Business and Social Sciences
Business and social sciences | Online ISSN 3067-8919
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Volume 2 Number 1 2024
RESEARCH ARTICLE (Open Access)
Intelligent Scheduling Systems and Healthcare Workforce Efficiency: A Survey-Based Study of 155 U.S. Healthcare Professionals
Mithra Rani Hur 1*
Business and Social Sciences 2 (1) 1-9 https://doi.org/10.25163/business.2110874
Submitted: 30 September 2024 Revised: 06 December 2024 Accepted: 11 December 2024 Published: 14 December 2024
Abstract
Background: Healthcare organizations depend on effective workforce scheduling to sustain quality care, yet conventional scheduling approaches frequently produce uneven workloads, administrative strain, and diminished staff morale. Whether intelligent, algorithm-driven scheduling systems meaningfully address these problems remains only partially understood.
Methods: A cross-sectional online survey was administered to 155 U.S. healthcare workers, including physicians, nurses, allied health professionals, and administrative staff. A structured questionnaire using five-point Likert-scale items assessed Intelligent Scheduling Accuracy (ISA), User Satisfaction (US), Ease of Use (EOU), System Reliability (SR), and Healthcare Workforce Efficiency (HWE). Data were analyzed in SPSS (version 26) using descriptive statistics, reliability analysis, Pearson correlation, and multiple linear regression.
Results: All constructs were rated favorably, with mean scores ranging from 4.05 to 4.21. HWE showed the strongest correlation with ISA (r = 0.703), followed by US (r = 0.681), EOU (r = 0.642), and SR (r = 0.617). Internal consistency was strong across constructs (Cronbach’s α = 0.873–0.915), and the regression model explained 61.0% of the variance in workforce efficiency (R² = 0.610, F = 58.74, p < .001), with ISA emerging as the strongest individual predictor (β = 0.341).
Conclusion: Intelligent scheduling systems appear to be positively associated with healthcare workforce efficiency, largely through the combined influence of scheduling accuracy, usability, and reliability. These findings, while preliminary, offer empirical support for continued investment in intelligent scheduling technologies within U.S. healthcare settings.
Keywords: intelligent scheduling systems; healthcare workforce efficiency; user satisfaction; system reliability; healthcare workforce management
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