Determining Healthcare Workforce Requirements for Kuluva Hospital in West Nile-Uganda, using the Workload Indicators of Stang Need (WISN)

Health workforce shortage is a major threat to global public health with a greater implication for low-resourced countries. The right placement of the available staff in many health facilities remains a challenge due to inadequate information on exact workload and work pressure that staff undergo in course of work. This study aimed to determine the need for key health workforce cadre in Kuluva hospital using Workload Indicators of Stang Need (WISN) methodology. The study followed a predominantly quantitative approach of Workload Indicator Stang Needs (WISN) methodology. We held a meeting with hospital management to understand policy issues and procedures. The key staff were interviewed in departments, available records reviewed, practices observed to establish the available working time, activity standards and time taken to perform other supportive activities. Service statistics was generated from HMIS data of 2016/17. Data was analyzed manually using calculator and Microsoft Excel spreadsheet.


Results
All cadre categories had the same available working time of 1,504 hours in a year with 105 staff of the studied cadres required to perform all activities in Kuluva hospital based on WISN calculation. Although overall work pressure was 30%, 5 out of 7 staff cadre categories experienced work pressure of varying degrees -medical o cers (70%), laboratory staff (70%) and clinical o cers (60%) were most affected compared to nurses (30%) and midwives (10%). There was perfect number of anesthetists but surplus nursing assistants than needed by the hospital. Amidst shortage, the critical cadres still spent signi cant time on non-professional activities; medical o cers (24%) and midwives (25%).

Conclusion
These ndings can provide insight into the management of Kuluva hospital to address the current disparities in the health workforce in terms of numbers and skill mix for continuous improvement of health service delivery to the population it serves.

