A qualitative study on the implementation of quality systems in Australian hospitals

Leggat, Sandra G.; Balding, Cathy

doi:10.1177/0951484817715594

Cited by 12 publications

(16 citation statements)

References 40 publications

(61 reference statements)

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“…Jones, F. et al [ 104 ] UK NPT Self-care training programme for stroke practitioners – Process evaluation Qualitative Retrospective Yes No No 71. Leggat et al [ 105 ] Australia NPT Quality improvement in hospitals Systematic review [ 236 ] No Process evaluation Qualitative Retrospective Yes Yes Yes 72. Lhussier et al [ 106 ] UK NPT Care planning in primary care No Field study Qualitative Retrospective Yes Yes N/A 73.…”

Section: Resultsmentioning

confidence: 99%

“…Studies included in this review fell into seven categories. The most numerous group of studies were those concerned with service organisation and delivery ( n = 29, 26.9% [ 23 , 27 , 32 – 35 , 43 – 46 , 58 , 76 , 79 , 82 , 84 , 86 , 89 , 91 , 92 , 99 , 105 – 107 , 110 , 115 , 116 , 119 , 122 , 127 , 133 – 136 , 140 , 146 , 148 ]). For example, in the UK, Grant et al [ 34 , 35 ] evaluated a complex intervention aimed at reducing risk in prescribing in primary care.…”

Section: Resultsmentioning

confidence: 99%

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Using Normalization Process Theory in feasibility studies and process evaluations of complex healthcare interventions: a systematic review

et al. 2018

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BackgroundNormalization Process Theory (NPT) identifies, characterises and explains key mechanisms that promote and inhibit the implementation, embedding and integration of new health techniques, technologies and other complex interventions. A large body of literature that employs NPT to inform feasibility studies and process evaluations of complex healthcare interventions has now emerged. The aims of this review were to review this literature; to identify and characterise the uses and limits of NPT in research on the implementation and integration of healthcare interventions; and to explore NPT’s contribution to understanding the dynamics of these processes.MethodsA qualitative systematic review was conducted. We searched Web of Science, Scopus and Google Scholar for articles with empirical data in peer-reviewed journals that cited either key papers presenting and developing NPT, or the NPT Online Toolkit (www.normalizationprocess.org). We included in the review only articles that used NPT as the primary approach to collection, analysis or reporting of data in studies of the implementation of healthcare techniques, technologies or other interventions. A structured data extraction instrument was used, and data were analysed qualitatively.ResultsSearches revealed 3322 citations. We show that after eliminating 2337 duplicates and broken or junk URLs, 985 were screened as titles and abstracts. Of these, 101 were excluded because they did not fit the inclusion criteria for the review. This left 884 articles for full-text screening. Of these, 754 did not fit the inclusion criteria for the review. This left 130 papers presenting results from 108 identifiable studies to be included in the review. NPT appears to provide researchers and practitioners with a conceptual vocabulary for rigorous studies of implementation processes. It identifies, characterises and explains empirically identifiable mechanisms that motivate and shape implementation processes. Taken together, these mean that analyses using NPT can effectively assist in the explanation of the success or failure of specific implementation projects. Ten percent of papers included critiques of some aspect of NPT, with those that did mainly focusing on its terminology. However, two studies critiqued NPT emphasis on agency, and one study critiqued NPT for its normative focus.ConclusionsThis review demonstrates that researchers found NPT useful and applied it across a wide range of interventions. It has been effectively used to aid intervention development and implementation planning as well as evaluating and understanding implementation processes themselves. In particular, NPT appears to have offered a valuable set of conceptual tools to aid understanding of implementation as a dynamic process.Electronic supplementary materialThe online version of this article (10.1186/s13012-018-0758-1) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Using Normalization Process Theory in feasibility studies and process evaluations of complex healthcare interventions: a systematic review

et al. 2018

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“…Hospital quality managers concentrated on compliance with these standards, thereby diverting their time and attention from the hospital quality improvement initiative. 66 Shifts in other influential government policies diverted attention from the quality program, as did an accreditation review in one hospital. At the organization level, the hospitals' senior and mid-level leaders reported additional challenges and priorities, which distracted them from dedication to the quality program.…”

