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Nursing Workload and Optimal Staffing in Medical and Surgical Wards: A Time-Motion Study Using a Patient
Muzelfe Biyik1, Ensar Durmuş2, Perihan Ersoy3
1Department of Nursing, Kutahya Health Sciences University, Kutahya, Türkiye.
Background:
Evaluating the nursing workforce solely on the basis of nurse numbers leads to the neglect of critical workload determinants, such as indirect nursing activities and care intensity. This limitation may result in inaccuracies in workforce planning and pose risks to the quality of care. The combined use of time-motion analysis and patient classification systems enables a more comprehensive and objective assessment of nursing workload.
Aim:
This study aimed to analyse the workload of nurses working in the chest diseases and urology wards of a teaching and research hospital in Türkiye using time-motion analysis and to determine the optimal number of nurses (ONN) based on a patient classification system.
Methods:
This study employed a cross-sectional, observational design based on time-motion analysis. Between June and July 2025, the workload of nurses working in the chest diseases and urology wards of a tertiary teaching hospital in Türkiye was evaluated. A total of 20 nurses were observed during morning and evening shifts over a 5-week period. The ONN was calculated using three key indicators derived from the patient classification system: patient classification score, nursing intensity coefficient, and total care time.
Findings:
The calculated ONN was consistent with the current staffing levels in both wards. Higher care intensity (nursing intensity score: 9.44) in the chest diseases ward and patient volume (patient classification score: 55.75) in the urology ward emerged as the primary determinants of workload. Time-motion analysis showed that 22.8%-26.6% of nurses' working time was allocated to direct care, 31.1%-32.9% to indirect care, and 34.6%-42.3% to personal tasks. Direct care time per patient was 12.60, 18.77, and 34.52 min for Group 1, 2, and 3 patients in the chest diseases ward and 11.18, 21.91, and 36.89 min, respectively, in the urology ward.
Results:
These findings indicate that nursing workload is shaped by different dynamics across clinical settings and that nurses allocate a limited proportion of their working time to direct patient care. The combined use of time-motion analysis and a patient classification system highlights the need for more accurate nurse staffing planning and workflow redesign to increase direct care time.
Implications For Nursing Management:
The findings provide nurse managers with an evidence-based approach to assess nursing workload by integrating time-motion analysis, patient classification, and unit-specific characteristics into staffing decisions. They can also use these findings to redesign workflows, improve documentation and medication-preparation processes, and routinely monitor unit-specific workload while preserving nurses' essential rest and recovery activities.
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