ArticleBMC health services research2022
Making the most of audit and feedback to improve diabetes care: a qualitative study of the perspectives of Australian Diabetes Centres.
Article in BMC health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiabetes 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.
methodsSemi-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.
resultsRepresentatives 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.
conclusionsThis 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.