguishable from an inverse power law (p > 0.05) above 4 contacts for the PCOOH data and 3 contacts for the ED data. Study 2 found the distribution of contact bursts closely followed a heavy-tailed distribution (p < 0.008), indicating the presence of correlated bursts. Study 3 identified data from 17 studies across 8 countries and found distributions similar to study 1 in all of them.ConclusionsUrgent healthcare use displays characteristic statistical features of large complex systems. These studies provide strong quantitative evidence that healthcare services behave as complex systems and have important implications for urgent care. Interventions to manage demand must address drivers for consultation across the whole system: focusing on only the highest users (in the tail of the distribution) will have limited impact on efficiency. Bursts of attendance — and ways to shorten them — represent promising targets for managing demand.

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