Data quality predicts care quality: findings from a national clinical audit
Data quality predicts care quality: findings from a national clinical audit
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 3 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: Six thousand two hundred five 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.
Care quality, Methodology, Missing data, National clinical audit, Rheumatoid arthritis
1-8
Yates, Mark
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Bechman, Katie
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Dennison, Elaine
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MacGregor, Alexander J.
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Ledingham, Jo
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Norton, Sam
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Galloway, James B.
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17 April 2020
Yates, Mark
c2aaab7c-fbc0-479d-a8f2-ec6649d60656
Bechman, Katie
9c3687a5-cf12-49c0-883e-7ee7456f31e6
Dennison, Elaine
ee647287-edb4-4392-8361-e59fd505b1d1
MacGregor, Alexander J.
36685a0f-6d1b-4f64-aad3-3ef40c6b8a11
Ledingham, Jo
72f75c68-4b12-4086-9d77-7db97d977e42
Norton, Sam
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Galloway, James B.
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Yates, Mark, Bechman, Katie, Dennison, Elaine, MacGregor, Alexander J., Ledingham, Jo, Norton, Sam and Galloway, James B.
(2020)
Data quality predicts care quality: findings from a national clinical audit.
Arthritis Research & Therapy, 22 (1), , [87].
(doi:10.1186/s13075-020-02179-y).
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 3 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: Six thousand two hundred five 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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Missing data paper pure submission
- Accepted Manuscript
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s13075-020-02179-y
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Accepted/In Press date: 31 March 2020
Published date: 17 April 2020
Additional Information:
Publisher Copyright:
© 2020 The Author(s).
Keywords:
Care quality, Methodology, Missing data, National clinical audit, Rheumatoid arthritis
Identifiers
Local EPrints ID: 439762
URI: http://eprints.soton.ac.uk/id/eprint/439762
ISSN: 1478-6354
PURE UUID: b7f6e58a-ccdb-4d45-ae15-0bbd05374cb9
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Date deposited: 01 May 2020 16:39
Last modified: 17 Mar 2024 02:43
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Contributors
Author:
Mark Yates
Author:
Katie Bechman
Author:
Alexander J. MacGregor
Author:
Jo Ledingham
Author:
Sam Norton
Author:
James B. Galloway
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