Analysis of Nurses’ Workload in the Inpatient Installation Using the Workload Indicator of Staffing Need (WISN) Method at Pekerja General Hospital, North Jakarta - Eka Riadi; Eddy Hermawan Hasudungan; Nurmansyah; Bambang Nurakhim; Aidah Hami (Lembaga Pengembangan Kinerja Dosen, 2025)
This study aims to analyze the workload of nurses in the inpatient installation at Pekerja General Hospital, North Jakarta, using the Workload Indicator of Staffing Need (WISN) method. The main objective is to assess whether the number of implementing nurses aligns with the standards set by the Indonesian Ministry of Health Regulation No. 340/MENKES/PER/III/2010 and to provide input regarding the ideal staffing needs for nursing services. This research applies a descriptive quantitative approach with bivariate analysis. The study sample consisted of 68 respondents, encompassing all nurses in the inpatient unit, using probability sampling techniques. Data collection was conducted through work sampling techniques and calculations based on the WISN method. The findings indicate that out of 117 beds, the Bed Occupancy Rate (BOR) ranges from 72.6% to 82.6%, which remains within the normal limit (maximum BOR >85%). The majority of respondents were female (85.2%) and had over one year of working experience. Based on the workload calculation using the WISN method, the hospital is still short of two nurses to meet optimal service requirements. The shortage of nursing staff, although small, has the potential to impact the quality of care, nurse performance, and patient satisfaction. This situation highlights the importance of accurate workload assessment and proper human resource allocation in hospital settings. Nurse managers and hospital administrators are encouraged to use the WISN method regularly as a decision-making tool for staffing adjustments. This study suggests that future research should incorporate additional variables, such as nurse satisfaction, patient acuity levels, and turnover rates, to obtain a more comprehensive understanding of staffing needs. The findings are expected to contribute to nursing human resource planning and management, especially in optimizing nurse-to-patient ratios for better healthcare delivery outcomes.