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Using ChatGPT‑4.0 to Address Nurse Scheduling Challenges in a Cardiology Clinic
Kimberly Rusk1, Rosemary Slotkus, Ozioma Abaribe
1Author Affiliations: Program Director and Chair (Dr Rusk), Department of Healthcare Administration; Associate Dean for Academic Affairs and Associate Professor (Dr O'Rourke), MHA Candidate (Abaribe), Department of Healthcare Administration; Loyola University Chicago, Chicago, IL; and Nurse Manager (Slotkus), Cardiology Clinic, UI Health, Chicago, IL.
Objective:
Evaluate ChatGPT‑4.0 as a decision‑support tool for an outpatient cardiology nurse scheduling problem.
Background:
Flexible schedules (4×10) may support retention but often conflict with clinic workflows and coverage needs.
Methods:
A qualitative case study used constraint mapping and iterative prompt engineering with human oversight to generate and validate schedules against role, full-time equivalent status, and clinic needs.
Results:
A hybrid 4×10/5×8 model met daily coverage 5 Outpatient Care Center (OCC), 1 Heart Center (HC), and maintained equity, but provided no redundancy for absences; human review resolved role‑specific gaps.
Conclusions:
ChatGPT can accelerate option generation and quickly solve challenges for nurse managers. Safe and effective use requires clear parameters and rigorous human validation.
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