Assessment of staffing need through a workload analysis in Jhenaidah and Moulvibazar, Bangladesh: a Workload Indicator of Staffing Need (WISN) study

Background: Improvement of health workers' performance is vital for the improvement of health-care service delivery, and workload management is an important factor in improving performance. This study aimed to assess the current workload and staffing need for delivering optimum health-care services in the public-sector district health system in Bangladesh. Methods: Between July 2, 2017, and Nov 30, 2017, we followed the steps of WHO's Workload Indicator of Staffing Need (WISN) methodology. We combined qualitative (document reviews, key informant interviews, in-depth interviews, observations) and quantitative methods (time-motion survey), to collect data in 24 health facilities from district hospitals up to community clinics in Jhenaidah and Moulvibazar districts, Bangladesh. The study included physicians, nurses, medical assistants, family welfare visitors, community care providers, and family welfare assistants. Workload components were defined on the basis of inputs from experts (n=5), refined further by actual service providers (n=87). We used WHO WISN software to calculate standard workload, category allowance factor, individual allowance factor, total required number of staff, WISN difference, and WISN ratio. Findings: Seven of the 20 staff categories had a very high workload (WISN ratio 0·30–0·49), and five staff categories had extremely high workload (0·10–0·29) indicating an overall high workload in the service providers studied. Medical consultants had the highest workload (WISN ratio 0·16), followed by other specialists. The staff category with the most number of positions needing to be filled was nurses at district hospitals (a mean of 136 vacant positions per facility), followed by general physicians (35). We noted that nurses were predominantly occupied with support activities (60% in the case of district nurses and 50% in sub-district facilities), instead of actual nursing care. If the vacant posts were filled, the workload for existing workers is reduced. However, simply filling the vacant posts would not be sufficient to reduce staff workload in some staff categories, such as nursing. Interpretation: WISN method of estimating workload and staff requirements can aid policymakers in making the best uses of existing human resources. Thus, WISN should be incorporated as a planning tool for managers at the district level. Implementation research should be carried out on how the workload-based staffing decisions can be effectively integrated into health systems. Funding: WHO Bangladesh