Abstract
Introduction
The study was aimed at comparing the applicability of quantitative techniques and its relevance in decision making by clinical and non-clinical healthcare managers and administrators.
Methods
A comparative cross-sectional study in design conducted at the Obafemi Awolowo University Teaching Hospital, Ile-Ife amongst 52 clinical and 50 non-clinically related healthcare managers and administrators. Data collection tool was a semi-structured self-administered questionnaire. Data were analysed using descriptive and inferential statistics of SPSS version 20 with statistical significance determined at p < 0.05.
Results
A higher proportion of the non-clinical healthcare managers and administrators were familiar and had used more quantitative techniques than their clinical counterparts. Experience ranked highest as the current method employed by both groups for most decision needing scenarios presented. There was no statistically significant difference in their perceived effectiveness of the current methods guiding decision making. However, a statistically significant difference was found in the methods preferred by both study population only for ‘reducing waiting lines, p = 0.015’ and in ‘maximising the use of human resources, p = 0.017’.
Conclusions
Approach to decision making in the Nigerian health sector is still largely experiential and more of a personalised bargaining process. Acknowledgement of the relevance of quantitative techniques by clinical and non-clinically related health managers and administrators as found in this study is hoped to accelerate the adoption of these techniques. It is believed that its application will lead to improved health service delivery and health outcomes.
Introduction
Decision making is the process of choosing the best alternative to achieving individual and organisational objectives. 1 According to Herbert A. Simon and associates, the work of managers is about ‘choosing issues that require attention, setting goals, finding or designing suitable courses of action, as well as evaluating and choosing among alternative actions’. 2 He stressed that ‘nothing is more important for the wellbeing of the society than for this work to be performed effectively’. 2 There are several theories or models to explain managerial decision making. Few of these include the pre-programmed organisational process model; the political views which is a personalised bargaining process. Also, there is the Simon’s theory of rationality in decision making which entails the four steps of intelligence, design, choice and review assuming the manager has the needed information to make decisions. 3 Simon also proposed the bounded rationality theory, where the manager is assumed to have incomplete information for decision making. For the rationality models, the alternatives are ranked by calculating their subjective expected utility. The approaches employed in selecting the best alternative may include any or all of experience, experimentation, research and analysis.3,4
According to Oetjen et al., the making of quality and informed decisions by healthcare managers cannot be underestimated. 5 Decision making by some managers in organisations is done after a great deal of research and scientific manipulations, whereas some others make their decisions on the spur of the moment or in reaction to an emergent situation. 6 The import of every decision made is that it either moves the organisation forward or draws it backwards. The effectiveness and quality of the decisions made will determine how successful a manager will be.7,8 To achieve quality decision making, in line with the various decision-making models, health managers will require the use of quantitative techniques.7,9 This study focused on the managerial decision making of healthcare managers and administrators in clinical and non-clinical departments of a tertiary health institution and not on clinical decision making in patient management.
Quantitative techniques can be defined as mathematical and statistical models which describe a diverse array of variable relationships, and are designed to assist health managers with problem-solving and decision making. 10 The relevance of quantitative techniques in the health industry cannot be over-emphasised. For instance, forecasting techniques are needed to analyse health demands and to assess how to deal with them. Optimisation techniques are needed for planning and allocating health resources. The queuing theory is needed to analyse and manage waiting lines in patient utilisation of facilities. Program evaluation review technique (PERT) or the critical path method (CPM) is necessary in project implementation control system. 11 Health managers have need of competencies in these quantitative techniques to guide decision making and achieve quality outcomes.
Unfortunately, this population of healthcare managers and administrators in the developing countries are an understudied group. 12 What informs their decision making; what quantitative skills do they possess for decision making; what is their level of willingness and preparedness for the adoption of these quantitative decision-making tools if they are yet to apply them? All these questions and more are yet to be answered in literature. This study was aimed at determining the applicability of quantitative techniques and its relevance in decision making by clinical and non-clinical healthcare managers and administrators in a public tertiary health facility in Nigeria. The specific objectives included comparison of their level of familiarity, extent of use and their perceived relevance of quantitative techniques to decision making. Their current methods guide in decision making. The perceived effectiveness of these current methods, their preferred methods for guiding decision making as well as the reasons for their preference were also explored and compared.
Methods
Study design and study location
The study was comparatively cross-sectional in design conducted at the Obafemi Awolowo University Teaching Hospital, Ile-Ife, South-west, Nigeria. This is a 527-bedded tertiary health facility with about 26 wards and an average daily outpatient rate of about 300 persons per day. Average monthly total bed occupancy rate is over 100%, though may vary per ward. The staff strength is over 3000; it consists of departments which are made up of organisational units. There are 42 departments broadly classified as 27 clinical and 15 non-clinically related departments. The governance structure consists of a Management Board and immediately below is the Chief Medical Director (CMD) at the Apex. The Chairman Medical Advisory Committee (CMAC) and the Director of Administration (DA) both relate immediately to the CMD as the strategic level managers. The Heads of the department serve as the tactical managers, while the unit heads within the departments serve as the operational level managers.
