The Utilisation of Workload Indicators Staffing Need Method in Determining Staffing Requirements and Workload in JAAFH Hospital

The foundation of a robust healthcare system lies in a strong and effective health workforce (HW) within health organisations [1‒3]. The availability of a skilled, sufficient, and efficient HW can be easily disrupted by increased demand for healthcare services. Therefore, the goal of HW management is to establish a working environment with the “right number of people with the right skills in the right place at the right time with the right attitude doing the right work at the right cost with the right work output” [4]. The importance of HW [5] highlights the need to manage recruitment, training, retention and allocation. In addition, investing in HW impacts areas such as education and expanding job prospects [5]. HW is the core that sustains the functionality of healthcare systems, ensuring that healthcare services are extended to a broader public and upholding the fundamental principle that everyone is entitled to the highest attainable standard of health. However, accessibility, acceptability, and quality of healthcare are intrinsically linked to the availability and distribution of healthcare professionals [6, 7]. Therefore, understanding the HW demand and aligning it with the actual workload is paramount in understanding the HW and ensuring uninterrupted services. For example, the COVID-19 pandemic has impacted healthcare systems worldwide, which required intervention in HW management [8‒10]. Due to the sudden emergence of COVID-19, HW had to be restructured to adapt to the surge in healthcare services [11]. Diagnosis and follow-up of COVID-19 require radiological examination, performed either by chest radiographs, a chest CT scan or ultrasonography [12]. Other factors that have increased workload were related to precautions taken to mitigate transmission [11, 13, 14].

The WISN, which was developed by the World Health Organisation (WHO), is one of the prominent approaches to understanding staffing needs and workload challenges [4]. WHO developed the WISN method in 1998 to calculate the required workforce for a department to function optimally [4]. The WISN method is simple to implement, using readily available data that have already been collected. It can be applied across all levels of healthcare services for staffing decisions while remaining acceptable to health service managers. Therefore, for the reasons metioned above, in addition to the endorsement of WHO, WISN was chosen as a measure of workload in healthcare. Numerous studies have harnessed the power of the WISN method to gain insights into the staffing needs and workload challenges faced by healthcare professionals [15‒19]. However, in the field of radiology, the application of WISN remains relatively limited.

JAAFH is a hospital that offers healthcare to both military members and their families. It documented over 125,000 outpatient visits during 2023 alone, hence, why is a competent radiology department deemed necessary. Therefore, examining the current workload and staffing needs in the radiology department would enable hospital management to understand the staffing needs and make informed decisions regarding staff and resource utilisations. To date, no study has investigated the workload and staffing needs in JAAFH or any other hospital in Kuwait. Therefore, this study focuses on whether the current staffing and workload of radiologists and radiology technicians in JAAFH in Kuwait can meet the demands of providing quality radiological services. This study aims to assess the workload and staffing needs of the radiology department in JAAFH by utilising the WISN method. The WHO’s WISN was used to compute the staff excesses or shortages at the radiology department of JAAFH. This research endeavour holds the promise of contributing to the knowledge surrounding HW planning and improving the quality of care delivered to the military personnel and beneficiaries served by the institution.

The WISN method was used to establish a steering committee, technical team, and expert group, followed by training workshops on the WISN method for the research team. The steering committee comprised several authors and members of the radiology department who administered the WISN and monitored progress. The technical team included a senior radiologist, authors, and additional members of the radiology department who supervised data collection. The expert group consisted of a senior radiologist and radiographers from the department.

The research team obtained data on available staff time (radiographers, junior and senior radiologists), the workload components, and time spent on each activity during work. The data were obtained by reviewing the departmental data archive, manpower directorate, and Radiology Information System (RIS). The archive and the directorate provided information on the public holidays, annual leaves, annual numbers of maternity leaves and workers salaries, while the RIS was used to retrieve data on the procedure types and numbers. Collected data were grouped on a monthly basis, outliers were identified, and then data were validated by the research group before data input into Microsoft Excel. Ethical approval was obtained, and informed consent was not required following local/national guidelines. The research adhered to data protection regulations and ensured the anonymity and confidentiality of data collection and storage.

A = number of working days per year.

B = public holidays days in a year.

C = annual leave days in a year.

D = sick leave days in a year.

E = other possible days off in a year for other reasons.

F = the number of working hours in one day.

The next step is defining the workload components. Components of the workload consist of tasks performed by each staff category and are categorised into three types: health service activity (A), performed by all staff members, and data are collected annually; support activity (B), performed by all staff members and data, and no data are collected; and additional activities (C), activities performed by specific staff members. The following step is obtaining activity standards, indicating the time required for a trained worker to complete an activity to a professional standard in local circumstances. These standards are represented as time units divided into service and allowance standards. Service standards are for health service activities, and allowance standards are for support and additional activities. There are two types of allowance standards: the category allowance standard (CAS), which is related to support activities that all staff perform, while the individual allowance standard (IAS) is related to additional activities performed by specific staff.

