Application of Workload Indicators of Staffing Needs (WISN

International Journal of Public Health Research 2015; 3(5): 254-263

Published online August 30, 2015 (http://www.openscienceonline.com/journal/ijphr)

Application of Workload Indicators of Staffing Needs (WISN) in Determining Health Workers’ Requirements for Mityana General Hospital, Uganda

Philip Govule1, *

, John Francis Mugisha1, Simon Peter Katongole

1, Everd Maniple

1, Miisa Nanyingi

1,

Robert Anguyo DDM Onzima2

1Faculty of Health Sciences, Uganda Martyrs University, Kampala, Uganda 2Department of International Public Health, Liverpool School of Tropical Medicine, Kampala, Uganda

Email address

[email protected] (P. Govule), [email protected] (J. F. Mugisha), [email protected] (S. P. Katongole),

[email protected] (E. Maniple), [email protected] (M. Nanyingi), [email protected] (R. A. D. Onzima)

To cite this article Govule Philip, Mugisha John Francis, Katongole Simon Peter, Maniple Everd, Nanyingi Miisa, Anguyo Robert DDM Onzima. Application of

Workload Indicators of Staffing Needs (WISN) in Determining Health Workers’ Requirements for Mityana General Hospital, Uganda.

International Journal of Public Health Research. Vol. 3, No. 5, 2015, pp. 254-263.

Abstract

Introduction: With reducing number of health workers amidst increasing disease burden, ever increasing population and limited

resources, health systems are faced with challenges of providing adequate and quality health care globally. The application of

provider-population ratio or fixed staff establishments have overtime, not matched the changing human resource needs of health

care organizations. This study aimed to estimate human resource requirements of Mityana hospital basing on workload as an

alternative to the existing approaches. Methodology: We employed descriptive cross-sectional design and the Workload Indicator

of Staffing Needs (WISN) methodology. We utilized focus group discussion, observation and document review to obtain

information from key informants; generated annual service statistics from the hospital’s records. The quantitative data were

analyzed using the WISN software and spread sheet. Results: All the cadres studied had the same hours of work in a year (1,664),

except nursing assistants whose annual available working time was1,696 hours. All the cadres were experiencing additional

workload due to use of their time for activities other than their primary duties. Medical officers used more than 50% of their time

for such (non-primary) activities compared to the laboratory staff (15%). As a result, the calculated WISN staff requirements

were very high compared to the existing staff levels. Mityana hospital had 44% of the posts filled for the studied cadres. The

nurses and midwives had the highest calculated requirements (72 and 45 respectively) and the highest staff positions filled (57%

and 84% respectively) making them experience the lowest work pressure (49% each). The highest work pressure was

experienced by medical officers and medical clinical officers (82% each). Conclusion: The study reveals shortages in health

workforce in Mityana hospital. Non-primary activities contributed to work pressure in different units of the hospital, resulting

into long hours of shifts which could have compromised quality of health care. This method (WISN) estimates staffing

requirements based on actual service provision. Stakeholders facing human resource challenges and scarcity can employ it in

prioritizing health cadres for recruitment and deployment based on existing work pressure.

Keywords

Workload Indicators of Staffing Needs (WISN), Health Workers’ Requirements, Mityana Hospital, Uganda

1. Introduction

In order to improve health care delivery, health services’

managers are faced with increasing challenges in provision of

adequate and quality health care amidst inadequate numbers

of health workforce to meet the demand of the ever growing

population [1] [2]. “Availability of trained health workers is

one of the most critical limiting factors for the delivery of a

minimum health package” [3]: pg 17. In health care delivery,

human resource is a major input and its level and mix is a

major determinant of cost and quality. Expenditures on human

resource in most health systems around the world account for

over 70% of recurrent budgets [4]. There is global health

International Journal of Public Health Research 2015; 3(5): 254-263 255

workforce shortage of 7.2 million and it is projected to reach

12.9 million by 2035 [5].

