Virtually all policy theorists call for formulation processes that emphasize the need for understanding the decision-making process rather than seeking decision that work towards maximizing societal utility (Alemi, 2008). It can be argued that improving and understanding the process of decision-making and clarification of policy goals can help in generation of policies attuned to individual and societal needs. Expected utility theory holds that decision makers faced with various alternatives always evaluate different alternatives independently in relation to the probability of occurrence and perceived value. This often results to computations that yield final values attached to the various options showing the maximal gain of choices. However, the prospect theory claims that a decision maker’s perception of utility could be subjectively influenced by the mode of framing the information upon which decisions are to be made. The frames could appear in form of gain or loss or risk/no risk. The reference from which the options are evaluated significantly influences decisions and policies regardless of the information reviewed (McCorduck, 2012). According to prospect theory the utility of a decision maker can derive from differing cognitive evaluations of different prospects. The utility is reflective of the manner on which the options may be framed. Research in decision making has shown that the development of utility among individuals is affected by personal preferences, emotions involved, desires, personal values and wants as well as the degrees of risk associated with outcomes (Anonymous, 2010).
In the healthcare set up the medication errors problem has been a persistent one and revelations of the extent of harm caused by medication errors has led to various policy changes. Health management teams in various facilities are often faced with the challenge of how to handle medication errors. Proposals have been made, which include training of staff to exercise more rigor in medication administration processes and the introduction of computerization and bar coding in the process of medication administration (Rothschild et al., 2011). The implementation of both measures requires significant investment in terms of time and money. Computerization and re-training of staff for system change is an expensive venture that most health facilities would rather avoid if they can because of the effect the measures may have on profitability and cost margins. Decisions in whether to make such operational and policy changes will require rational thinking based on the measures of various variables that pertain to the problem such as mortality and morbidity levels. The decision-making challenge is however significant because there may be no sufficient information upon which to base the decision making process. This is partly due to the fact that reporting of such occurrences is never well done due to fear of legal suits that commonly arise. As such, the decision makers may be unable to access the full effects of the problem as well as perform a cost-benefit analysis that could objectively support the decision whether to computerize and re-train for system changes in medication delivery. Faced with such a challenge decision makers may have to make decisions without proper and full information on the problem’s risk levels and losses. This situation may make use of bounded rationality as an approach in reaching a decision because of its flexibility (Jones, 1999). This is because the approach is best suited to situations where there is a deficiency in information required for decision-making (McCaughey & Bruning, 2010). The implementation of such a measure may also be significantly influenced by other factors such as ethical and legal considerations, which may have nothing to do with a cost-benefit analysis framework as would be the case in a typically rational decision-making process. In such cases, the management will most probably be faced with a limitation on information availability and therefore, any decision making may be based on a multiple of other factors.
Alemi, F. (2008). Introduction to decision analysis, retrieved from http://gunston.gmu.edu/730/IntroductionToDecisionAnalysis.asp
Anonymous (2010). Decision making in healthcare facilities, retrieved from http://labs.fme.aegean.gr/decision/images/stories/docs/HealthcareFacilities_decisionmaking
Jones, D. B. (1999). Bounded rationality. Annual Review Political Science 2 (1), 297–321
McCaughey, D. & Bruning, S. N. (2010). Rationality versus reality: the challenges of evidence-based decision making for health policy makers. Implementation Science, 5 (1) 39-46
McCorduck, P. (2012). Bounded Rationality, Sunstone Press
Rothschild, M. J. Bates, W. D. Seger, L. D. Keohane, A. C. Seger, C. A. & Moniz, T. T. (2011). Addition of electronic prescription transmission to computerized prescriber order entry: Effect on dispensing errors in community pharmacies; American Journal of Health-System Pharmacy, 68 (2): 158-163