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Updated: Jan 14, 2026

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task
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Optimizing Long-Term Facility Staffing With Artificial Intelligence: Aligning Care With Needs and Resources.

Abubakar Sadiq Bouda Abdulai1, Jean Storm1, Jill Manna1

  • 1Quality Insights, Inc, Charleston, WV, USA.

Journal of the American Medical Directors Association
|October 20, 2025
PubMed
Summary
This summary is machine-generated.

New Centers for Medicare & Medicaid Services staffing rules for long-term care facilities face debate. Artificial intelligence offers a data-driven solution for tailored, outcomes-based nursing staff recommendations.

Keywords:
Long-term careartificial intelligencenursing homestaffing mandatestaffing optimization

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Last Updated: Jan 14, 2026

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Published on: December 5, 2025

179

Area of Science:

  • Healthcare Management
  • Nursing Informatics
  • Artificial Intelligence in Healthcare

Background:

  • The Centers for Medicare & Medicaid Services (CMS) mandated minimum nursing staff hours and on-site registered nurse presence in long-term care facilities.
  • This regulation aims to enhance resident safety and care quality but has generated controversy regarding its practicality and uniform application.
  • Facilities face challenges balancing fixed staffing mandates with diverse resident needs and operational constraints.

Purpose of the Study:

  • To propose an artificial intelligence (AI)-assisted methodology for evaluating and recommending long-term care facility staffing levels.
  • To offer a flexible, data-driven alternative to rigid staffing mandates.
  • To support tailored, outcomes-based staffing strategies that respect facility-specific contexts.

Main Methods:

  • Utilizing existing data sources within long-term care facilities.
  • Developing an AI-driven approach to analyze resident needs and operational data.
  • Generating data-informed recommendations for optimal nursing staffing levels.

Main Results:

  • The proposed AI approach provides a framework for personalized staffing recommendations.
  • It enables facilities to align staffing with specific resident acuity and care requirements.
  • The methodology supports evidence-based decision-making for resource allocation.

Conclusions:

  • Artificial intelligence presents a viable solution to the challenges posed by uniform staffing regulations in long-term care.
  • An AI-assisted approach can facilitate adaptable, resident-centered staffing models.
  • This technology supports improved quality of care while acknowledging the operational realities of diverse long-term care settings.