The Number of trained health personnel has historically

been inadequate hence the need to use this scarce resource

effectively [6]. There is an increasing need for health

organizations to identify the most appropriate staffing levels

and skill mix to ensure efficient and effective use of the

limited resources [7]. With its efficient use, health personnel

can make a major contribution to the health of a nation. Often

this is not the case as the required cadres are frequently

missing in specific geographical areas or health facilities

where they are needed most; or in surplus where need is low

resulting into inefficiencies. These imbalances result into

spending too much time on activities requiring little time –

leaving little time for processes that require more time [8] [9].

In developed countries there has been a general shortage of

health care personnel, with shortage of nurses at 400,000; and

projected to reach one million by 2020. By the same time

(2020) about 200,000 physicians will be lacking [10]. In

developing countries, especially sub-Saharan Africa there has

been worse and chronic shortage experienced all through. It is

estimated that 1.5 million health workers are lacking [11]. In

Uganda, the gap of health workers required in government

health facilities is over 44% [12]. These shortages hinder

delivery and maintenance of quality healthcare across the

world [13]. Poor planning and management of human

resource for health among other factors, contribute to the

shortages. In order to solve some of these problems, many

countries devolved service delivery and public administration

to sub-national and district-levels, including health services

management, but there are no coordination mechanisms

between central and decentralized units of government to

address issues such as urban-rural imbalances in the

distribution of the workforce [14].

Similarly, efforts by Uganda government, such as

decentralization (including of health workforce), incentives to

attract and retain health workforce in lower health centres and

hard to reach areas [15] have yielded minimal results. The

Uganda health sector human resource policy review reveals

that 20% of the districts have critical shortage of health

workforce compared to population [16]. This has caused

disparity in delivery of health care across the county. Even still,

supply of the right number and quality of people in health care

institutions for attainment of health care objectives has

remained a challenge to managers. These bottlenecks task the

health managers with challenge of identifying methods for

allocating this scarce resource (in numbers and skills) in order

to optimize society-wide service delivery. The required

number of health workers and their skill mix in a health

facility will depend on the workload and the range of services

in the facility which in most cases is related to the minimum

health care package of the facility [18].

One crucial discovery is that most health care institutions

base their staffing on practitioner-to-population ratio,

historical patterns of staffing and professional judgment.

Others adopted more sophisticated methods of analyzing

(their) staffing needs, like case-load profiling, acuity measures,

queuing theory, production functions, treatment care standards

and other combination factors by use of regression analysis

[19]. All these methods failed to estimate staffing

requirements in accordance with the actual workload and have

not taken into context variations in local demand for health

services [2]. In attempt to address the human resource

constraints, the Workload Indicators of Staffing Needs (WISN)

was developed by Shipp in 1998 and made popular by the

World Health Organization (WHO) as a way to analyze and

compute the different health cadres required by health

facilities, based on workload [20]. WISN encompasses the

routine activities of a particular cadre, the duration per activity

and other activities carried out by a particular cadre which is

not reflected in the employment contract and the annual

available working time [21]. Application of WISN in planning

and projection of human resources for health helps to rectify

many of the observed deficiencies in access to human resource

for health when it is required, irrespective of where people in

need reside or to which socio-economic group they belong.

This is inline with the assertion that a “well planned human

resource for health is appropriate for the provision of adequate

and quality healthcare services to the healthcare needs of local

populations to increase health status and decrease healthcare

costs” [21]. This implies each health facility having its own

staffing requirements depending on (its) workload. Following

its development by WHO, the WISN has been used to

determine staffing requirements in Papua New Guinea, the

United Republic of Tanzania, Kenya, Sri Lanka and also in six

other countries: Bahrain, Egypt, Hong Kong, Oman, Sudan

and Turkey among others [18]. WISN has also been used in

Namibia to rationalize the staff requirements for nurses,

doctors, pharmacists and pharmacist assistants [22]. In East

Africa, the method was used in Tanzania to determine the

staffing needs for quality prenatal care for nurse officers,

nurse midwives and nursing assistants [23] – it was used to

calculate the staffing requirements for medical officers, nurses

and laboratory staff in Kenya [21]. In Uganda, this method

was initially used to determine the nursing staff requirements

in Lacor, a private not for profit (PNFP) hospital in Northern

Uganda in 2005 [6]. The method was later experimented by

the government of Uganda in two districts of Mbale and

Mukono [24]. Considering that the staffing requirements for

health facilities differ, we sought to apply WISN in

determining staff requirements in a selected hospital in

Central Uganda. One of the possible issues argued is that the

method is burdensome since determining staffing

requirements is required by each health facility independently.

