Optimising Nurse–Patient Assignments: The Impact of Machine Learning Model on Care Dynamics—Discursive Paper

Abstract

BACKGROUND: Machine learning (ML) models can enhance patient-nurse assignments in healthcare organisations by learning from real data and identifying key capabilities. Nurses must develop innovative ideas for adapting to the dynamic environment, managing staffing and establishing flexible workforce solutions. AIM: This discursive paper discusses the application of ML in optimising patient-nurse assignments within healthcare settings, considering various factors such as staff skill mix, patient acuity, cultural competencies and language considerations. METHODS: A discursive approach was used to optimise nurse-patient assignments and the impact of ML models. Through a review of traditional and emerging perspectives, factors such as staff skill mix, patient acuity, cultural competencies and language-related challenges were emphasised. RESULTS: Machine learning models can potentially enhance healthcare patient-nurse assignments by considering skill integration, acuity level assessment and cultural and language barrier awareness. Thus, models have the potential to optimise patient care through dynamic adjustments. CONCLUSION: The application of ML models in optimising patient-nurse assignments presents significant opportunities for improving healthcare delivery. Future research should focus on refining algorithms, ensuring real-time adaptability, addressing ethical considerations, evaluating long-term patient outcomes, fostering cooperative systems, and integrating relevant data and policies within the healthcare framework. No patient or public contribution.

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