Workload Indicators of Staffing Need (WISN) Method for Midwives Planning and Estimation at Asrade Zewude Memorial Primary Hospital, North west Ethiopia

Background Workforce is a crucial component of the health service delivery system. Ethiopia faces health workforce challenges when it comes to evidence based health workforce planning. Workforce planning was initially determined by comparing the health worker ratio to the general population number. Later, it was determined by standard staffing schedules for each health facility level. However, neither of these methods addressed the evidence based workload variation issue among the same level facilities all around the country. A workload indicator of staff needs (WISN) method can address these variations. Therefore this research was carried on to determine workload pressure excess or gap in midwives, thereby to promote the WISN use in health facilities, based on WISN results of midwives at Asrade Zewude memorial Hospital. Methods A cross sectional study using WISN model was used to determine the workload excess and gap pressure in midwives at Asrade Zewude Memorial primary hospital, North West Ethiopia. Midwives were selected based on a priority point scale as outlined in the WISN method. Results According to the data obtained, midwives worked five days a week and 1030 h per year. This working time was spent on health service activities (58.4%), additional activities (36.6%) and support activities (5%). WISN calculations demonstrated a shortage of five midwives with WISN ratio of 0.8 at Asrade Zewude Memorial primary hospital North West Ethiopia. Conclusion Midwives at the study area were carrying on their routine tasks even though there was a staff gap of 5: thus, the midwives had a workload excess of 20%. Under these conditions, it may be hard for the facility to achieve universal health service goals. Therefore the hospital should institutionalize WISN method planning to objectively employ midwifery professionals. This study had limitations too as it used retrospective annual service statistics and small sample size which affects generalization of the results to other health facilities and other health worker cadres within the study hospital.


Introduction
All countries, regardless of their socioeconomic level, struggle about healthcare workers' education, employment, performance and retention. Health priorities of sustainable development goals cannot be achieved if not supported by health care work planning and deployment strategies [1,2].
Countries have been estimating health care workers of a health facility for a long time. The need for tools that facilitate this estimation, including objective and scientific methodologies has grown over time [3][4][5]. In order to improve health care delivery, health service managers are faced with increasing challenges in finding qualified health care workers that can meet the community expectations. As one of the six building blocks of the WHO framework for health systems, human resources for health (HRH) is one of the most critical factors for a minimum health service package delivery [6].
Organizations need to identify the most appropriate staffing levels and skill mix to ensure the most efficient use of the already limited resources. Often, this is not the case as it is not easy to find qualified health workers in specific geographical areas or health facilities where they are needed most; or in surplus where need is low resulting in inefficiencies. Poor planning and management of human resources for health contribute to these deficiencies. The required number of health workers and their professional diversity in a health facility depends on the workload and the minimum health care package of the facility [3,7].
Application of Workload Indicators of Staffing Need (WISN) method for health care worker planning and projection helps to rectify many of the observed deficiencies in access to human resource for health. This implies that a health facility has its own staffing requirement which depends on its client load. Following its development by WHO, the WISN has been used to determine staffing requirements in different countries such as Kenya, Sudan, Hong Kong, Papua New Guinea, Sri Lanka, the United Republic of Tanzania, Bahrain, Egypt, Oman and Turkey among others [3,4,6,[8][9][10][11][12][13][14][15][16][17][18]. A previous WISN method study on midwives in different parts of the world showed WISN ratio of 0.71-6.09. Workload pressure of midwives was 0.71-6.09 in the Philippines, 1.00 in Guinea and 0.83-1.83 [11,19,20] in Greece.
As a human resource management tool, WISN has significant advantages for stakeholders. Human resource managers use WISN for evidence based estimation of the health workforce. Researchers and policy makers consider recommendations for a policy change, implemented on on WISN results of health facilities [21]. The health facilities use WISN to understand the level of workload pressure, the cause of shortage or surplus and staff reassignment [20,22]. During the monitoring and the evaluation of the health system, WISN can be used for various purposes like enhancing human resources for health investment [23,24]; updating the system based on current sanctioning norms; managing task division among employees; managing health workforce; observing details in the work process that were not perceived as a source of work overload [25][26][27]. Most importantly, WISN can also be used for data quality improvement, health service quality improvement, staff satisfaction and patient satisfaction by matching each employee's skills and competences with the right tasks in order to deliver a high-quality health service [28].
Ethiopia is currently facing challenges in re-confirming health care workers' contracts for problems related to availability, quality, accessibility, acceptability and effective coverage mainly due to poor health care planning. The population ratio to health worker method and standard staffing schedule for each health facility level are no longer effective for evidence based health care worker planning. Therefore the WISN method of staff requirement to health facilities should be advocated as an option by applying it at Asrade Zewude Memorial primary hospital.