This looks a bit awkward; but it is known to have specific

returns of effectiveness and efficiency following its use in the

long run [25].

The Uganda Health Sector Strategic Plan III (HSSIP

2010/11-2014/15) stipulates the minimum staffing standards

(basing mainly on practitioner-to-population ratio, historical

patterns of staffing and professional judgment) at 75% for all

health facilities to enable meeting the growing demand of

health care, with the ratios of senior, mid-level and lower

health cadres at 1:5:2 respectively [26] [27]. Unfortunately,

256 Govule Philip et al.: Application of Workload Indicators of Staffing Needs (WISN) in Determining Health Workers’

Requirements for Mityana General Hospital, Uganda

the overall national staffing level for health workers has

remained low (at 56%). Further analysis of the staffing level

of Mityana hospital revealed low staffing at 63.2% of

approved posts for some cadres and excesses of 2% for others

[27]. These observations depict deficiencies in use of fixed

staffing norms as applied in distribution of health workers,

estimation of work pressure and determination of staffing

requirements. This study aimed to estimate staff requirement

based on workload, so as to generate evidence to inform

policy makers and implementers to address issues on

distribution of staff, workload pressure, review and re-align

task-allocations between staff cadres and improve the quality

of health care.

2. Objectives

To apply WISN in determining health workers requirements

in order to improve health services in Mityana General

Hospital, central Uganda.

The methodological steps to achieve the objective included

to establish available working time (AWT) for selected

categories of health workers and to establish the additional

and support workload experienced by different categories of

health workers.

The specific objectives included to establish

workload-based staffing requirements for different categories

of health workers; and to compare WISN-based staffing

requirement with the existing staffing levels for Mityana

General Hospital, central Uganda.

3. Methodology

We conducted a descriptive cross-sectional study in

Mityana General Hospital adapting the WISN methodology

initially developed by Shipp [18] where we estimated the

available working time by reviewing the health staff records.

We used both focus group discussion and key informant

interview define and quantify the workload load components:

health service, support and additional activities. We employed

focus group discussion (1 FGD for each cadre) to set activity

standards. We conducted key informant interviews using

predetermined checklists to obtain data regarding the activity

standards for the various cadres while focus group discussions

were used to obtain information regarding the challenges

faced by various cadres in accordance with the workload. For

the interviews, we employed different checklists for relevant

cadres with designs guided by national WISN standards

produced by the Uganda MoH in partnership with United

States Agency for International Development, IntraHealth and

Uganda Capacity Program. Service statistics’ data were

collected from the hospital management information system

reports and registers. The data collected from key informant

interviews were then transferred to the WISN software (WISN

English version 1.1.132.0).

3.1. Sampling Technique

This study targeted six cadres namely; medical officers,

medical clinical officers, nurses, midwives, laboratory staff

and nursing assistants. These cadres were considered because

they are the ones mostly affected by increased workload.

Secondly, they have relatively larger numbers of staff for

which internal deployment and transfers can easily be carried

out. We interviewed 10 midwives, 5 laboratory staff, 9 nursing

assistants, 6 medical clinical officers, 8 nurses and 4 medical

officers of at least one year experience each.

3.2. Variables

The variables of this study include; available working time,

activity standards, annual workload, category allowance

standards (CAS), individual allowance standards (IAS), staff

levels, workload pressure and staffing requirement.