Background
Health systems are highly labour demanding and health workforce play a critical role towards performance and execution of the key health system functions. Ensuring both the right numbers and skill mix of work force. Provision of the required resources and incentive is required for healthcare workers to accomplish their assigned functions well [1]. The need for balance between the human and physical resources as being essential to maintain an appropriate skill mix between the different types of service provision ensures the health system's effectiveness [2]. Despite the global efforts, healthcare managers continue to be faced with serious human resource challenges towards delivery of quality health services to the population. Even then, the few available health workers in the health facilities are not equitably distributed according to workload, resulting into high work pressure among some staff cadre categories hence poor quality services offered to the population [3]. One major obstacle to the achievement of the global strategy on human resources by health managers for many health systems in developing countries is inadequate number of health workforce to meet the demand of the ever-growing population [4,5]. The WHO report [6] estimates global health workforce shortage of 7.2 million and projects it to reach 12.9 million by 2035. Liese and Dussault [7] agree with the WHO ndings and substantiated that in Africa, average ratio of physicians per 100,000 population is 15.5 compared to 311 for nine selected developing countries. Nurse's average at 73.4 per 100,000 population compared to 737.5/100,000 in developing countries. African countries have 20 times fewer physicians and 10 times fewer nurses compared to developed countries. Korea, India, Vietnam, Singapore have average of 106.3 physicians per 100,000 population and 220.3 nurses for the same population. Even in Sub-Saharan Africa (SSA), some countries are doing better than others. For instance, Botswana and Egypt have the following ratios of 28.7 and 218 physicians per 100,000 population and 240 and 284 nurses per 100,000 population respectively [7].
In a related study, shortages of 1,000,000 nurses and 200,000 physicians were projected for the year 2020 [8]. Human resource for health shortage will make access to healthcare services more di cult should appropriate human resource planning not be in place to address this shortage. Gasim, [9] in his study on the current crisis of human resources for health in Africa reports total workforce of 590,198 health workers with shortage of 817,992. This will require increase of 139% to reach the required level of health workers. In a nutshell, African countries have 20 times fewer physicians and 10 times fewer nurses compared to developed countries. Africa's ratio is still even poorer compared to developing countries such as India, Korea, and Vietnam that average106.3 physicians and 220.4 nurses per 100,000 people.
Disparities in sta ng and skills mix have been reported in many African countries for various reasons [10]. Out of 48 African countries, 13 had fewer than 5 physicians per 100,000 people except Burkina Faso, Mozambique and Tanzania. African countries present different human resource for health challenges; e.g. 50% of physicians in Namibia are expatriates, Cameroon had ban on health workers' recruitment for 15 years; Ghana, Zambia and Zimbabwe lose annually 15%-45% of public health sector employees.
Among Sub Sahara African (SSA) countries, Malawi has consistently had one of the worst health worker to population ratios, with 2.22 physicians per 100,000 people compared to 4.55 in Kenya and 9.09 in Zambia, with only 50% of the available posts lled. Sub-Sahara Africa accounts for 11% of World's population, 25% of global disease burden but only 3% of global health workforce thus putting the total workforce of doctors, nurses and midwives in Africa at approximately 590,198 with an estimated shortfall of 817,992 hence making African countries not meeting the WHO's "Health for All" standard of 1doctor per 5000 population [11,12].
In Uganda's health care system, the private not for pro t (PNFP) facilities play pivotal role; contributing 30%−35% of health service delivery and accounting for 40% of the country's hospitals. One -third of the workforce serving the country's strategic plan, i.e. 11,000 of the 36,000 health workers and 60% of the country's nurses are trained in 20 PNFP schools among others [13]. Over the years, Uganda adopted generic sta ng norm for distribution of health workers at various levels of the health care system, especially in the public sector. This sta ng norm has neither addressed human resource for health challenges of the country nor improved equity in human resource allocation and distribution. The Annual Health Sector Performance Report of Uganda, [3] identi ed some key challenges facing the health sector that include: 9.8% public expenditure on health as opposed to the Abuja target of 15%, combined health worker to patient ratio of only 0.74 (doctors: 0.03, midwives: 0.25, nurses: 0.46) compared to the WHO recommendation of 2.3 per 1000 population, severe staff shortage of 68% in general hospitals including the PNFP. In PNFP facilities, although the average sta ng was 73%, this varies from 28-88% [3].
To address the critical challenge of inadequate human resource for health, demand has grown for appropriate tools to expedite planning, including tools that can help with applying objective and scienti c methodologies to estimate health workforce requirements [3]. Establishing the right sta ng level and skill mix is thus a dire component of e cacious and e cient health care delivery [14]. The strategic objective of the Second National Health Policy (NHP II) of Uganda's Ministry of Health (MoH) is to ensure adequate and appropriate human resource for quality health service delivery [15]. Uganda government recognizes the contribution of the private sector in health service delivery and supports PNFP facilities with human resource, medicines and health supplies, funds, support supervision and other logistics to improve health care [16]. Despite this support to PNFP facilities, PNFP hospitals have high staff attrition of between 60% and 70% of the departures destined to government [17]. Kuluva hospital's management uses resources generated from internal and external sources to recruit staff every year to ll the gaps created by staff attrition in order to improve service delivery in the hospital while staff needs are determined by the management from time to time. However these allocations and deployments have not been based on the World Health Organization's Workload Indicator Sta ng Needs (WISN) methodology [18], a human resource management tool that bases the health work force requirement on the health facility workload [19].
"WISN Method as an analytical tool is used to determine the required number of health workforce that can cope with actual workload in a given facility and to estimate sta ng required to deliver expected services of a health facility based on workload [19]. Importantly, the difference between the actual and calculated number of health workers show the level of staff shortage or surplus for the particular staff cadre and the facility type for which WISN has been applied. On the other hand, the ratio of the actual and the required number of staff is a measure of the workload pressure with which the staff is coping. More sophisticated analyses may use calculations of workforce size and mix through use of case-load pro ling, acuity measures, queuing theory, production functions, treatment care standards or a combination of factors in regression analysis [4]. In 2010 when computerized application-based WISN tool was released, four countries in the WHO African Region, (Ghana, Kenya, Liberia and Sierra Leone) participated in the rst sub regional levels WISN capacity building workshops [20]. In India, it was applied to estimate the number of required nurses in an emergency hospital [21] and also for calculating the health worker requirements for maternal and child health services guarantees [5]. Namibia applied WISN to establish the number of doctors, nurses, pharmacists and pharmacy assistants for the different levels of health facilities [22]. Ghana adopted WISN in 2011 to address the issue of numbers [20,23]. In the revised HRH norms and standard guidelines, the Kenya health sector adopted WISN approach which also speci ed the implementation master plan [24]. In Uganda, WISN tool has been successfully applied in Lacor hospital, a PNFP facility in Northern Uganda [25] and in other health facilities to determine staff requirements by MoH [14], for instance Mityana hospital [4]. Application of WISN in Uganda has clearly shown that Uganda has one health worker for every 818 people, far below the WHO recommendation of a minimum of one health worker for every 439 people, with majority concentrated in urban centers [14]. The application of WISN in Uganda is in line with the main aim of the MoH, i.e. to have an adequate, appropriately skilled and equitably distributed health care workforce that is responsive to the needs of the people [16,26].

