A STUDY ON UTILIZING DATA DRIVEN OPTIMIZING STAFFING LEVEL IN MATHURAM HOSPITAL IN TRICHY
Keywords:
Hospital staffing- Optimization-Data-driven approaches-Patient demand-Resource allocation-Operational efficiency-Patient care- Staff satisfaction- Workload distribution-Staff burnout Quality of care-Strategic staffing- Performance improvement-Staffing models.Abstract
This study explores the use of data-driven methodologies to optimize staffing levels in hospitals. By leveraging advanced analytic and machine learning algorithms, hospitals can effectively forecast patient demand, allocate resources efficiently, and ensure adequate staffing levels across various departments. This abstract highlights the significance of adopting innovative techniques
to enhance patient care, improve staff satisfaction, and optimize operational efficiency within healthcare facilities. Balancing Patient Needs and Staffing Resources: A Framework for Optimal Hospital Staffing Levels. Achieving optimal staffing levels in hospitals requires a delicate balance between meeting patient needs and efficiently allocating staffing resources. This abstract presents a comprehensive framework that integrates patient acuity, workload distribution, and staff skill mix to determine the ideal staffing levels across different units within a hospital setting. By implementing this framework, healthcare institutions can enhance patient outcomes, mitigate staff burnout, and optimize resource utilization, ultimately improving overall quality of care.
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References
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Cavouras, C. A. (2002). Nurse staffing levels in American hospitals: A 2001 report. Journal of Emergency Nursing, 28(1), 40-43.
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