3.2.1. Available Working Time (AWT)

AWT = A-(B+C+D+E)

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

3.2.2. Activity Standards

Activity standard (AS) which is also referred to as the

amount of time necessary for a well-trained skilled and

motivated worker to perform an activity to professional

standards in a given circumstance was used to assess length of

‘professional time’ taken to execute tasks and put into

perspective primary activities; and other individually and

group-conducted activities. We later used the set AS to

calculate the standard workload, category allowance factor

(CAF) and individual allowance factors (IAF).

(i). The standard workload: This was calculated using the

formula;

Standard workload = Available Working TimeActivity Standard

The activity standard was obtained through key informant

interview with health staff of each cadre.

(ii). Category allowance factor (CAF): This was calculated

by summing up the percentages of time it takes all

members of the staff category to perform activities for

which the annual statistics were not available known as

category allowance standard (CAS). The CAF was

used in subsequent variables to compute the number of

staff needed.

CAF = 11 − ∑CAS%

(iii). IAS was obtained by calculating how much time

additional activities of certain cadres require by;

a. Writing down the number of staff who performed each

activity and the time it took them.

b. Multiplying the number of staff by the time the activity


required in one year

c. Calculating the total IAS in a year by adding the results

obtained in (b) above

The individual allowance standards was then used to derive

the individual allowance factor using the formula;

IAF = IASAWT

The result was also used to compute the staff requirement to

cover additional activities of certain cadres and determining

the additional workload of the staff in Mityana hospital.

3.2.3. Annual Workload

The annual workload for each staff category were obtained

from the hospital reports; and laboratory and ANC registers.

These figures were used to calculate the basic staff

requirements and the total staff requirements for each

category.

3.2.4. Workload-Based Staffing Requirements

The results obtained from the above variables were then

used to compute the workload-based staffing requirements

using the formula:

WISN staff requirement

= �Annual workload x CAFStandard workload " + IAF

3.2.5. Staffing Gaps

The staffing gaps were established by subtracting the WISN

staffing requirements from the existing staffing level.

3.2.6. Workload Pressure

The workload pressure was calculated using the formula:

Workload pressure = (100- (existing/computed staff x100)

3.3. Data Analysis

Data were analyzed using excel spreadsheets, the WISN

software and manually. Considering that activity standards

were obtained through interviewing staff who reported

different activity, we obtained an average activity standard

before transferring to WISN software . In order to do this,

templates were generated in excel where various averages and

uniform time standards were calculated for the service

standards, CAS and IAS. The information was then

transferred to the WISN software and results generated in

accordance with the objectives of the study. Focus group

discussions were analyzed manually by a team of researchers

and the results obtained were used to substantiate the study’s

findings.

3.4. Quality Control

We adhered to standard quality control protocols of WISN

methods. The data collected were immediately entered to

quickly identify missing data, which was then obtained before

the end of the study. More so, the researchers assigned to

interview the particular cadres, were themselves peers of such

professions which enabled proper guidance of the respondents

in giving accurate information. The data were analyzed by two

persons independently to ensure reliability.

3.5. Limitations of the Study

Like any other WISN study, this study was conducted using

data retrospectively generated from the preceding year.

Significant differences in workload in the consecutive years

may affect validity of results. However, they remain useful for

planning purposes since workload variations across years and

seasons is comparable.

4. Results

The present study applied WISN method in determining

staffing requirements for medical officers, clinical officers,

nursing assistants, laboratory staff, midwives and nurses in

Mityana general hospital.

4.1. Available Working Time (AWT)

The available working time was the same for all the cadres

but Nursing Assistants (table 1).

Table 1. The Available Working Time.

