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Development of a Data Model to Predict Nursing Workload Using Routine Clinical Data.

Dirk Hunstein1, Lena Frischen2, Madlen Fiebig3

  • 1CEO, ePA-CC GmbH, Wiesbaden, Germany.

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|August 23, 2024
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Summary
This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) can predict nursing workload. The Self Care Index (SPI) effectively identifies staffing needs, improving healthcare resource management.

Keywords:
Clinical Decision SupportMachine LearningNursing WorkloadPrediction ModelSelf-Care Index SPIStaff ManagementepaAC

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Area of Science:

  • Healthcare Management
  • Artificial Intelligence in Healthcare
  • Nursing Informatics

Background:

  • Effective human resource management in nursing is crucial for high-quality patient care.
  • Staffing levels are directly influenced by patient health status and nursing workload.
  • Current methods for determining staffing needs often lack data-driven precision.

Purpose of the Study:

  • To develop and validate an AI/ML-based approach for predicting nursing workload.
  • To identify key predictors of nursing workload from routine clinical data.
  • To provide data-driven recommendations for optimal nursing staffing levels.

Main Methods:

  • A multi-center study involving data from three hospitals.
  • Utilized artificial intelligence (AI) and machine learning (ML) algorithms.
  • Identified the Self Care Index (SPI), derived from the nursing assessment tool epaAC (nursing assessment tool for AcuteCare), as a primary predictor.

Main Results:

  • The SPI alone explained 40% to 66% of the variance in nursing minutes (adjusted R²).
  • Incorporating additional predictors like "fatigue" and "pain intensity" increased explanatory power by up to 17%.
  • A predictive model was established for data-based personnel controlling.

Conclusions:

  • AI and ML models can accurately predict nursing workload based on routine data.
  • The SPI is a strong, validated predictor of nursing workload.
  • This approach provides a foundation for data-driven, AI-powered nursing staff management and resource allocation.