Study population, inclusion and exclusion criteria, sampling
Study was conducted among health managers and health administrators at the various levels of management in all the clinical and non-clinical departments. Health managers were defined as those whose job description included the overall operation or management of the health facility.13,14 They also manage all the business aspects of the health facility. The health administrators are those whose job description included the management of the staff in the health facility.13,14 The clinically related group of health managers and administrators were those who, by the organisational structure and design of work in the institution, report to the CMAC, while the non-clinical group report to the DA or directly to the CMD. All three strategic managers and each of the tactical managers in the 42 departments were selected for interview using a purposive sampling technique. To boost the power of the study, simple random sampling technique through balloting was used to select three operational level managers in each of the 42 departments in addition. This made a total sample size of (4 persons per 42 departments) plus 3 strategic managers totalling 171. Only respondents willing to participate were interviewed.
Research instrument and data collection
Data collection was via a semi-structured self-administered questionnaire. The quantitative techniques assessed included: productivity benchmarking; forecasting; linear programming; simulation models; flow charts; scatter diagram; fish-bone diagram; determining upper and lower quality control limits; determining full time equivalent (FTE); Gantt chart; scheduling alternatives; queuing theory; network analysis techniques in project management such as PERT and the CPM; decision techniques under uncertainty such as maximin criterion, maximax criterion, minimax regret criterion, Laplace and Hurwitz criterion. 11 Brainstorming, a qualitative technique, was also assessed.
Data management
The variables were measured using a Likert scale of 1 to 4 with 1 as the lowest and 4 as the highest to address familiarity; extent of use of quantitative techniques and their perceived effectiveness of the current methods being used for decision making. Familiarity in this study meant being aware and knowledgeable of the quantitative techniques assessed. Perceived relevance of quantitative techniques was assessed using one best option answer from ‘relevant’, ‘not relevant’ and ‘don’t know’. Also, the preferred method for guiding decision making was assessed using one best option from any of ‘current methods’, ‘quantitative techniques’ or ‘none of the two’. Data collected were subjected to field and office editing, categorisation and coding. The variables with a Likert scale of 1 to 4 were recoded; 1 and 2 were recoded into 1, while 3 and 4 were recoded into 2 with the recoded ‘1’ being either of ‘not familiar’ or ‘never use’ and the recoded ‘2’ being either of ‘familiar’ or ‘ever use’. The variables assessing relevance to practice was also recoded into ‘relevant’ and ‘not relevant’. The ‘non-relevant’ comprised ‘not relevant’ and ‘don’t know’ grouped together.
Data analysis
Data analysis was done using the SPSS statistical package version 20. Frequency distribution of the level of familiarity, extent of use, relevance to practice, degree of effectiveness of the current methods used, preferred method in decision making was done. Responses of the clinical and non-clinically related healthcare managers and administrators were compared using a chi-square statistical test with statistical significance determined at p-value < 0.05.
Ethical approval
Ethical approval for the study was obtained from the Health Research Ethics Committee (HREC) of the Institute of Public Health, Obafemi Awolowo University, Ile-Ife with an HREC No: IPHOAU/12/389.
Results
Description of the study participants
A total of 32 (76.2%) of the 42 departments (18 clinical and 14 non-clinically related departments) participated in the study giving 128 eligible respondents. In all, the response rate was 80% as only 102 of the 128 eligible respondents participated. There were 52 (51.0%) and 50 (49.0%) clinical and non-clinical-related healthcare managers and administrators, respectively, of the 102 total respondents. The mean age of the clinical and non-clinical healthcare managers and administrators was 49.9 ± 7.675 years and 47.8 ± 6.125 years, respectively, with no statistically significant differences in their mean ages (t = −1.512, p = 0.134). There were 39 (78.0%) males and 11 (22.0%) females among the non-clinically related healthcare managers and administrators, while there were 41 (78.8%) males and 11 (21.2%) females among the clinically related healthcare managers and administrators. There was no statistically significant difference in the sex distribution of both population of respondents.
Comparison of the familiarity with quantitative methods
Comparison of the degree of familiarity with the listed quantitative techniques.
Comparison of the extent of use of quantitative techniques
Comparison of the extent of use of the listed quantitative techniques.