Study Objective
This study applied the World Health Organization (WHO) Workload Indicators of Sta ng Need (WISN), 2011 tool to determine the ideal sta ng requirements in Kuluva hospital.

Study Design, Site And Settings
In this descriptive study with predominantly quantitative approach, a set of operations was used, using Workload Indicators of Sta ng Need (WISN) methodology initially developed by Shipp [18] to estimate optimal health professionals required at Kuluva Hospital of West Nile, Uganda. We targeted eight cadres namely; medical o cers, clinical o cers, nurses, midwives, laboratory personnel, radiographers, anesthetists and nursing assistants. These are cadres usually known to be performing activities of the hospital and hence any variation in their numbers greatly affects the operations of the hospital. They also directly experience work pressures in their daily operations. Kuluva hospital, located in North Western Uganda (West Nile) being one of the 40 PNFP hospitals operates at the level of general hospital in Uganda. The hospital receives patients from Uganda and neighboring countries like the Democratic Republic of Congo (DRC) and South Sudan. Of the 76 staff in the cadre categories, 25 were selected and 20 were interviewed; these included 2 medical o cers, 2 clinical o cers, 5 midwives, 6 nurses, 1 anesthetist, 1 laboratory personnel and 3 nursing assistants. We interviewed staff who were available at work, familiar with the activities in the hospital and had worked for at least six months.

Data collection and study instruments
The WISN study team held several meetings with the relevant groups including the top hospital management (Kuluva hospital) to understand the practice and policy issues relating to human resource for health; senior staff in departments and individuals who had worked in Kuluva hospital for at least six months.
Using the pre-designed forms and checklists, the team sought key variables including; -available working time (AWT), activity standards, allowance standards and service statistics guided by the existing national WISN standards [27], developed by the Uganda MoH, United States Agency for International Development, IntraHealth and Uganda Capacity. These were available for medical o cers, clinical o cers, nurses, midwives and nursing assistants. For laboratory staff, radiographers and anesthetists we relied on information from the key informants and available standard operating procedures. The team was able to establish the available workload, conditions of health services and their components as well as other support activities performed by the health workers. Standard operating procedures in place were also used to obtain and verify additional information given by the health workers. We also reviewed the Health Management Information System (HMIS) reports of 2016/17 and activities that were not captured in HMIS, e.g. work plan, human resource manual, circulars or memos, duty rosters and leave rosters among others.
Staff available on duty and who were willing to participate were interviewed at their work units to establish the different activities they were participating in and how long they would take to accomplish the activities to the expected standards. They were speci cally interviewed on health service and supportive activities done either in groups of the cadre category or some individuals within the cadre category. Kuluva hospital human resource policy manual and the staff list were requested for from the hospital management to refer to the key human resource policies applicable to the WISN method. Potential working hours, days, o cial annual leave, other authorized offs and record of absences due to trainings and conferences for 2017/2018 nancial year were reviewed to determine the annual AWT. Annual service statistics for 2016/17 was accessed from Arua district health o ce since this was the most recent nancial year and the variation in service load would not be signi cant.