Available Time in a year Cadres

Medical Officers Clinical Officers Laboratory staff Midwives Nurses Nursing Assistants

Working Days Per Week 5 5 5 5 5 5

Working Hours Per Day 8 8 8 8 8 8

Annual Leave 22 22 22 22 22 18

Public Holidays 13 13 13 13 13 13

Sick Leave 2 2 2 2 2 2

Special No Notice Leave 10 10 10 10 10 10

Training Days Per Year 5 5 5 5 5 5

Non - Working Days 52 52 52 52 52 48

Non-working weeks 10.4 10.4 10.4 10.4 10.4 9.6

Working days 208 208 208 208 208 212

Working Weeks 41.6 41.6 41.6 41.6 41.6 42.4

Working hours 1,664 1,664 1,664 1,664 1,664 1,696



The fewer annual leave days the nursing assistants took

explains the variation in AWT. This is due to the fact that in

Uganda, they fall under the government staff category of U8

that is entitled to18 days of annual leave.

On further interviews with another hospital management

team member, we discovered that although the standing order

provides for 40 training days per year for every public servant,

not all members of the staff category had gone for training. It

was proper to use the training days as provided in the standing

orders and not the actual trainings days taken by a few

members of the staff category because we realized (during

focus group discussion) that the reason some cadres did not

attend training was a result of staff shortage and high

workload. Using the actual days would mean promoting this

gap as the norm, which will then increase the AWT and reduce

the number of staff actually required to produce quality

services. These facts came out so clear during the focus group

discussion where one of the participants expressed with

sadness:

We have terrible shortage of nurses which affects the

quality of nursing care, but luckily, nurses prolong the

hours of work in order to cover some shifts.

Laboratory staffs were often recalled from leave because

they are few.

Table 2. Absences due to trainings and workshops during 2012/2013.

Cadres Number of staff as of

2012/13

Number of staff who attended

workshops and trainings

2012/13

Total number of hours spent on

workshops and trainings 2012/13

Number of hours if

all staff had gone on

training

Medical officers 6 1 40 240

Clinical officers 7 2 80 280

Midwives 37 6 240 1,480

Nurses 27 8 320 1,080

Laboratory staff 4 1 40 160

Nursing assistants 12 2 80 480

Total 93 20 800 3,720

From table 2, only 20 members of staff from the different

cadres had actually gone for training during 2012/2013 fiscal

year meanwhile it would have been expected that if this

hospital had the required number of staff, all members of each

category should have gone for 40 hours training per year; and

as such the total training days would have been 3,720 which

reduces the AWT in a year.

4.2. Additional Workload for Different

Categories of Health Workers

Additional workload is work pressure generated by

involving staff to perform supportive and additional activities

other than the routine activities that are captured in the Health

Management Information System. We found out that the staffs

in Mityana were involved in some support (CAS) and

additional activities (IAS) which constituted additional

workload. The support activities (CAS) were those performed

by all members of a staff category. For example, for laboratory

staff, the activities were found to include annual staff party,

departmental meetings, and preparation of work area among

others. Individual staffs were also found to be performing

some activities which were not captured in the Health

Management Information System, and these increased the

workload. For example, medical officers were involved in

management meetings, outreach activities and support

supervision.

4.3. Time Consumed by Support and

Additional Activities

The support activities (CAS) consumed majorly medical

officers’ time (54%) (Table 3). Similarly, individually

conducted activities took more medical officers time

(25,421.81 minutes) than any other cadre – the clinical officers

having had least time on other individual activities (418.28

minutes).

Table 3. Time consumed by additional activities.

Cadre Total Category Allowance (%) Total Annual Individual Allowance (in minutes)

Medical Officers 54% 25,421.81

Clinical Officers 31% 418.28

Laboratory Staff 15% 9,555.75

Midwives 40% 2,187.91

Nurses 30% 1,688.39

Nursing Assistants 20% 729.29


Table 4. Workload-based staffing requirements.

Cadre Basic staff requirement CAF BSR IAF Total staff required

Medical Officers 5.63 2.17 12.22 15.13 28

Clinical Officers 27.01 1.45 39.16 0.25 40

Laboratory Staff 10.7 1.18 12.62 5.69 18

Midwives 41.99 1.67 70.12 1.3 72

Nurses 30.6 1.43 43.75 1 45

Nursing Assistants 22.94 1.39 31.88 0.43 32

4.4. Workload Based Staffing Requirements

(BSR) for Different Categories of Health

Workers

The basic staff requirement (BSR) column in table 4 shows

the number of staff that would have been required in Mityana

general hospital if all members of that staff category dedicated

their entire time in their primary professional activities. For

example, only 6 medical officers would have been required if

all their time were dedicated to their primary activities.

