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. 2022 Feb 24;22(1):255.
doi: 10.1186/s12913-022-07652-9.

Making the most of audit and feedback to improve diabetes care: a qualitative study of the perspectives of Australian Diabetes Centres

Affiliations

Making the most of audit and feedback to improve diabetes care: a qualitative study of the perspectives of Australian Diabetes Centres

Matthew Quigley et al. BMC Health Serv Res. .

Abstract

Background: Diabetes has high burden on the health system and the individual, and many people living with diabetes struggle to optimally manage their condition. In Australia, people living with diabetes attend a mixture of primary, secondary and tertiary care centres. Many of these Diabetes Centres participate in the Australian National Diabetes Audit (ANDA), a quality improvement (QI) activity that collects clinical information (audit) and feeds back collated information to participating sites (feedback). Despite receiving this feedback, many process and care outcomes for Diabetes Centres continue to show room for improvement. The purpose of this qualitative study was to inform improvement of the ANDA feedback, identify the needs of those receiving feedback and elicit the barriers to and enablers of optimal feedback use.

Methods: Semi-structured interviews were conducted with representatives of Australian Diabetes Centres, underpinned by the Consolidated Framework for Implementation Research (CFIR). De-identified transcripts were analysed thematically, underpinned by the domains and constructs of the CFIR.

Results: Representatives from 14 Diabetes centres participated in this study, including a diverse range of staff typical of the Diabetes Centres who take part in ANDA. In general, participants wanted a shorter report with a more engaging, simplified data visualisation style. Identified barriers to use of feedback were time or resource constraints, as well as access to knowledge about how to use the data provided to inform the development of QI activities. Enablers included leadership engagement, peer mentoring and support, and external policy and incentives. Potential cointerventions to support use include exemplars from clinical change champions and peer leaders, and educational resources to help facilitate change.

Conclusions: This qualitative study supported our contention that the format of ANDA feedback presentation can be improved. Healthcare professionals suggested actionable changes to current feedback to optimise engagement and potential implementation of QI activities. These results will inform redesign of the ANDA feedback to consider the needs and preferences of end users and to provide feedback and other supportive cointerventions to improve care, and so health outcomes for people with diabetes. A subsequent cluster randomised trial will enable us to evaluate the impact of these changes.

Keywords: Audit and feedback; CFIR; Consolidated Framework for Implementation Research; Diabetes Mellitus Type 1; Diabetes Mellitus Type 2; Framework; Qualitative; Quality Improvement.

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Conflict of interest statement

SG is employed by Monash University and receives funding from National Health and Medical Research Council (NHMRC), Medical Research Future Fund (MRFF), and the Victorian Department of Health and Human Services. She has no declaration of interest specific to the research reported in this paper. SZ reports payment to institution (Monash University) from Eli Lilly Australia Ltd, Boehringer-Ingelheim, Merck Sharp & Dohme (MSD) Australia, AstraZeneca, Novo Nordisk, Sanofi, Servier, for work outside the submitted work. The other authors declare no relevant declarations of interest with regards to this manuscript.

Figures

Fig. 1
Fig. 1
Study overview. Figure 1 shows the larger body of work within which this project sits. This manuscript describes Phase1, the qualitative formative work, which will inform the development of the intervention delivered in Phase 2
Fig. 2
Fig. 2
Example of coding process of mapping data to CFIR and eliciting belief statements. Figure 2 shows an example of the coding process. The raw data, represented in the bubbles on the left, is first mapped to the domains and constructs of the CFIR. Then, belief statements are generated that represent a synthesised version of participant comments reflecting common beliefs. After checking these belief statements against the raw data, exemplar quotes are chosen from the raw data

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