WISN methodology and operational de nitions
Unless or otherwise speci ed, the terms used in this study were adopted from the WISN guidelines [19]. This study was conducted in 6 steps which are in accordance with the updated version.
i. Available Working Time (AWT): This is the time that a health worker has available in one calendar year to do his or her work, taking into account authorised and unauthorised absence [19]. It is denoted by the formula (Eq. 1); AWT = A-(B + C + D + E)……………………Equation 1.
Where: -A = the number of potential working days in a year; B = the number of public holidays; C = the number of off-duty days due to annual leave; D = the number of off-duty days due to sickness; E = the number of off-duty days due to other leaves. Following a discussion and available records in the facility, we estimated the annual working days, estimated days for vacation, public holidays, other annual leave and absence days per year and then deducted days off from annual working days.
ii. Activity standard (AS ): This is the amount of time necessary for a well-trained, skilled and motivated worker to perform an activity to professional standards in the local circumstances; and other support activities conducted individually and in groups [19]. The set AS is then used to calculate the standard workload, category allowance factor (CAF) and individual allowance factor (IAF).
iii. The standard workload /service standard: This refers to activity standard for health service activities for which annual statistics are regularly collected and reported in the HMIS [19]. Activity standard was obtained by interviewing the key informants in the different cadre categories. The standard workload was then calculated using the formula (Eq. 2): iv. Category allowance factor (CAF): The CAF is a multiplier used to estimate the number of health workers required for both health service delivery (i.e. with outputs reported in the HMIS) and support activities done by cadre categories in groups. At rst, we summed up the percentages of time it takes all members of the staff category to perform activities for which annual statistics were not available in order to derive the Category Allowance Standard (CAS). The CAS was then used in the subsequent stage to compute the CAF. using the formula (Eq. 3) v. Individual allowance factor (IAF): Staff requirement to cover additional activities of certain cadre members (not all members) participate in the activity. The IAF shows how many full-time equivalent staff members are needed to cover the time commitment of certain cadre members to additional activities. IAF is added to staff requirement of health service and support activities. This was obtained by the following methods; we wrote down the number of staff who performed each activity and the time it took them, then multiplied the number of staff by the time the activity required in a year to derive the Individual Allowance Standard (IAS) for each activity. Finally we calculated the total IAS in a year by adding the activityspeci c IAS. The IAS was then used to derive the allowance factor with the formula in Eq. 4 The nding was also used to compute the staff requirement to cover additional activities of some cadres and then establishing the additional workload of the staff in Kuluva hospital. vi.
Annual Workload: Also referred to as the service statistics for each staff category were obtained from DISH 2 platform by the district HMIS focal person as data from the hospital was incomplete. The statistics were for OPD, maternal and child health, laboratory, HIV/AIDS, imaging, theatre and in-patient services performed by each staff category.

Workload based sta ng requirement
This WISN staff requirement was calculated using the formula in Eq. 5

Sta ng Gaps
This was de ned as staff not available to cover required activities. Established by subtracting WISN staff requirement from the current existing staff.
Workload pressure: Additional work staff have to cope with because of staff shortage. This was calculated using the formula: 100-(existing staff/computed staff x 100).

Sta ng requirement
Staff needed to cover health service, supportive and other additional activities.

