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Integrating Nurse Preferences Into AI-Based Scheduling Systems: Qualitative Study.

Fabienne Josefine Renggli1, Maisa Gerlach1, Jannic Stefan Bieri1

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Summary
This summary is machine-generated.

Integrating artificial intelligence (AI) into nurse scheduling can improve fairness and efficiency, but human oversight is crucial. This study maps nurse preferences to AI methods, suggesting a hybrid approach for optimal outcomes in healthcare staffing.

Keywords:
AIAI-based schedulingCPLLMMIPMLNLPRLartificial intelligenceburnoutcomprehensive frameworkconstraint programmingdissatisfactionfeasibilityinterviewlarge language modelmachine learningmixed-integer programmingnatural language processingnurse schedulingreinforcement learningwell-beingwork-life balance

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Area of Science:

  • Healthcare Management
  • Operations Research
  • Artificial Intelligence

Background:

  • Nurse scheduling is a critical healthcare challenge impacting patient care and nurse well-being.
  • Traditional methods often neglect nurse preferences, leading to burnout and high turnover.
  • Inadequate scheduling practices reduce morale and negatively affect patient outcomes.

Purpose of the Study:

  • To develop a framework for integrating nurses' preferences into AI-supported scheduling.
  • To gather qualitative insights from nurses and supervisors on scheduling experiences.
  • To map these insights to suitable mathematical and AI-based scheduling techniques.

Main Methods:

  • Focus group interviews with 21 nurses, supervisors, and temporary staff in Swiss healthcare.
  • Qualitative data analysis using open and axial coding to identify key themes.
  • Mapping identified themes to AI methodologies like Mixed-Integer Programming (MIP), Constraint Programming (CP), Genetic Programming (GP), and Reinforcement Learning (RL).

Main Results:

  • Fairness and participation (85%) and flexibility/autonomy (76%) were key nurse priorities.
  • AI integration is seen as beneficial for efficiency and fairness (62%), but concerns exist regarding reliability and human oversight (38%).
  • Specific AI methods were mapped: MIP for fair allocation, CP for complex rules, GP for absences, and RL for dynamic adaptation. A preliminary MIP implementation was demonstrated.

Conclusions:

  • AI-supported scheduling can enhance fairness, transparency, and efficiency in nursing.
  • Addressing concerns about AI reliability, adaptability, and human oversight is essential.
  • A hybrid approach combining AI with human decision-making may be optimal for nurse scheduling.