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Hidden labour: the skilful work of clinical audit data collection and its implications for secondary use of data via integrated health IT

McVey, L., Alvarado, N., Greenhalgh, J. , Elshehaly, M. ORCID: 0000-0002-5867-6121, Gale, C. P., Lake, J., Ruddle, R. A., Dowding, D., Mamas, M., Feltbower, R. & Randell, R. (2021). Hidden labour: the skilful work of clinical audit data collection and its implications for secondary use of data via integrated health IT. BMC Health Services Research, 21(1), article number 702. doi: 10.1186/s12913-021-06657-0

Abstract

Background
Secondary use of data via integrated health information technology is fundamental to many healthcare policies and processes worldwide. However, repurposing data can be problematic and little research has been undertaken into the everyday practicalities of inter-system data sharing that helps explain why this is so, especially within (as opposed to between) organisations. In response, this article reports one of the most detailed empirical examinations undertaken to date of the work involved in repurposing healthcare data for National Clinical Audits.

Methods
Fifty-four semi-structured, qualitative interviews were carried out with staff in five English National Health Service hospitals about their audit work, including 20 staff involved substantively with audit data collection. In addition, ethnographic observations took place on wards, in ‘back offices’ and meetings (102 h). Findings were analysed thematically and synthesised in narratives.

Results
Although data were available within hospital applications for secondary use in some audit fields, which could, in theory, have been auto-populated, in practice staff regularly negotiated multiple, unintegrated systems to generate audit records. This work was complex and skilful, and involved cross-checking and double data entry, often using paper forms, to assure data quality and inform quality improvements.

Conclusions
If technology is to facilitate the secondary use of healthcare data, the skilled but largely hidden labour of those who collect and recontextualise those data must be recognised. Their detailed understandings of what it takes to produce high quality data in specific contexts should inform the further development of integrated systems within organisations.

Publication Type: Article
Additional Information: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Publisher Keywords: Clinical audit, Interoperability, Data collection, Data quality, Electronic patient records, Registries
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > R Medicine (General)
Departments: School of Science & Technology > Computer Science
SWORD Depositor:
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