Explainable AI for Equitable Nurse Scheduling: Pragmatic Pre-Post Implementation Study.
Artificial intelligence-assisted scheduling system associated with 81% drop in nurse scheduling time
Explainable AI for Equitable Nurse Scheduling: Pragmatic Pre-Post Implementation Study.
Inequitable and time-consuming shift scheduling contributes to nurse burnout, dissatisfaction, and turnover.
This study aimed to design, deploy, and evaluate a transparent, fairness-audited, explainable artificial intelligence-enabled nurse scheduling decision support system (XAI-NSDSS) to reduce administrative burden, eliminate experience-based algorithmic bias, and enhance staff acceptance in a real-world hospital setting.
A pragmatic before-after implementation study was conducted at a 671-bed teaching hospital in Taiwan (January-December 2023), involving 8 departments and 156 nurses (42 novice, 78 midlevel, and 36 experienced).
Artificial intelligence scheduling cut monthly nurse scheduling time by most
Nurse satisfaction improved from a mean of 3.2 (SD 0.8) to a mean of 4.4 (SD 0.6; P<.001), with 148 out of 156 nurses (94.9%) adopting the system by Month 3.
Preexisting experience-based bias was fully eliminated: workload coefficient of variation (coefficient of variation) decreased 50% (0.18-0.09; P<.001), disparate impact ratios normalized from 1.35-1.56 to 1.01-1.04, and preference satisfaction equity was achieved across experience tiers (ANOVA P=.38).
This study presents the first longitudinally validated explainable artificial intelligence implementation framework for nurse scheduling with formal algorithmic fairness auditing and weight sensitivity analysis.
The XAI-NSDSS framework is replicable, scalable, and provides a practical blueprint for responsible artificial intelligence adoption in health care workforce governance, with fairness guarantees that are robust to institutional customization of optimization priorities.