Data Analysis
Data was analyzed manually and using Microsoft excel spreadsheet program. To analyze the AWT, potential working days per week, working hours per day and approved absences were entered for each cadre and the result automatically generated. The activity standard for each cadre category was entered in the excel spreadsheets for each activity generated guided by the activity and allowance standards for Uganda and interviews and average time generated in minutes. The rounded-off average time for each cadre category in minutes was used to develop a graph in excel template. The activity standards were further converted to hours and the AWT divided with the result using a calculator to obtain the standard workload for each cadre category for the activity. The annual service statistics of 2016/17 nancial year for each health service activity was divided by the standard workload to get the basic staff required to perform the speci c health service activities and their total gives number of staff per cadre required for those activities. The allowance standards (CAS and IAS), were generated for supportive activities in excel spread sheets and converted to factors for each supportive activity using a calculator. The information on CAF and IAF were then further used to manually calculate the total staff requirements for Kuluva hospital.
To determine the work pressure, WISN ratio for each staff cadre category (Existing staff/computed staff) was converted into percentages and subtracted from 100% to establish the work pressure. In summary overall work pressure for the hospital is summarized in Eq. 6: In-depth interviews for each individual representing the different cadres was analyzed manually by a team of researchers and the results obtained were used to substantiate the study's ndings.

Quality control
The team adhered to WISN's standard quality control protocols. All the study staff were trained on the methodology and data collection tools were pre-tested at Oli health center IV, Arua municipality before the main data collection exercise. The study team created rapport with the management and staff and further explained the rationale of the study so that they could clearly understand the relevance of this study and be able to provide correct and therefore reliable information. Interviews for particular cadres were led by interviewer who was a peer of such profession or was very familiar with the operations of that cadre. This enabled proper guidance of the respondents. The data collected using the standard WISN tool for the staff categories were double-checked before end of the study by at least two members in the group familiar with the operations of the staff cadre category. The data collected was immediately entered into the excel sheet so as to enable quick identi cation of the errors. Additionally, data analysis was carried by two persons independently to ensure reliability.

Available Working Time (AWT) of staff cadre categories
The AWT was estimated at 1,504 hours per year and was the same for all staff categories of Kuluva hospital (Table 1). The unit time for the health workers to perform different tasks to professional standards was established and the average standard time for each cadre category is shown in Fig. 1. Nurses had the highest average unit time to perform their professional activities followed by anaesthetists. Nursing assistants had the shortest time.
Looking at the complexity of activities, nurses and medical o cers spent their longest time in operations. Besides, nurses participate in pre-operative and post-operative nursing care that consumes a lot time. However, the longest time of nursing assistants is spent on assisting midwives during delivery of about 30 minutes compared with nurses (455minutes) supporting major surgery and medical o cers (130 minutes) according to allowance standards for Uganda.