Section: Multilevel Analysis Of Learningmentioning

confidence: 99%

Multi‐level analysis of the learning health system: Integrating contributions from research on organizations and implementation

Harrison

Shortell

2020

Learning Health Systems

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Introduction: Organizations and systems that deliver health care may better adapt to rapid change in their environments by acting as learning organizations and learning health systems (LHSs). Despite widespread recognition that multilevel forces shape capacity for learning within care delivery organizations, there is no agreed-on, comprehensive, multilevel framework to inform LHS research and practice. Methods:We develop such a framework, which can enhance both research on LHSs and practical steps toward their development. We draw on existing frameworks and research within organization and implementation science and synthesize contributions from three influential frameworks: the Consolidated Framework for Implementation Research, the social-ecological framework, and the organizational change framework. These frameworks come, respectively, from the fields of implementation science, public health, and organization science.Results: Our proposed integrative framework includes both intraorganizational levels (individual, team, mid-management, organization) and the operating and general environments in which delivery organizations operate. We stress the importance of examining interactions among influential factors both within and across system levels and focus on the effects of leadership, incentives, and culture. Additionally, we indicate that organizational learning depends substantially on internal and cross-level alignment of these factors. We illustrate the contribution of our multilevel perspective by applying it to the analysis of three diverse implementation initiatives that aimed at specific care improvements and enduring system learning. Conclusions:The framework and perspective developed here can help investigators and practitioners broadly scan and then investigate forces influencing improvement and learning and may point to otherwise unnoticed interactions among influential factors. The framework can also be used as a planning tool by managers and practitioners.

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“…Clinicians, in particular, are unlikely to adopt a system they cannot understand, and while the more advanced AI models seem to perform well, making them more understandable is important for trustable and reliable systems (13,58). As predicted by socio-technical theory (59,60), the challenges to AI implementation are rarely technical (39), but have more to do with the social aspects of change. The most common barrier found in this scoping review was social in that if hospital managers and clinicians did not understand how the application worked and did not trust the analysis they would be unlikely to adopt AI (13).…”

Section: Discussionmentioning

confidence: 99%

How are hospitals using artificial intelligence in strategic decision- making?—a scoping review

Leggat¹,

Yap²

2020

J Hosp Manag Health Policy

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Artificial intelligence (AI) is a useful tool for clinical decision-making in hospitals, and for strategic decision-making in other industries. This scoping review provides a comprehensive review of the potential for AI to improve strategic decision-making in hospitals by exploring current applications of AI in this area. Peer-reviewed publications and conference presentations associated with AI for strategic decisionmaking were identified in Health Administration, Computer Science and Business and Management databases to answer the research question; how are hospitals using AI in strategic decision-making? The review found 19 published AI applications for hospital strategic decision-making. The applications used a variety of knowledge-based, probabilistic reasoning and data-driven AI, that generally followed the course of AI maturity. They focused on specific decisions, with none providing a comprehensive framework for strategic decision-making drawing on existing enterprise-or system-wide data. There was little evidence of evaluation of the AI applications, with no cost-benefit evaluation. The scoping review suggests the need for substantial improvement in the understanding of AI and its application among hospital decisionmakers leading to greater organisational maturity. This would suggest that journals and researchers require evaluative and economic research and that training to improve understanding of AI be provided for board members, managers and clinicians.

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A qualitative study on the implementation of quality systems in Australian hospitals

Cited by 12 publications

References 40 publications

Using Normalization Process Theory in feasibility studies and process evaluations of complex healthcare interventions: a systematic review

Using Normalization Process Theory in feasibility studies and process evaluations of complex healthcare interventions: a systematic review

Multi‐level analysis of the learning health system: Integrating contributions from research on organizations and implementation

How are hospitals using artificial intelligence in strategic decision- making?—a scoping review

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