Machine Learning in Healthcare Human Resources Management: A Systematic Review
DOI:
https://doi.org/10.71399/hxkd3a26Keywords:
Artificial Intelligence, Machine Learning, Human Resources in the Health Sector, Staffing and Scheduling, TurnoverAbstract
Introduction: Human resource management in healthcare faces increasing challenges related to workforce shortages, high turnover and organisational complexity.
Objectives: To critically analyse the available scientific evidence on the application of machine learning techniques in healthcare human resource management, with a focus on predicting staffing needs and turnover.
Material and Methods: A systematic literature review with narrative synthesis was conducted and reported according to PRISMA guidelines. The search was guided by the PECO framework and included studies published between 2019 and 2025 in the PubMed and Scopus databases.
Results: Four primary studies were included, using algorithms such as Random Forest, LASSO, Support Vector Machines and Boosted Trees, applied to predictive workforce planning, care model adequacy and turnover intention prediction. Overall, machine learning models showed better predictive performance than traditional statistical approaches.
Conclusions: Machine learning shows promise as a decision-support tool in healthcare human resource management; however, its application should be context-sensitive, ethically grounded and supported by more methodologically robust research.
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