Allowance Standards
Additional activities performed by the heath workers were also elicited through key informant interviews. Time allocated for support activities were computed using the allowance standards for Uganda. Although most time was devoted for the professional work by the health workers, signi cant amount of time was spent by critical cadres such as medical o cers, midwives, anaesthetists and lab staff on supportive activities as shown in Fig. 2.
We also captured additional workload that is generated when the staff were involved in other activities not reported in the routine health management information system (HMIS). Table 2 summarises total time spent on such activities.  Workload-based sta ng requirements (BSR) of cadre categories at Kuluva hospital Table 3 shows the sta ng requirement for Kuluva hospital based on workload. The basic sta ng requirement (BSR) column shows the number of staff that would be needed to offer basic healthcare services whose outputs are reported in the HMIS. The column of category allowance factor (CAF) was used as a multiplier for BSR in order to determine the intermediate staff requirement (ISR), i.e. the number of staff required to offer basic healthcare (reported in HMIS) and additional activities performed by all members of the cadre category not reported in HMIS. The individual allowance standard (IAF) shows the equivalent number of staff required to perform other supportive activities not reported in HMIS by some members of the cadre. This factor was added to ISR to get the total sta ng requirement for the hospital.
More nurses were required by Kuluva hospital followed by medical o cers, nursing assistants, lab staff and midwives. The lowest number required was for the anesthetists and clinical o cers. If this number required is not met, there is likelihood of disrupted service delivery hence poor quality service.  such as outreach, support supervision, mentorship, management meetings and yet some cadres are constrained. The implication of this nding is that less time is used to perform professional work by the skilled staff, resulting in poor quality of services, long service queues, and dissatisfaction from clients. Quite often, the Ugandan general public has been complaining about health workers spending more time on seminars and workshops [30]. Most of the support activities could actually be shifted. For example management could shift responsibility of transporting blood to midwives or nursing assistants who experienced proportionately less work pressure than did their laboratory counterparts. Similarly, nursing assistants can be spread across all departments to help with sterilization, general cleanliness, temperature logging and sample registration among other non-professional roles. If the staff used their time maximally for their professional work, actual staff requirement of the key cadres under study could have reduced. In a WISN study of family physicians, the authors [31] established that physicians were spending more time attending in service trainings, performing referral coordination, management and administrative assignments that could be performed by other cadres and yet these activities take a lot of time. The ndings in Kuluva hospital were not in conformity with study ndings at Mityana hospital for cadres like medical o cers [4]. in undertaking some non-professional responsibilities. In the event that trained staff are away for activities like meetings, workshops, the excess numbers of nursing assistants can easily be used to undertake some crucial health service activities such as working with medical o cers during ward round, post-operative care and yet this would be best for the nurses and midwives.
Although overall work pressure of 30% appears relatively low for staff of Kuluva hospital, lab staff (70%), medical o cers (70%) and clinical o cers (50%) were most overloaded. However, midwives (10%) and nurses (30%) had the least pressure. Anesthetists had a perfect number matching with the workload, whereas nursing assistants were in surplus. Although the staff on ground spent most of their time on professional activities, if the sta ng gap is not closed by making critical human resource decisions such as recruitment, some staff would leave hence making the situation worse. Accordingly, the number of these critical cadres should be increased or some of their non-professional responsibilities shifted in order to improve quality healthcare. With recent improvements in salary for public health workers, more attrition from the PNFP health facilities is eminent. Abundance of nursing assistants could be due to government policy of phasing out the cadre hence those already in place might not be willing to leave. The attrition of nurses and nursing assistants was the lowest in a previous study [33]. There is urgent need for the hospital to review its human resource policies and plan to address the sta ng gaps in order to offer good quality healthcare [4].

Study Limitations
The WISN methodology has been considered as an ideal tool in the current context of human resource for health planning, it however has its limitations [11,20]. The methodology is based on the annual workload derived from the annual service statistics and varies yearly. This WISN study relied on data of the most recent nancial year (2016/17) in order to calculate the sta ng needs for nancial year 2017/18. Its accuracy is thus determined by the precision of the statistics themselves. This study did not determine the sta ng needs in each department hence future WISN studies could consider this as well for appropriate staff deployment across departments.
In this study, service statistics for radiographers for instance was not re ected in the HMIS hence dropped from the nal analysis. In a way of limiting any bias in the process, triangulation of the data was done with the corresponding information obtained from the interviews.

Conclusion
Our assessment revealed the same AWT for all cadres of staff in Kuluva hospital. There is overall shortage of key staff for service delivery, these include; laboratory staff, midwives, clinical o cers, medical o cers and nurses hence high work pressure on the staff of these cadre categories amidst surplus nursing assistants. The key staff spent signi cant amount of time on non-professional activities.
The hospital management needs to review AWT with a particular focus on the approved 'offs' including public holidays that do not appear on the national schedule, leave days to optimally use the available human resource for health. The hospital should consider reviewing roles and responsibilities of different staff cadres. Where possible, non-professional roles undertaken by some cadres with high work pressure should be shifted to other cadre with lower or no work pressure in the event the hospital cannot ll in the additional staff positions.
Strategies towards capacity building through tailored training programs can be enhanced for nursing assistants who are able to join nursing and midwifery training institutions geared towards lling the existing gaps in nursing and midwifery positions. Proportion of time (%) used by health workers for health service and other activities in Kuluva