Machine Learning Models for Forecasting Employee Demand in Healthcare HR

Abstract

Purpose

This paper will analyze the major considerations of workforce demand in the healthcare setting and assess the success of machine learning models in enhancing workforce demand prediction accuracy. It involves exploration on how operational variables and data-driven practices can help in improving human resource planning.

Design/Methodology/Approach

A quantitative research design was considered with the help of a structured questionnaire to gather primary data among the healthcare professionals and HR people. To test the proposed hypotheses and investigate relationships between variables, statistical methods, such as reliability analysis, descriptive statistics, correlation analysis and regression analysis, were used.

Findings

The findings suggest that the rate of patient inflow, the emergency and the staff absenteeism has a crucial impact on the accuracy of workforce demand forecast. The use of machine learning was the most influential factor with a high positive predictive performance impact. Also, the use of HR data was discovered to improve decision-making processes. The proposed framework was statistically supported with all hypothesized relationships being validated, which confirms the robustness of the proposed framework.

Practical Implications

The study highlights the importance of adopting machine learning and data-driven HR practices in healthcare organizations. Integration of predictive models and data usage optimization will help healthcare institutions to increase workforce planning, decrease inefficiencies, and positively influence patient care outcomes.

Originality/Value

This study adds to the body of literature since it narrows down to the specific topic of workforce demand forecasting in healthcare through machine learning. It offers a combined framework integrating operation and organizational factors, which bridges a gap in human resource management research.

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