Objectives Nursing is a safety critical activity but not easily quantified. This makes the building of predictive staffing models a challenge. The aim of this study was to determine if relationships between registered and non-registered nurse staffing levels and clinical outcomes could be discovered through the mining of routinely collected clinical data. The secondary aim was to examine the feasibility and develop the use of ‘big data’ techniques commonly used in industry for this area of healthcare and examine future uses.
Setting The data were obtained from 1 large acute National Health Service hospital trust in England. Routinely collected physiological, signs and symptom data from a clinical database were extracted, imported and mined alongside a bespoke staffing and outcomes database using Mathmatica V.10. The physiological data consisted of 120 million patient entries over 6 years, the bespoke database consisted of 9 years of daily data on staffing levels and safety factors such as falls.
Primary and secondary outcomes To discover patterns in these data or non-linear relationships that would contribute to modelling. To examine feasibility of this technique in this field.
Results After mining, 40 correlations (p<0.00005) emerged between safety factors, physiological data (such as the presence or absence of nausea) and staffing factors. Several inter-related factors demonstrated step changes where registered nurse availability appeared to relate to physiological parameters or outcomes such as falls and the management of symptoms. Data extraction proved challenging as some commercial databases were not built for extraction of the massive data sets they contain.
Conclusions The relationship between staffing and outcomes appears to exist. It appears to be non-linear but calculable and a data-driven model appears possible. These findings could be used to build an initial mathematical model for acute staffing which could be further tested.
- data mining
This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Statistics from Altmetric.com
Contributors AL contributed to study design, interpretation, analysis and writing. RC contributed to analysis, interpretation, visualisation and writing. SJ contributed to project management, data extraction, permissions, interpretation, analysis and writing. JS contributed to data collection and interpretation. MG contributed to interpretation. EM contributed to study design and interpretation. GP contributed to analysis writing. MR contributed to study design interpretations, analysis and permissions.
Funding This work was funded by NHS England, Compassion in Action Programme.
Competing interests None declared.
Ethics approval Birmingham City University's Faculty Academic Ethics Committee (Faculty of Health, Education and Life Science).
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement This work is secondary data analysis. Anonymised data available from the host Trust. Analysis of secondary data and all results from the authors.
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.