Machine learning and artificial intelligence are becoming common place in everyday life but have been relatively slow to be adopted as a mechanism for improving healthcare. However, these techniques afford bountiful opportunity to generate insight from real-world data insight that could not be gleamed from experimental designs, therefore they warrant further investigation before being written off as ‘data dredging’.Įxploratory data analysis techniques applied to large datasets that are routinely collected as part of standard care have the potential to revolutionise healthcare. Exploratory data analysis techniques do not conform to the accepted hypothesis setting and testing associated with the traditional experimental research design within medicine and consequently, can be subject to criticism.
This project proposes to utilise exploratory data analysis techniques to investigate the underpinning contributory factors to nurse retention and ultimately patient safety across 10 acute and 10 mental healthcare providers. As part of its most recent Efficiency Research Programme, which sought to advance understanding of workforce retention in health and social care, the Health Foundation have provided funding to a team within the Centre for Social Care, Health and Related Research at Birmingham City University.
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