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Background and AimsConsidering the increasing use of information technology (IT) and the need of the implementation of related projects, the lack of IT specialists in the health system is one of the major challenges that require planning and foreseeing. This study was conducted with the aim of predicting the number of required IT personnel in hospitals of Isfahan University of Medical Sciences based on the modeling of identified and weighed influential factors in 2023.MethodFirst, Delphi method and multi‐criteria decision‐making (MCDM) using the Expository Posthaste Effective Resemblant Tool (ExPERT) were conducted to identify and weigh the components that affect IT staff's workload in hospitals. Then, the model for predicting the required number of IT personnel for the involved hospitals was developed. In all stages, the obtained information and results were checked and confirmed using experts' opinions in Focus Group Discussions.ResultsTwenty‐one hospitals (57%) out of 37 hospitals are facing a shortage of IT personnel. This varies from 0.5 to 1.6 personnel in different hospitals. Thirteen hospitals (35%) were reported to have adequate IT staffing and three hospitals (8%) had excess IT staffing.ConclusionThis study provided a predictive model for required IT staff in hospitals using MCDM through ExPERT which can be used in cases where the use of workload‐based methods such as Workload Indicators of Staffing Need is complex or time‐consuming.
Background and AimsConsidering the increasing use of information technology (IT) and the need of the implementation of related projects, the lack of IT specialists in the health system is one of the major challenges that require planning and foreseeing. This study was conducted with the aim of predicting the number of required IT personnel in hospitals of Isfahan University of Medical Sciences based on the modeling of identified and weighed influential factors in 2023.MethodFirst, Delphi method and multi‐criteria decision‐making (MCDM) using the Expository Posthaste Effective Resemblant Tool (ExPERT) were conducted to identify and weigh the components that affect IT staff's workload in hospitals. Then, the model for predicting the required number of IT personnel for the involved hospitals was developed. In all stages, the obtained information and results were checked and confirmed using experts' opinions in Focus Group Discussions.ResultsTwenty‐one hospitals (57%) out of 37 hospitals are facing a shortage of IT personnel. This varies from 0.5 to 1.6 personnel in different hospitals. Thirteen hospitals (35%) were reported to have adequate IT staffing and three hospitals (8%) had excess IT staffing.ConclusionThis study provided a predictive model for required IT staff in hospitals using MCDM through ExPERT which can be used in cases where the use of workload‐based methods such as Workload Indicators of Staffing Need is complex or time‐consuming.
Objectives: This study aimed at identifying the needs for the health workforce in 16 public health laboratories in the Serbian capital by assessing the workforce stock, workload activities, activity standards, and workload pressure. Methods: A review of laboratory records and regulations, interviews with key respondents, and observing work processes provided data for the World Health Organization method for determining staffing needs based on workload indicators (Workload Indicators of Staffing Need, WISN). Results: A total of 99 laboratory workers spend almost 70% of their available working time in undertaking core activities. Core activities per sample can take from 0.25 to 180 min. Laboratory workers are under moderate or high workload pressure (the WISN ratio from 0.86 to 0.50). The WISN difference indicates a shortage of 22.22% of laboratory analysts and 20.63% of laboratory technicians. To balance the staffing to workload, these laboratories need an additional 8 FTE analysts and 13 FTE technicians. They could also consider selectively reducing workload pressure by automating some of the additional activities while maintaining the competence of laboratory workers and opportunities for professional development. Conclusions: Staffing policy should account for work processes, activity standards, and workload pressure to determine necessary staffing to meet the need for laboratory services in the local context.
Sub-Saharan African countries health systems are generally faced with shortages and inequitable distribution of qualified health workers. The application of provider-population ratio or fixed staff establishments, not considering variation in workload, given contextual variations in service utilization rates, cannot adequately match the human resource needs of different health facilities. The Workload Indicators of Staffing Need (WISN) method uses workload to determine staffing needs in a given facility. The aim of this study was to assess the current workload and staffing needs of maternal and child health services in 12 primary healthcare facilities from Burkina Faso, Niger, and Cote d’Ivoire. We employed the WISN methodology, using document reviews, in-depth interviews with health providers, and observations, to obtain the data needed for estimating the required number of staff in a given facility. Then, we calculated both the WISN difference (current−required staff), and the WISN ratio (current staff/required staff). Using the WISN ratio, we assessed the work pressure that health workers experience. The results showed a shortage of health workers in most services in Cote d’Ivoire and Niger (WISN ratio <1), in contrast to Burkina Faso where services were either adequately staffed or overstaffed (WISN ratio ⩾1). The workload pressure was generally high or very high in Cote d’Ivoire, while in Niger, it was very high in maternity services but rather low in dispensary ones. There was also a geographic discrepancy in health workers staffing, rural areas services being more understaffed, with a higher workload pressure as compared to urban areas ones. This study results strengthens the body of knowledge on the shortage of health workforce in sub-Saharan Africa French speaking countries. Policies and strategies to increase students training capacities and the application of regular WISN studies for a better staff distribution are necessary to address the human resource needs of health facilities in these countries.
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