However, because of the additional workload, more are

required.

Column 5 (IAF) shows the number of staff required by each

cadre to perform sole additional activities. It is seen that

nurses require only 1 staff for such. The IAF was added to the

ISR to obtain the total staff required by the various cadres in

Mityana with midwives seen to require the highest number of

staff (72).

Figure 1. Staff required for primary and additional activities.

Except for medical officers, staffs required for primary

activities were higher than those for additional activities.

4.5. Comparison of Workload Based on

Staffing Requirement with the Existing

Staffing Levels

As seen in table 4, the WISN staffing requirements are very

high for all cadres compared to the hospital staffing levels.

The WISN calculation were based on the annual workload

data for 2012/2013 fiscal year and therefore the staffing levels

within that same period was used for comparison alongside

the those of 2013/2014 fiscal year. The highest requirement

for the period 2012/2013 were for clinical officers (n = 40),

whereas the existing staff were only 7. This was followed by

nursing assistants of whom 32 were required compared to only

11 that were available. These shortages seemed to have been

noticed in 2013/2014 fiscal year.

Table 5. Existing staffing levels and staff required.

Cadres staff levels as

at 2012/2013

Staffing

levels as at

2013/2014

WISN Staff

required

Existing gap as

at 2012/2013

present Gap

(2013/2014)

percentage

shortage as at

2013/2014

Percentage of

filled post as of

2013/2014

Medical Officers 5 8 28 23 20 71% 29%

Clinical Officers 7 9 40 33 31 78% 22%

Lab Staff 4 8 18 14 10 56% 44%

Midwives 37 41 72 35 31 43% 57%

Nurses 23 38 45 22 7 16% 84%

Nurse Assistants 11 9 32 21 23 72% 28%

Although the midwives had the highest WISN staff requirement, the existing staff as at 2012/2013 and 2013/2014



fiscal years were also high at 37 and 41 respectively, thus constituting the cadre with the lowest gap (48.6%).

Figure 2. Staffing levels in 2012/2013, 2013/204 and WISN requirements.

Figure 3. Work pressure in Mityana hospital.

The WISN ratio was obtained by dividing the existing staff

by the total required staff expressed as a percentage. The

results were then subtracted from 100 to get the workload

pressure. A low WISN ratio indicates high work pressure on

the staff. From figure 3, it is evident that clinical officers and

medical officers had the lowest WISN ratio and therefore were

under more work pressure than the other cadres. The

midwives and nurses had the least work pressure.

5. Discussion

An earlier WISN study conducted in Mbale and Mukono

districts had calculated the AWT based on standing orders of

the Ugandan Public Service and the Catholic Medical Bureau

[2]. The resultant AWT for nursing assistants (in hours) in that

study were comparable to ours. The difference in AWT for the

earlier studies and ours were not far apart. The AWTs were

1,624, 1,680; and 1,688 for medical officers, clinical officers;

and nurses and midwives respectively. On the other hand, a

study conducted in Lacor hospital [6] reports higher AWTs of

1,815.4 hours; and 1,884.0 hours for nurses and midwives;

and nursing assistants respectively. In the Lacor hospital study,

training days were not included in the calculation of AWT,

probably because no staff had gone on training during that

period, thus the higher estimate. In addition, Lacor hospital is

not a public health facility as such, not a subject of some


policies and legislations related to work schedules for public

health workers. The Uganda Public Service Standing Order

[28] stipulates that ‘a public officer must undertake staff

development activities for a minimum of forty (40) hours in a

financial year to improve his or her competencies’. The use of

actual time taken to perform an activity rather than the

standard time, or not including that activity because it was not

done in that period may lower the total staff requirement

because it will mean calculating a required staff that is

necessary to continue performing in an environment which

denies them the right to go for training or take days off to rest.