Methods
A cross sectional WISN design was used to compare the number of available midwives to the number of required midwives across the facility. Midwives have been selected based on priority score using WISN method as explained in detail in Table 1. The gap/excess and workload pressure of midwives were calculated at Asrade Zewude Memorial primary hospital, North West Ethiopia. The hospital is located in the Amhara regional state and located 410 km from Addis Ababa-the capital city of Ethiopia, 155 km from Bahir Dar-the capital city of Amhara region and 30 km from Finoteselam-the capital city of West Gojjam Zone. It has officially been opened on the 19th of September, 2015 and since then it has been serving for Sekela woreda, Womberma woreda, Bure Zuria Woreda and Bure City administration communities with the health work force of 210 employees. The selection of midwives was a priority due to current staffing problems. Their working time availability were determined using the national calendar, the personnel files and the HR regulatory documents. Workload components of midwives were determined by establishing an expert working group, by monitoring their performance and evaluating the unit and the human resources management staff. The expert group took a two hours long detailed training course, using the WISN manual.
The data used by expert working group: detailed job descriptions obtained from human resources management, annual service statistics obtained from hospital performance monitoring and evaluation unit and midwifery service activities and support activities. The workload data were cross checked from July 1, 2019-June 30, 2020 for the midwifery department using health service activity data of the health information system district's (DHS2) database.
The WISN tool uses information to determine staffing requirements in health care settings. The information includes routine activities performed by midwives at Asrade Zewude Memorial Primary Hospital (i.e., workload components), the necessary time to conduct core activities and associated activities (i.e., activity standards), working time availability in 1 year for the execution of their task and the annual service delivery statistics in the selected health service delivery point [5,21].

Determining priority cadres
The WISN tool can be used to determine the number of health care workers of all categories at any health care setting although it is not a budget-friendly option to do it all at one time. The author decided to prioritize which staff categories at Asrade Zewude Memorial Primary Hospital would be the first WISN target (see Table 1). The author preferred to start with the cadres with less complex activity standards. It is then easy to expand this to other health workers in subsequent WISN applications, after the team gained experience with the method.
The author set out priorities in such a way that all the working units have been listed with the staff categories working there. Then, the author detected the most difficult staffing problems regarding these staff cadres. By considering current and future staffing problems, the author decided which staff category (or categories) should have highest priority. Here are some questions to consider in the cadre selection process: • Which cadre faces supply problems • Which problem has highly influenced health care quality? • Which problem is more likely to have an immediate effect on health care quality? • Are any of the cadres particularly important for future health programs?

Results
Based on this study, the current employees assigned by the standard staffing schedule (stable number of health workers for the same level of health facility, locally called form 15) and the required staff based on current service standard showed a discrepancy. Number of midwives during data collection was 20 but the required number of midwives based on WISN calculation was 25 so only 80% of the staff needed was available.
The working days per week were five and the working hours per day was 7.8. The annual and other leave-days were 25 and total annual working time was 1030 h (see Table 2).
Activity standards for midwives working at Asrade Zewude Memorial Hospital were fifteen health service activities, four support activities and five additional activities (see Table 3).
Based on expert working group observation, attending labor with partograph and doing episiotomy took 12 h. Attending labor with partograph without episiotomy took 8 h. Health service activities requiring a shorter time were family planning, preparing mothers for cesarean section, immunization at birth, postnatal care and birth notification (see Table 4).
The support activities account for 5% of the total working hours of which 3% is spent on meetings (see Table 5). Midwives at Asrade Zewude Memorial Primary Hospital spend a total of 377 h. in a year for additional activities. Additional activities requiring more time were evaluating or performance appraisal of the unit, health center mentorship and preparing performance reports; reporting monthly attendances and schedule posting required a shorter amount of time (see Table 6).
Calculation based on WISN showed that about twenty five midwives were needed for health service activities, support activities and additional activities (see Table 7).

Discussion
This study demonstrated the implementation process of the WISN methodology using midwifery staff data at Asrade Zewude Memorial Hospital. According to this study, the midwives at Asrade Zewude Memorial Hospital had fifteen workload components under health service activity, six workload components under additional activity and two workload components as support activity. The results demonstrated that there were five working days with 1030 h actual working time per year for midwives at Asrade Zewude Memorial Hospital. This working time was spent on health service activities (58.4%), additional activities (36.6%) and support activities (5%).
According to the study, the traditional way of staffing method, actual number of midwives and WISN method has revealed varied results. Based on Form 15 (fixed number of health worker for the same level of health facility) the number of midwives expected was 8 and the actual number of midwives was 20 but when calculated based on available data, 25 midwives were needed based on the WISN method.
The workload pressure among midwives in the study facility was found to be a WISN ratio of 0.8. This was higher than the results obtained by a research including Ugandan midwives (0.53.-0.67), Bangladeshi nurses (0.69), Indonesia (0.64) and Indian midwives (0.57) but it is lower than those obtained by a study that included Greek midwives (1.33-1.83), Guinean midwives (1.00) and Filipino midwives (0.71-6.09) [9-11, 19, 30, 31]. The variation may be a result of sociocultural and political differences as well as facility service variations but in the case of Bangladesh results, cadre differences should be taken into consideration before interpreting the results.

Limitations
As a WISN method study, the accuracy of the result was based on the accuracy of the hospital annual service statistics (using DHIS2). However, the annual service statistics were affected by Covid-19 restrictions. Another limitation was the sample size, which focused only on midwives, making its generalizability questionable for other health departments and cadres within the same facility.

Conclusion
WISN method is useful for evidence based staff requirement estimations. It enables health facilities to work with qualified health workers based on an objective workload evidence. WISN workforce planning method had challenges related to proper documentation and data quality. Therefore institutionalizing this tool enables health facilities to focus and strengthen data quality yet another dimension of the health system. Based on the WISN output of Asrade Zewude memorial Hospital the following recommendation was forwarded to stakeholders.
• Researchers should consider further WISN study scales at a national level.
• Decision makers at regional health bureau and federal ministry of health should better institutionalize WISN method to have the right number of midwives at the right time. • Human resource department should advocate the use of WISN in the health facility.
• The planning and health information department should enhance WISN based planning as it encourages performance and data quality.