Staffing

Hospital staffing involves determining the number of personnel of the required skills in order to meet predicted requirements. In practice, several interrelated considerations make the task very complex. Factors are the organisational structure and characteristics, personnel recruitment, skill classes of the per- sonnel, working preferences, patient needs, circumstances in particular nursing units, etc. Another significant staffing decision is to define work agreements

staffing, budgeting and personnel rostering takes place at different levels and for completely different time horizons. Many researchers have therefore decomposed the nurse rostering problem in phases (3 phases in [26, 214], 4 in [194], and 5 phases in [204]). Interaction between the levels is certainly necessary but in practice it would be unworkable to handle the problems simultaneously all the time, although sub-optimal short-term decisions could theoretically be avoided. Personnel are usually hired for longer periods than the short-term rostering period. Although staffing and hospital management decisions are beyond the scope of this project, a brief summary of some work is presented. This section is mainly presented to discover different kinds of input data for the short-term timetabling problem. The system discussed in this thesis will preferably tackle the most general and complete staffing decisions. The literature overviews from Section 3.2.1, nearly all mention some of the staffing stages [26, 120, 193, 194, 204, 214].

From the 1960’s on, hospital staffing has fascinated many researchers from varying fields: pure mathematics, operations research, artificial intelligence, social and life sciences. Wolfe and Young[221, 222], for example, presented in 1963 a model to minimise the cost for assigning nurses of different skill classes to various tasks.

Schneider and Kilpatrick [183], (1975), developed mathematical programming models to determine optimal manpower utilisation in health maintenance organisations. The problem corresponds very well to that of group practices and outpatient settings and thus differs from the nurse scheduling problem in hospitals. Three different healthcare team configurations are considered, having people with different medical skills. The analytical models combine medical care aspects and financial considerations to search for an optimal solution. The developed methods produce very good results when applied in the very early stages of setting up a health maintenance organisation.

Warner[214], (1976), presented an overview of three stages: nurse staffing, nurse scheduling (see also Section 3.2.1) and nurse reallocation. The staffing problem in this work is defined as an annual decision in which seasonal variation can be considered. It consists of determining an appropriate number of full time equivalent nurses for each skill. A methodology for the staffing decision is pro- posed by Warner and many hospitals accept it (subject to small adaptations). After the scheduling phase comes the third step: the reallocation of nurses. This phase is a fine-tuning of staffing and scheduling. It involves determining how float nurses are assigned to units based on nonforecastable changes or absenteeism. Among hospital schedulers, the potential benefits of this realloca- tion step in the process are still uncertain. However, Warner is convinced that the combination of the three stages in the end leads to a better scheduling policy.

instead of a strict equilibrium. He counts the actual capacity utilisation by dividing the actual workload per hour by the available staff per hour. Uniform criteria can be handled for all wards in the hospital. However, differences in workload between wards can be registered and result in a mechanism for co-ordination between wards.

Smith-Daniels et al. [198], (1988), present a literature overview on capacity planning in healthcare. They distinguish between capacity decisions on facility resources and on work-force resources. In these categories, two decision levels are selected: acquisition decisions and allocation decisions. The acquisition decisions for work-force resources match the meaning of ‘staffing’ as it is defined in this section. The research domain of this thesis only considers the allocation decisions for work-force resources, namely the assignment of workers to days and shifts. This part is not deeply studied in [198]. Two other decisions in the group are the assignment of workers to units and to tasks. Many different strategies and approaches have been collected. Smith-Daniels et al. predict that the strict staffing and timetabling of people and other resources will all be combined in an objective for the new large scale health organisations.

Easton et al. [86], (1992), compare 12 different staffing policies during a one month period in a large hospital in the United States. They are attempting to provide adequate staffing levels to meet the patients needs and attractive work schedules to satisfy the personnel. The research is carried out at the management level, considering costs and the annual percentage of personnel turnover, reflecting dissatisfaction.

It is a common problem in hospital environments that unplanned capacity ad- justements have to be made from time to time. In busy periods, unscheduled nurses will be expected to work, and in slack periods, people will work too few hours to earn their full wages.

Restrictions on shift rotation and work stretches, distribution of unattractive work, higher wages for weekend and night work, 12-hour shifts during the week- end, etc are considered. Alternative scheduling patterns (called ALTOURs in [86]) are getting more and more common in nurse scheduling environments. The patterns involve 8, 10, 12 or 16 hours shifts, combined with days off patterns and compensation days.

Easton et al. also discuss the possibility of working with ‘float’ nurses. Float nurses can easily solve temporarily occurring under- and overstaffing in different wards. It is not recommendable however, to ask float nurses to undertake high risk tasks that require a lot of experience, such as working in intensive care, assisting in an operating theatre, etc.

Finally, the overview presents the results of 12 different methods, it compares: - scheduled hour utilisation

- the number of different ‘tours’ (see also Section 3.2.5) - . . .

for both unit scheduling and centralised scheduling (see Section 3.2.3). They conclude that the expected nursing expenses decrease as the scheduling alter- natives increase. In order to obtain this result, the nursing requirements have to obey some rules. The research also excludes overtime, part-time work, un- derstaffing, etc because it is very hard to formalise them.

Although this thesis provides no staffing policies, the algorithms can handle the results of any of the management decisions discussed in this section.