Comparison of the perceived relevance of quantitative techniques
More than 50% of both groups of healthcare managers and administrators studied perceived that each of linear programming; simulation models; flow charts; scatter diagrams; determining upper and lower control limits in TQM; determining the FTE; PERT; CPM; scheduling alternatives; brainstorming and queuing theory was relevant to their practice, and for these there were no statistically significant differences in the responses of the two groups studied.
Comparison of Perceived relevance of quantitative techniques by clinical and non-clinical healthcare managers and administrators.
Comparison of the current methods used to guide decision making and their perceived effectiveness
These two groups of healthcare managers and administrators were presented with some decision needing scenarios that could necessitate the use of these quantitative techniques and asked for which methods currently guides or would guide their decision making. The three responses with the highest frequencies for each of the decision needing scenarios were summarised. The use of experience ranked either highest or second to the highest as the current methods employed or to be employed by both groups of healthcare managers and administrators. Almost twice the number of the non-clinical group of healthcare managers and administrators compared with their clinical group said they do apply or would apply forecasting as their decision-guiding method when predicting expected demand for service rendered, (16% vs. 9.6%). Five (9.6%) of the clinically related healthcare managers and administrators insisted that they must have a criteria to guide their decisions and would not make decisions under uncertainty.
In allocating or sharing resources among units, both groups of managers and administrators said they do or would engage their wealth of experience and also consider the level of productivity of the units. However, 1 (1.9%) of the clinically related healthcare manager and administrator said he/she would consider institutional preference in the allocation of resources. While Gantt chart topped the list for displaying and monitoring planned activities by the clinical group of healthcare managers and administrators, it was ranked the least by their non-clinical counterparts. Also, while ‘verbal communication’ topped the list of current methods employed by the non-clinical group of healthcare managers and administrators in explaining a process of how service is delivered, it was the least where their clinical-related counterparts were concerned. Rather they had ‘flow chart’ topping their list for the same decision needing scenario.
Comparison of current methods used in guiding decision making by both groups studied.
Comparing perceived effectiveness of current methods used in decision making.
LR: likelihood ratio.
Comparison of the preferred methods for decision making and reasons for their preference
Comparing the preferred methods for guiding decision making by both population studied.
LR: likelihood ratio.
For the decision-needing scenarios for which the continual use of their current methods were preferred, the reasons given by the non-clinical healthcare managers and administrators for this were based on its being easy to apply and that it had worked so far. As managers, they felt their current methods which were inclusive of experience and intuition were more economical, less tasking and would need less personnel. Some even alluded that the quantitative techniques were not relevant to the hospital set up.
The clinically related healthcare managers and administrators gave as their reasons for preferring the current methods its convenience, simplicity and one that makes for faster decision making which had worked so far. Both groups of healthcare managers and administrators acknowledged their ignorance in quantitative techniques as part of their reasons for preferring the current methods, while a few preferred the adoption of both the current and quantitative methods.
The reasons given by the non-clinical healthcare managers and administrators who preferred the quantitative techniques were its being scientific, unbiased and objective in nature; they also believed it would make for faster decision making. Their clinically related counterparts interestingly were much louder in their reasons as they alluded to its reproducibility; the possibilities of risk minimisation; its accuracy and that it makes for evidence-based decision making. They also concurred with their non-clinical counterparts on its being scientific and unbiased.
Nonetheless, some opted for neither the use of experience nor quantitative techniques. The reasons given by the non-clinical healthcare managers and administrators who preferred none of the methods were that the good health outcomes recorded so far in their health facility could not be attributed to any current decision-guiding method nor would it need a new quantitative decision guiding method. For the clinically related healthcare managers and administrators who opted for none of the methods, some of them insisted that quantitative techniques were neither applicable nor needed in the health institutions as they believed they were time wasting and causes delay.
Discussion
This study compared the extent to which clinical and non-clinically related healthcare managers and administrators are familiar with quantitative techniques, use it and perceived its relevance in their day-to-day decision making in health institutions. There were no statistically significant differences in the age and sex distribution of the two populations studied. This pre-supposes that they were similar demographically.
Familiarity with the techniques
Findings from this study showed that a higher proportion of the non-clinically related healthcare managers and administrators reported being familiar with a higher number of the quantitative techniques compared with their clinical counterparts. The reasons for this could be as a result of their educational background and exposures. Some of these non-clinical healthcare managers and administrators had their first degrees in accounting and engineering from where these quantitative techniques were adopted. Brainstorming had the highest proportion of both study population who were familiar with it. Brainstorming is a qualitative technique and may not be as brain tasking as the quantitative techniques which may explain the findings. Unfortunately, brainstorming may not be able to address all manners of decision-needing scenarios, and so it is highly insufficient a technique for a health manager to possess alone. There is need to engage all other techniques in order to make quality decisions that would move the health institutions forward.