The calculated staff requirements in Mityana hospital

considered all the official leaves, public holidays, trainings etc

as provided in the standing orders, because only then will they

be properly motivated and empowered (with knowledge and

skills) to provide quality service. Due to shortage, some staffs

were denied these rights inorder to improve staff availability.

If the staffs of the various categories were to use their time

mainly for the primary activities for which they were

professionally trained, the total staff requirement would not be

as high. In Mityana hospital, non-primary activities consumed

chunks of time of critical cadres like the medical officer.

Although additional activities such as operational research,

management meetings, staff meetings, and appraisals amongst

others are necessary for the smooth running of services, it is

obviously detrimental when large portion of a professional’s

time is spent on them: especially in contexts with scarce

professionals like Uganda. These findings were also supported

by the focus group discussions that were conducted for the

various cadres. One of such discussions with the midwives

revealed mismatch between number of mothers delivering

each day and availability of midwives, yet, despite the

shortage (of midwives), some midwives continued to be

assigned to perform other activities. In addition, this study

observes that even with the high workload at the maternity,

some midwives were assigned to be dispensing medicines due

to shortage in those departments. These facts are similar to

those of other WISN studies elsewhere in Uganda. A WISN

study in Lacor hospital (Uganda) [6] reports 17 midwives

involved in doing “non-midwifery work” like dispensing

medicines and performing nursing procedures in outpatient

department and general wards as a result of inadequate

nursing staff. This further worsened the shortage of midwives

in this hospital. Earlier WISN studies made similar revelations

elsewhere in Mozambique [28] and Indonesia [30]. Different

members of staff are required to perform primary, support and

additional services, with the number of staff required

increasing as additional workload increases. With the

exception of medical officers, the staff required for primary

activities in Mityana were higher than those required for

support and additional activities – an ideal situation. This is

required to be the general practice because the primary

activities are those for which the professional in each cadre

has been trained and recruited to perform. The major

involvement of the medical officers in other activities most of

the time has high quality implications. Additionally,

employing upto 22 medical officers just to perform additional

services is not only reflective of ineffective deployment but

adds to local brain waste in a context where this cadre is

scarcest. Other than thinking of additional medical officers,

the management of Mityana hospital should first consider

maximizing use of this ‘precious’ cadre in professional

engagements.

High gaps exist in required staff and the status quo. For

example in 2012/2013 there was a shortage of 33 clinical

officers. Though the staff level for this cadre was increased to

9 in 2013/2014, this increase reflected negligible addition

given the 31 more needed. The cadres like medical officers,

clinical officers and nurse assistants with more than 70% staff

shortages (each) is not surprising since (only) 63% of the

approved posts had been filled in Mityana hospital [31] –

implying that there was already a shortage of 37%. In addition,

this 37% shortage was based on the government of Uganda’s

estimation of staffing requirements using staff to population

ratio which unfortunately was calculated based on the 2010

population of 306,000 people [32] compared to the currently

over 800,000 people [31]. This implies that the 37% shortage

was not in consideration of the extra population and this

justifies the high staffing gaps we have highlighted. This calls

for relevant authorities to adjust staff to population health

worker needs to meet the taste of time as population changes.

In 2012/2013, the nursing assistants in Mityana were 11 – a

number that has regressed to 2 contrary to the other cadres

whose staffing levels are on the rise. This may be attributed to

the present policy to phase-out this cadre in Uganda that is

making nursing assistants to leave in pursuit of alternative

careers. In addition, those who reach retirement age are not

replaced. This seems to be quite detrimental to the hospital as

the study reveals that this staff category still shoulders high

workload and eradicating them may increase the pressure on

the existing staff who are already under much pressure.

