An observational study on the rate of reporting of adverse event on healthcare staff in a mental health setting: An application of Poisson expectation maximisation analysis on nurse staffing data. (June 2020)

• Record Type: Journal Article
• Authors: Cook, Robert M; Jones, Sarahjane; Williams, Gemma C; Worsley, Daniel; Walker, Ray; Radford, Mark; Leary, Alison
• Other Names: Bian Jiang guest-editor.; Zhang Yaoyun guest-editor.; Tao Cui guest-editor.
• Is Part Of: Health informatics journal. Volume 26:Number 2(2020)
• Page Start: 1333
• Page End: 1346
• Publication Date: 2020-06
• Subjects: count regression -- nurse staffing -- retention -- safety -- workforce; Medical informatics -- Periodicals; 610.285
• DOI: 10.1177/1460458219874637
• Languages: English
• ISSNs: 1460-4582

Abstract

Evidence highlights the intrinsic link between nurse staffing and expertise, and outcomes for service users of healthcare, and that workforce retention is linked to the clinical and organisational experiences of employees. However, this understanding is less well established in mental health. This study comprises a retrospective observational study carried out on routinely collected data from a large mental healthcare provider. Two databases comprising nurse staffing levels and adverse events were modelled using latent variable methods to account for the presence of multiple underlying behaviours. The analysis reveals a strong dependence of the rate of adverse events on the location and perceived clinical demand of the wards, and a reduction in adverse events where registered nurses exceed 'clinically required levels'. In the first study of its kind, these findings present significant implications for nursing workforce policy and present an opportunity to not only improve safety but potentially impact nurse retention.