Machine Learning Applied to NHS Electronic Staff Records Identifies Key Areas of Focus for Staff Retention

Rupert Milsom, Magdalena Zasada, Cath Taylor and Matt Spick ()
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Rupert Milsom: Ashford and St. Peter’s Hospitals NHS Foundation Trust, Ashford TW15 3AA, UK
Magdalena Zasada: School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XH, UK
Cath Taylor: School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XH, UK
Matt Spick: School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XH, UK

Administrative Sciences, 2025, vol. 15, issue 8, 1-12

Abstract: Background : In this work, we examine determinants of staff departure rates in the NHS, a critical issue for workforce stability and continuity of care. High turnover, particularly among clinical staff, undermines service delivery and incurs substantial replacement costs. Methods : Here, we analyse a unique dataset derived from Electronic Staff Records at Ashford and St. Peter’s NHS Foundation Trust, using a machine learning approach to move beyond traditional survey-based methods, to assess propensity to leave. Results : In addition to established predictors such as salary and length of service, we identify drivers of increased risks of staff exits, including the distance between home and workplace and, especially for medical staff, cost centre vacancy rates. Conclusions : These findings highlight the multifactorial nature of staff retention and suggest the potential of local administrative data to improve workforce planning, for example, through hyperlocal recruitment strategies. Whilst further work will be required to assess the generalisability of our findings beyond a single Trust, our analysis offers insights for NHS managers seeking to stabilise staffing levels and reduce attrition through targeted interventions beyond pay and tenure.

Keywords: retention planning; machine learning; training; staff absence; predictors; NHS (search for similar items in EconPapers)
JEL-codes: L M M0 M1 M10 M11 M12 M14 M15 M16 (search for similar items in EconPapers)
Date: 2025
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