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Related Experiment Videos

Explainable AI for Equitable Nurse Scheduling: Pragmatic Pre-Post Implementation Study.

Ben-Chang Shia1, Szu-Ming Peng1, Qui-Yang Zhang1

  • 1Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City, Taiwan.

JMIR Nursing
|July 2, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces an explainable AI system for nurse scheduling, significantly reducing administrative time and errors while eliminating bias. The transparent AI approach enhances nurse satisfaction and acceptance, offering a scalable solution for healthcare workforce management.

Area of Science:

  • Healthcare Management
  • Artificial Intelligence in Healthcare
  • Nursing Workforce Optimization

Background:

  • Inequitable nurse scheduling contributes to burnout and turnover, exacerbated by rigid systems and unfair workload distribution in Taiwan.
  • Existing AI scheduling tools often lack transparency and fail to address algorithmic bias, hindering clinical adoption.
  • High annual nurse turnover (11.6%) in Taiwan necessitates innovative solutions for fair and efficient scheduling.

Purpose of the Study:

  • To design, deploy, and evaluate a transparent, fairness-audited, explainable AI-enabled nurse scheduling decision support system (XAI-NSDSS).
  • To reduce administrative burden and eliminate experience-based algorithmic bias in nurse scheduling.
  • To enhance staff acceptance and satisfaction with scheduling processes in a real-world hospital setting.
Keywords:
algorithmic fairnessdecision support systemsexplainable artificial intelligenceimplementation sciencenurse schedulingworkforce managementworkload equity

Related Experiment Videos

Main Methods:

  • A pragmatic before-after study at a 671-bed teaching hospital in Taiwan, comparing 6 months of manual scheduling with 6 months of AI-assisted scheduling.
  • Utilized an XAI-NSDSS integrating a random forest workload prediction model, Shapley Additive Explanations (SAE), and a hybrid IP+BDE optimizer.
  • Assessed outcomes using linear mixed effects models and GEE, with formal weight sensitivity analysis (WSA) for robustness.

Main Results:

  • Monthly scheduling time decreased by 81.2% and error rates by 73.8% (P<.001).
  • Nurse satisfaction significantly improved (mean 3.2 to 4.4; P<.001), with 94.9% adoption.
  • Experience-based bias was eliminated, with workload CV decreasing 50% and disparate impact ratios normalizing (P<.001).

Conclusions:

  • The XAI-NSDSS provides the first longitudinally validated framework for explainable AI in nurse scheduling with formal fairness auditing.
  • The system is replicable, scalable, and offers a practical blueprint for responsible AI adoption in healthcare workforce governance.
  • Fairness guarantees are robust to institutional customization, ensuring equitable scheduling across diverse priorities.