In health care decision-making, there are four main methods of economic evaluation. These are cost-utility analysis, cost of illness studies, cost-effectiveness analysis, and cost-benefit analysis. Each method is suitable for a given scenario as they are designed to work differently. Therefore, given a situation where an evaluation has to be done on new diabetes medication, the cost-benefit analysis would best fit. It involves a comparison of interventions and consequences wherein the costs and benefits are determined in monetary terms. This enables comparing the new medication and a preexistent one using the summary metric (Park, Jit & Wu, 2018). This facilitates decision-making based on the costs or benefits identified.
The method is preferred because it considers benefits and costs even beyond intervention length, which enhances planning. It is also data-driven, allowing for the evaluation of a decision free from personal biases or opinions (Hwang, 2016). This ensures that given models are evidence-based and agnostic, and this builds logical operationalization. Additionally, the technique is effective and efficient in uncovering unidentified costs and benefits. This is especially essential when a given solution has underlying intangible costs that are less obvious (Hwang, 2016). Such charges often lead to adverse effects on the quality of operations in an organization. Moreover, the method makes it easy to establish the best decisions by reducing benefits versus costs. It thus eliminates the complexity that other forms have present in evaluation.
However, it is limited by the fact that one cannot easily predict all variables. In most cases, if all factors are established, there is always a likelihood of unreliability and inaccuracy, especially in the long run (Park et al., 2018). It is thus as good as the available data used. Additionally, it eliminates the human element in the evaluation as it focuses exclusively on data. This makes it questionable based on the morality of such decisions in health care, mainly because the decisions affect other human beings. Moreover, it is not suitable for long-term interventions due to variable fluctuation and inconsistent timeframes (Park et al., 2018). These limitations are likely to lead to inefficient decision-making and increased risk.
Nonetheless, in the performance of a cost-benefit analysis, it is essential to gather information on costs and benefits. Such information would include annual expenditure, cost categories, overheads, resources and activities, and depreciation (Robertson, Skelly & Phillips, 2019). On the other hand, benefits would involve collecting information on the monetary value of the given intervention to clients and the organization. It is also essential to have information on discount rates and the period over which the evaluation will be done (Robertson et al., 2019). Moreover, data on alternative scenarios should be collected to know the options available for comparison and analysis (Park et al., 2018). With this information, the method can efficiently and effectively give the desired evaluation outcomes.
References
Hwang, K. (2015). Cost-benefit analysis: Its usage and critiques. Journal of Public Affairs, 16(1), 75-80.
Laitinen, E. (2019). Discounted cash flow (DCF) as a measure of startup financial success. Theoretical Economics Letters, 09(08), 2997-3020.
Park, M., Jit, M., & Wu, J. (2018). Cost-benefit analysis of vaccination: A comparative analysis of eight approaches for valuing changes to mortality and morbidity risks. BMC Medicine, 16(1), 17-23.
Robertson, L., Skelly, C., & Phillips, D. (2019). Making hard choices in local public health spending with a cost-benefit analysis approach. Frontiers in Public Health, 7, 108-117.
Rosko, M., Al-Amin, M., & Tavakoli, M. (2020). Efficiency and profitability in US not-for-profit hospitals. International Journal of Health Economics and Management, 20(4), 359-379.