Corrective measures were implemented regarding the services provided in the department. AWT was adjusted by adding 15 min to working hours and shifting some tasks from senior radiologists to junior radiologists, and a mammogram service was added to the department. The final step of WISN is analysis and interpretation of WISN results by providing ratios as an indicator for workload and staffing needs, where a WISN ratio of 1 shows a balance in workload and staffing, ratios of >1 indicate overstaffing, and <1 indicate understaffing.

Data collection was performed between August and October 2022. Following the training of the core staff in the radiology department, the WISN tool has been applied to radiographers and junior and senior radiologists. Data were collected, aggregated, analysed, and verified. The AWT, activity standards, standard workload, CAS, CAF, IAS, and IAF were obtained.

Table 1 demonstrates the current AWT and the future of staff in the department after an anticipated corrective measure that the department management board will take. The table also shows the working hours as a percentage of each category that works per year currently and in the future. Results show that junior radiologists work the longest hours (1,062 per annum, 58%); on the other hand, senior radiologists work the shortest hours (1,002 per annum, 55%). Furthermore, all AWTs are predicted to increase for all staff, with junior radiologists having the most significant increase in AWTs as a percentage (+5%), while radiographers and senior radiologists increased (+1%) and (+3%), respectively. According to our data, junior radiologists had fewer sick leave days. This lower rate of absence translates into a higher proportion of their time being available for work.

Table 2 shows WISN results in calculating the required staff based on the current package of services provided compared to the current staff and staff needed after the corrective measures, i.e., the hypothetical adjustment of the AWT and the anticipation of the workload following the addition of a planned extra service (mammogram). Results show that currently, based on the services provided, junior radiologists and senior radiologists need a 150% increase (4–10) and 38% (8–11) of staff to compensate for the workload, while radiographers will require a 69% reduction of staff (54–17). Furthermore, factoring the mammogram in, staff requirements grow even more for junior radiologists (+250%) and senior radiologists (+75%), while radiographers must only be reduced to 57% of staff.

Table 3 shows the WISN ratio for the three cadres (currently and in the future) after introducing the mammogram service. There is a shortage of both junior and senior radiologists (current and future), while data show an excess of radiographers. To determine the severity of the shortage of junior and senior radiologists and whether the cadres can cope with the extra workload, the WISN ratio was converted into a percentage of workload pressure, as shown in Table 4. The workload pressure as a percentage has increased for both cadres in the future (on anticipating the introduction of the mammogram service) compared to the current situation.

Figure 1 shows a comparison between the current and future required staff. The junior radiologists spent most of their time in group (A) activities, while the senior radiologists spent more than 25% of their time, both currently and in the future, in additional activities (C), such as teaching and postgraduate board meetings. Regarding the radiographers, they spent most of their current time in group (A) activities, while in the future, they will spend 12% of their time in group (B) activities.

We sought to assess workload and staffing needs of the radiology department in JAAFH using the WISN method. The main findings of this study are twofold. First, AWT was predicted to increase for all staff in the radiology department, with junior radiologists having the biggest increase in AWT (+5%). Second main finding, there are shortages of junior and senior radiologists; adding a new service will only exacerbate these shortages. On the other hand, results indicate a surplus of radiographers and adding a new service will not affect the current workload of radiographers. This study attempted to demonstrate the implementation of WISN in JAAFH and provide the necessary steps for other departments in JAAFH to follow.

HW remains the cornerstone to the provision of optimal healthcare services [20]. Health policymakers are tasked with providing an environment where staff can flourish and deliver optimal healthcare services. As mentioned earlier, the goal is to provide sufficient number of staff at the right time, with the right attitude and skills [4]. Therefore, WISN can be helpful for policymakers in directing and managing load and recruitment. The WISN method has been applied in the JAAFH radiology department as a pilot, and it will offer guidance on implementation to other departments in JAAFH. The results focused on measuring the current WISN ratio, workload pressure, and the proportion of time distributed across workload components, as well as for the planned future service that will be introduced (mammogram).

All three cadres in the radiology department work less than 7 h per day even after implementing corrective measures, i.e., increasing the AWT. This is due to the entitled radiation protection leave (15 days per year) in addition to the annual leave. It is worth mentioning that all government institutions work either 7 h or more per day. As results show, junior radiologists have the highest AWT per year (58%), while senior radiologists have the least AWT per year due to the annual number of sick leaves. Senior radiologists spent more time in group C activities compared to junior radiologists, who were not engaged in any C activity. This also needs some revision of senior radiologist’s role and whether they can delegate tasks to junior radiologists.

