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Data quality predicts care quality: findings from a national clinical audit

Research output: Contribution to journalArticle

Mark Yates, Katie Bechman, Elaine Dennison, Alexander Macgregor, Jo Ledingham, Sam Norton, James Galloway

Original languageEnglish
JournalArthritis Research and Therapy
Publication statusAccepted/In press - 29 Mar 2020

King's Authors

Abstract

Background. Missing clinical outcome data are a common occurrence in longitudinal studies. Data quality in clinical audit is a particular cause for concern. The relationship between departmental levels of missing clinical outcome data and care quality is not known. We hypothesise that completeness of key outcome data in a national audit predicts departmental performance. Methods. The National Clinical Audit for Rheumatoid and Early Inflammatory Arthritis (NCAREIA) collected data on care of patients with suspected rheumatoid arthritis (RA) from early 2014 to late 2015. This observational cohort study collected data on patient demographics, departmental variables, service quality measures including time to treatment, and the key RA clinical outcome measure, disease activity at baseline and three months follow up. A mixed effects model was conducted to identify departments with high/low proportions of missing baseline disease activity data with the results plotted on a caterpillar graph. A mixed effects model was conducted to assess if missing baseline disease activity predicted prompt treatment. Results. 6,205 patients with complete treatment time data and a diagnosis of RA were recruited from 136 departments. 34.3% had missing disease activity at baseline. Mixed effects modelling identified 13 departments with high levels of missing disease activity, with a cluster observed in the Northwest of England. Missing baseline disease activity was associated with not commencing treatment promptly in an adjusted mix effects model, odds ratio 0.50 (95% CI 0.41 to 0.61, p <0.0001). Conclusions. We have shown that poor engagement in a national audit program correlates with the quality of care provided. Our findings support the use of data completeness as an additional service quality indicator.

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