IntroductionValue-based healthcare delivery models have emerged to address the unprecedented pressure on long-term health system performance and sustainability and to respond to the changing needs and expectations of patients. Implementing and scaling the benefits from these care delivery models to achieve large-system transformation are challenging and require consideration of complexity and context. Realist studies enable researchers to explore factors beyond ‘what works’ towards more nuanced understanding of ‘what tends to work for whom under which circumstances’. This research proposes a realist study of the implementation approach for seven large-system, value-based healthcare initiatives in New South Wales, Australia, to elucidate how different implementation strategies and processes stimulate the uptake, adoption, fidelity and adherence of initiatives to achieve sustainable impacts across a variety of contexts.Methods and analysisThis exploratory, sequential, mixed methods realist study followed RAMESES II (Realist And Meta-narrative Evidence Syntheses: Evolving Standards) reporting standards for realist studies. Stage 1 will formulate initial programme theories from review of existing literature, analysis of programme documents and qualitative interviews with programme designers, implementation support staff and evaluators. Stage 2 envisages testing and refining these hypothesised programme theories through qualitative interviews with local hospital network staff running initiatives, and analyses of quantitative data from the programme evaluation, hospital administrative systems and an implementation outcome survey. Stage 3 proposes to produce generalisable middle-range theories by synthesising data from context–mechanism–outcome configurations across initiatives. Qualitative data will be analysed retroductively and quantitative data will be analysed to identify relationships between the implementation strategies and processes, and implementation and programme outcomes. Mixed methods triangulation will be performed.Ethics and disseminationEthical approval has been granted by Macquarie University (Project ID 23816) and Hunter New England (Project ID 2020/ETH02186) Human Research Ethics Committees. The findings will be published in peer-reviewed journals. Results will be fed back to partner organisations and roundtable discussions with other health jurisdictions will be held, to share learnings.
Business process analysis and process mining, particularly within the health care domain, remain under-utilized. Applied research that employs such techniques to routinely collected health care data enables stakeholders to empirically investigate care as it is delivered by different health providers. However, cross-organizational mining and the comparative analysis of processes present a set of unique challenges in terms of ensuring population and activity comparability, visualizing the mined models, and interpreting the results. Without addressing these issues, health providers will find it difficult to use process mining insights, and the potential benefits of evidence-based process improvement within health will remain unrealized. In this article, we present a brief introduction on the nature of health care processes, a review of process mining in health literature, and a case study conducted to explore and learn how health care data and cross-organizational comparisons with process-mining techniques may be approached. The case study applies process-mining techniques to administrative and clinical data for patients who present with chest pain symptoms at one of four public hospitals in South Australia. We demonstrate an approach that provides detailed insights into clinical (quality of patient health) and fiscal (hospital budget) pressures in the delivery of health care. We conclude by discussing the key lessons learned from our experience in conducting business process analysis and process mining based on the data from four different hospitals.
Objective. Unwarranted variation in clinical practice is a target for quality improvement in health care, but there is no consensus on how to identify such variation or to assess the potential value of initiatives to improve quality in these areas. This study illustrates the use of a triple test, namely the comparative analysis of processes of care, costs and outcomes, to identify and assess the burden of unwarranted variation in clinical practice.Methods. Routinely collected hospital and mortality data were linked for patients presenting with symptoms suggestive of acute coronary syndromes at the emergency departments of four public hospitals in South Australia. Multiple regression models analysed variation in re-admissions and mortality at 30 days and 12 months, patient costs and multiple process indicators.Results. After casemix adjustment, an outlier hospital with statistically significantly poorer outcomes and higher costs was identified. Key process indicators included admission patterns, use of invasive diagnostic procedures and length of stay. Performance varied according to patients' presenting characteristics and time of presentation.Conclusions. The joint analysis of processes, outcomes and costs as alternative measures of performance inform the importance of reducing variation in clinical practice, as well as identifying specific targets for quality improvement along clinical pathways. Such analyses could be undertaken across a wide range of clinical areas to inform the potential value and prioritisation of quality improvement initiatives.What is known about the topic? Variation in clinical practice is a long-standing issue that has been analysed from many different perspectives. It is neither possible nor desirable to address all forms of variation in clinical practice: the focus should be on identifying important unwarranted variation to inform actions to reduce variation and improve quality. What does this paper add? This paper proposes the comparative analysis of processes of care, costs and outcomes for patients with similar diagnoses presenting at alternative hospitals, using linked, routinely collected data. This triple test of performance indicators extracts maximum value from routine data to identify priority areas for quality improvement to reduce important and unwarranted variations in clinical practice. What are the implications for practitioners? The proposed analyses need to be applied to other clinical areas to demonstrate the general application of the methods. The outputs can then be validated through the application of quality improvement initiatives in clinical areas with identified important and unwarranted variation. Validated frameworks for the comparative analysis of clinical practice provide an efficient approach to valuing and prioritising actions to improve health service quality.
Variation in adherence to clinical guidelines, and in the organisation and delivery of health care significantly impact patient outcomes and health service costs. Despite mounting evidence of variation in clinical practice, the funds allocated to improve the quality of existing services remain small, relative to the resources allocated to new technologies. Quality improvement is a complex intervention, with a lack of focus on outcomes, and greater uncertainty around its effects. These factors have contributed to a relatively narrow, mainstream view of quality improvement as focussing on safety, with efforts to improve adherence to best practice limited to high profile clinical areas. This paper presents an analysis of linked, routinely collected data to identify variation in patient outcomes and processes of care across hospitals for patients presenting with low-risk chest pain. Such analyses provide a low cost, broadly applicable approach to identifying potentially important areas of variation in clinical practice, to inform the prioritisation of more detailed analyses to validate, and further investigate the causes of variation.
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