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. 2022 Apr;66(3):455-465.
doi: 10.1111/1754-9485.13359. Epub 2021 Dec 1.

Head and neck cancers: Monitoring quality and reporting outcomes

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Head and neck cancers: Monitoring quality and reporting outcomes

Madhavi Chilkuri et al. J Med Imaging Radiat Oncol. 2022 Apr.

Abstract

Introduction: Head and neck cancers (HNC) require high level multidisciplinary care to achieve optimal outcomes. Reporting of quality indicators (QIs) has been instigated by some health services in an effort to improve quality of care. The aim of this study was to determine the quality of care provided to patients with HNC at a single institution by analysing compliance with QIs and to explore the feasibility and utility of collecting this data.

Methods: This was a single institution retrospective chart review of all patients with squamous cell HNC at Townsville Hospital who were treated with curative intent between June 2011 and June 2019. Data was entered into a RedCap database and then exported to Stata V16 for analysis.

Results: A total of 537 patients were included in the overall study, with six patients who had a synchronous non-HNC and two patients who received previous radiotherapy (RT) to the head and neck region excluded from the outcome analysis. Overall, compliance with pre-treatment, treatment and post-treatment QIs was high, with the exception of smoking cessation support (66%), post-treatment dental review and time to post-operative RT (33% of patients within 6 weeks). The 5-year overall survival was 69.4% (CI; 64-73.2%). The cumulative incidence of locoregional relapse for the overall study cohort was 18% (CI; 14.8-21.4%).

Conclusion: Collecting and evaluating quality metrics is feasible and helps identify areas for improvement. Centres treating HNC patients should strive towards monitoring quality against benchmarks and demonstrate transparency in outcome data.

Keywords: head and neck cancer; outcomes; quality improvement; quality indicators.

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Figures

Fig. 1
Fig. 1
Study schema.
Fig. 2
Fig. 2
(a) Kaplan–Meier OS for whole cohort; (b) Kaplan–Meier OS for p16+ and p16− OPSCC; (c) Locoregional relapse competing risks regression for whole cohort; (d) Locoregional relapse competing risks regression for p16+ and p16− OPSCC.

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