WISN analysis revealed a shortage of both senior and junior radiologists in the JAAFH radiology department. The WISN ratio for both senior and junior radiologists was 0.7 and 0.4, respectively. Policymakers in JAAFH should consider these significant shortages disadvantageous to the service provided in JAAFH. Early intervention and management is advisable since consequences of these shortages can result in poor imaging results or long waits for imaging procedures [21]. Moreover, increased workload, given the shortages, can result in staff burnout and stress. Despite the increase of AWT for all three cadres, the WISN ratio has decreased for all staff members. This indicates that the introduction of the new service will increase demand for junior and senior radiologists. Adding the new service will require an increase of at least 250% of junior radiologists to meet demand. To mitigate the current stress on radiologists, the management board might need to review their current job description and explore the possibility of task sharing, shifting, or possibly assigning new responsibilities. These solutions should be worked out in conjunction with prioritising the recruitment of new junior radiologists to make up for the 250% increase in staff needed to provide the new service. Although there are budget constraints, reallocating funds from other departments and raising this to the management board or maintaining phased recruitment would be advantageous.

On the other hand, data showed surplus in radiographers (WISN ratio 3.2). Surpluses can influence healthcare services and have financial consequences [22]. For instance, excess in radiographers can strain the budget allocated to the department, and these funds that are paid for overstaffing can be managed wisely and used for other purposes that could serve the department [22]. Furthermore, surpluses represent underutilised manpower, which results in a reduced workload, and efficiency and can negatively affect satisfaction in the long term [3], can affect staff satisfaction. Therefore, managing surplus of radiographers would further enhance the service provided by the department. Management of radiographers surplus can be achieved by reallocating them to new services, such as mammography, or to high-demand areas where staff are overloaded. Another management strategy for surpluses is to upskill staff in advanced radiology modalities that the department lacks, which would provide expertise and reduce job loss. While contextual differences limit direct comparisons, our results disagree with WISN studies that were conducted in Oman (WISN ratio = 0.8) [19], which reported deficiencies and Indonesia (WISN ratio = 1) [23], which reported adequate staffing needs in the radiology department. To avoid the reasons that are caused by surpluses, using WISN as a measurement tool would help predict the current and future staff requirements to reach the optimal staff required in the department.

To date, this is the first study that utilised WISN to assess department workload and staffing needs in Kuwait, and direct comparisons with results from the international literature are not possible due to obvious reasons (heterogeneity in healthcare standards and location). However, WISN method has been widely used and implemented to investigate staffing requirements and workload levels in healthcare settings [3, 17‒19, 24‒26]. Therefore, successfully implementing this tool in the JAAFH hospital should provide the first step to further implementation in other departments and other hospitals in Kuwait.

A limitation associated with this study is the accuracy of the data collected, which depends on the accuracy of data collection and processing in the radiology department. For instance, missing data, such as sick leaves can lead to over- or underestimation of staff needs. Future studies should address this limitation by conducting data audits and cross-checking with health systems and other manual staff logs available in the department. Based on the findings of this study, several recommendations proposed for development of the radiology department at JAAFH. Firstly, recruiting radiologists, particularly junior radiologists, should be prioritised to avoid service disruption and work overload. Secondly, to address current staffing limitations in the department, restructuring the workload and managing productivity might help avoid the deterioration of the service and increase the waiting times due to the limited staff available. The department should reassess the staffing needs again after any changes in demand or recruitment to understand how changes have affected the needs and workload of the department.

The WISN method proved effective in analysing workload dynamics among cadres working in the same department. It also assisted in determining future staffing based on projected scenarios. It also showed its utility in providing evidence-based solutions and recommendations for better planning and management of the HW. The calculation and interpretation of workload pressure and time allocation in each workload component permitted a better understanding of the work context and dynamics. These insights will better position health policymakers to undertake informed decisions based on the WISN results to ensure fair distribution of work among different cadres appropriate delegation of activities. The adoption of WISN at a national level are profound implications. Standardisation of WISN among national hospitals can optimise workforce planning and effectively meet staffing needs required for the Ministry of Health’s vision for 2030.

The Ministry of Defence Scientific Research Committee approved the research on July 27, 2022. Written informed consent from participants was not required in accordance with local/national guidelines.

The authors declare no conflict of interest.

This study was not supported by any sponsor or funder.

Conceptualisation: Faisal Albanwan, Nazar Mohamed, and Abdulaziz Alhenaidi. Data curation: Faisal Albanwan, Mohammed Emad AlSabti, and Ammar Osman. Formal analysis: Faisal Albanwan and Nazar Mohamed. Methodology: Faisal Albanwan, Nazar Mohamed, and Sultan Alsalahi. Project administration: Faisal Albanwan, Nazar Mohamed, Abdulaziz Alhenaidi, and Sultan Alsalahi. Software, visualisation, validation, resources, funding acquisition, and investigation: not applicable. Supervision: Faisal Albanwan and Abdulaziz Alhenaidi. Writing – original draft: Faisal Albanwan, Nazar Mohamed, Sultan Alsalahi, Limya Alaradi, and Abdulaziz Alhenaidi. Writing – review and editing: Sultan Alsalahi and Abdulaziz Alhenaidi.

The datasets generated or analysed during the study are included in this published article. Further enquiries can be directed to the corresponding authors.