Use of quantitative techniques in decision making
More than 50% of both population of healthcare managers and administrators had never used any of the techniques assessed except for brainstorming. Many of the respondents reported that they were never taught on these techniques in their pre-service training and may not even be expected to use them as such. It may be necessary to study the persistent non-use of these quantitative techniques, and how it could have impacted on the unhealthy state of the Nigerian health system.
However, there were a statistically significantly higher proportion of the non-clinical healthcare managers and administrators compared with their clinical counterparts who said they had at some point used some techniques such as forecasting; fishbone diagram; FTE; maximax criterion; Laplace; minimax regret criterion; Hurwitz criterion; CPM and the queuing theory. If these managers still have the capacity to use these techniques, then, it will be an advantage to the health system as it will help to inform quality decision making. This will result in less wastage of resources especially when they can rightly predict expected demand for service through forecasting. It will help in the equitable distribution of resources where work done is commensurable with reward received. Their ability to apply the CPM will help to prevent all controllable factors that could lead to the abandonment of projects. The nightmares of delayed service utilisation which may eventually lead to denial of service utilisation or poor service delivery will be eradicated with the application of the queuing theory. It will equally translate to increased financial income for the health institution as well as improved good will and social image. Presenting supportive evidence with the case of Rytilä et al., 15 they used simulation modelling to increase the efficiency of blood supply chain management. They concluded that use of simulation modelling will eventually improve the allocation of resources and the overall quality of care of the patients.
Perceived relevance of the techniques
The question on relevance was included to assess their perception and readiness to begin engaging the use of quantitative techniques in guiding decision making in the health industry whenever the opportunity to be trained on it arises. Interestingly, all the techniques assessed received a reasonable acceptance of being relevant as indicated by more than 50% of both group studied except for the quantitative techniques guiding decision making under uncertainty. In support of this finding, Laffel et al. 16 proposed that healthcare organisations through their managers and administrators may well make important advances in the quality of care and service through the application of principles and techniques in industrial quality management science.
Comparison of the current methods used to guide decision making and their perceived effectiveness
Experience, featured most as the current method used or would be used by both the clinical and non-clinical healthcare managers and administrators to guide their decision making for all the decision-needing scenarios presented to them. Both groups of healthcare managers and administrators studied felt their current methods for guiding decision making were very effective. If experience featured most frequently as the current method used and it is said to be effective, then, an additional quantitative technique combined with experience will only make decision making better. Some of the respondents actually felt either of quantitative or experience may not be very effective as a stand-alone method rather, both will become more effective when combined as one could complement the other. This finding was similar to the writings of Mays et al. 17 where they opined that there is no single, agreed framework that will be sufficient for synthesising diverse forms of evidence in policy making which is based on decisions made.
Comparison of the preferred methods for decision making and reasons for their preference
The preference for quantitative techniques reported by both groups of healthcare managers and administrators studied suggests that there is an apparent readiness, willingness to be trained in quantitative techniques and adopt its use to guide decision making in this health institution of study. From the reasons given for their preference, it shows that they do have high expectations from the application of quantitative techniques. It could also be said that they might have been primed by the contents of this survey and made more aware of quantitative techniques in decision making. Hence, it is needful for the management of the health institution to provide trainings to build the capacity of their staff on quantitative techniques.
Change, though inevitable is difficult to many, however, it is constant. A few of the respondents preferred the continual use of their current methods because of the fear of possible challenges associated with a change to the use of quantitative techniques to guide decision making. However, change is necessary and may become urgent; hence, the need to better get prepared for the change in decision-making processes and move with the tide as seen in the standardised developed world. Nonetheless, they suggested that if quantitative techniques must be introduced, it must be done gradually. The simpler, easier to use result producing methods should be presented first and time allowed for the build-up of great interests in the quantitative techniques.
It will also be preferable to train people on the computer-based programs and softwares with which these quantitative techniques could be applied. These processes will in no doubt raise the status and ranking of the Nigerian health institutions in the delivery of quality health services. The techniques will also increase accountability by health managers and the need to produce results and make ‘evidence-based changes’ quoting the clinically related healthcare managers and administrators.
Conclusions
It may be inferred from this study that health managers and administrators in developing countries still base their decision making majorly on experience as shown by the low level of familiarity and use of quantitative techniques needed for experimentation or research and analytical approaches to decision making among the populations studied. Theoretically, it may be more closely aligned towards the political view which is more about personalised bargaining process. However, both acknowledged its relevance and preferred its use. With these, it is hoped that the adoption of quantitative techniques in decision making by health managers in developing countries of which Nigeria is one will be accelerated. The emergence of which is hoped to improve the quality of services provided and the health outcomes of the citizenry.
Footnotes
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Authors contributed to the financing of the research.