What can data mining tell us about patient safety? Using linear discriminant analysis to identify characteristics associated with positive safety rating in London NHS organisations
Authors: Crespo Roberto Fernández , Neves Ana Luisa , Alagha M. Abdulhadi , Leis Melanie , Flott Kelsey , Bray Owen , Fontana Gianluca , Peck Jess , Aldred Vicky , Darzi Ara 
Published: November 2021

Objective

To identify key characteristics associated with a CQC positive and negative safety rating across London NHS organisations.

Design

Advanced data analytics and linear discriminant analysis.

Data sources

Linked CQC data with patient safety variables sources from 10 publicly available datasets.

Methods

Iterative cycles of data extraction, insight generation, and analysis refinement were done and involved regular meetings between the NHS London Patient Safety Leadership Forum and analytic team to optimise academic robustness alongside with translational impact. Ten datasets were selected based on data availability, usability, and relevance and included data from April 2018 to December 2019. Data pre-processing was conducted in R. Missing values were imputed using the median value while empty variables were removed. London NHS organisations were categorised based on their safety rating into two groups: those rated as ‘inadequate’ or ‘requires improvement’ (RI) and those rated as ‘Good’ or ‘outstanding’ (Good). Variable filtering reduced the number of variables from 1104 to 207. The top ten variables with the largest effect sizes associated with Good and RI organisations were selected for inspection. A Linear Discriminant Analysis (LDA) was trained using the 207 variables. Effect sizes and confidence intervals for each variable were calculated. Dunn’s and Kruskal-Wallis tests were used to identify significant differences between RI and Good organisations.

Results

Ten variables for Good and RI NHS organisations were identified. Key variables for Good organisations included: Organisation response to address own concerns (answered by nurse/midwife) (Good organisation = 0.691, RI organisation = 0.618, P<.001); fair career progression (answered by medical/dental staff) (Good organisation = 0.905, RI organisation = 0.843, P<.001); existence of annual work appraisal (answered by medical/dental staff)) (Good organisation = 0.922, RI organisation = 0.873, P<.001); organisation’s response to patients’ concerns (Good organisation = 0.791, RI organisation = 0.717, P<.001); harassment, bullying or abuse from staff (answered by AHPHSSP) (Good organisation = 0.527, RI organisation = 0.454, P<.001); adequate materials supplies and equipment (answered by ‘Other’ staff) (Good organisation = 0.663, RI organisation = 0.544, P<.001); organisation response to address own concerns (answered by medical/dental staff) (Good organisation = 0.634, RI organisation = 0.537, P<.001); staff engagement (answered by medical/dental staff) (Good organisation = 0.468, RI organisation = 0.376, P<.001); provision of clear feedback (answered by “other” staff) (Good organisation = 0.719, RI organisation = 0.650, P<.001); and collection of patient feedback (answered by wider healthcare team) (Good organisation = 0.888, RI organisation = 0.804, P<.001).

Conclusions

Our study shows that healthcare providers that received positive safety inspections from regulators have significantly different characteristics in terms of staff perceptions of safety than those providers rated as inadequate or requiring improvement. Particularly, organisations rated as good or outstanding are associated with higher levels of organisational safety, staff engagement and capacities to collect and listen to patient experience feedback. This work exemplifies how a partnership between applied healthcare and academic research organisations can be used to address practical considerations in patient safety, resulting in a translational piece of work.