Surprisingly, the nursing assistant, a cadre due for extinction

from Uganda’s health system still experiences higher

workload than the nurses. These findings are similar to that of

a study which reveals work pressure on nursing assistant as

twice higher than for nurses [6]. If left unattended to, the

resultant effects of this high work pressure may be similar to

that of a study which was conducted in Tanzania in 2008,

which reveals that shortage of health workers leads to high

work pressure which in turn leads to staff spending less time

on each activity than is set by activity standards [23]. This

indicates that the quality of services being delivered is even

below the locally accepted standards. The study [23] further

reveals that high workload pressure leads to poor health

workers’ attitude, lack of morale, absenteeism and passivity in

attending to patients. High workload pressure translates to

poor quality services and has negative impact on staff

motivation leading to high staff-turnover which in turn widens

the staff shortages [33].

6. Conclusions

This study revealed overall shortage of all cadres evaluated

– clinical officers and medical officers most affected. The



study further reveals much of health professionals’ time being

spent in activities other than their technical job-descriptions. It

was however, difficult to apportion work pressure to specific

departments since we did not apply WISN to them. This

implies that it is not possible to advise local staff redistribution

based on the findings of our study. The severe staff shortage

we observed was partly compensated by the affected cadres’

working for longer hours than stipulated in the Uganda Public

Service Standing Orders and this could have compromised

quality of health care. Optimizing use of the already scarce

professional cadres through deploying them for critical and

technical services could suffice in the short term while the

hospital management explores alternative mechanisms for

bridging the staff gaps to address the long-term human

resource needs of this hospital. This method (WISN) estimates

staffing requirements based on actual service provision.

Stakeholders facing human resource challenges and scarcity

can employ it in prioritizing health cadres for recruitment and

deployment based on existing work pressure.

Acknowledgements

We acknowledge the contribution of the following to this

study: Dr Grace Namaganda, Glory Tchiaze Tsangue,

Christian T. John, Lou Eluzai Loponi, Mucunguzi William, Fr.

Nnunda Pius, Mary Grace Lanyero, Nakyanzi Josephine,

Muluya Kharim Mwebaza, Sr. Regina Nakachwa. This study

was funded by the Faculty of Health Sciences, Uganda

Martyrs University.

List of Abbreviations

AWT: Available Working Time

CAS: Category Allowance Standards

CAF: Category Allowance Factor

IAS: Individual Allowance Standards

IAF: Individual Allowance Factor

WHO: World Health Organization

WISN: Workload Indicators of Staffing Needs

HSSIP: Health Sector Strategic Investment Plan

HSSP: Health Sector Strategic Plan

HMIS: Health Management Information System

SUO: Standard Unit of Output

MOH: Ministry Of Health

MHSDMU: Medicines and Health Services Delivery

Monitoring Unit

UCC: Uganda Communication Commission

Operational Definitions

Except otherwise indicated, the following definitions are

adopted from the WHO WISN user’s manual [2].

WISN: Human resource planning and management tool that

gives health managers at all levels a straight forward,

systematic way to make staffing decisions and manage human

resources efficiently and effectively.

AWT: The time that a health worker has available in one

year to do his or her work.

Standard Workload: The amount of work within a health

service workload component that one health worker can do in

a year.

Activity Standards:The time it takes a trained and

well-motivated member of a particular staff category to

perform an activity to acceptable professional standards in the

circumstances of the country.

Health service activity: The health service- related

activities performed by all members of the staff category and

for which annual statistics are regularly collected.

Support activity: The important health service activities,

performed by all members of the category but for which

annual statistics are not regularly collected.

Additional activity: The activity performed only by certain

(not all) members of the staff category and for which annual

statistics are not regularly collected.

Caseload profiling: The fundamental aspect of identifying

team requirements and staffing levels needed to effect patient

care (Burns, 2003).

Acuity measures: Estimation of staffing requirements based

on what people see or think appropriate without using any

scientific method.

Queing theory: Estimation of staffing needs based on

patients waiting for services (Adan & Resing, 2002).

IAF: A staff requirement to cover additional activities of

certain cadre members.

IAS: The actual working hours per person in a year.

CAS: The total percentage of each health worker’s time

taken up by support activities.

SUO: A comprehensive output indicator of hospital

workload and performance.

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