Anesthesia demands a lot from physicians in regards to decision making and technical capability, the former especially usually influenced by pressure and frequently changing medical situations. Traditionally, trainers emphasize more on technical abilities as well as knowledge acquisition. But besides behavioral elements of simulation, there is little emphasis on the decision-making process. The author of this article describes the application of a modified way of decision evaluation for helping in the decision-making process both in training a more structured method to trainees and functioning in a clinical situation.

Traditional Decision Analysis

Decision evaluation is an approach that assists in the decision-making process, taking into consideration all probable outcomes and score them based on their occurrence probability and utility. That enables a person to designate a specific decision as better than the others. Decision-makers derive the probabilities from published works and obtain utilities from interviewing individual patients.One example is the case of a person suffering from a hypothetical tumor. In such a scenario, three possible treatment alternatives are drug therapy, surgery, no therapy at all. While both drug treatment and surgery have higher chances of curing the tumor, the latter carries the potential of a significant disfigurement while the former could lead to kidney failure. With no therapy, there is little room for an unprecedented remission.

The table below illustrates the probabilities of all the potential outcomes. Most doctors are familiar with this tabular illustration but not the patients. Additionally, the tabulation does not offer and intuitive demonstration of the probable paths through which a patient could pass.

Table 1

Decision analysis is essential since it assists doctors to choose between the therapy alternatives as follows. The physician first outlines the treatment options graphically through a decision tree, as illustrated in Fig. 1, except for the grey columns. The tree displays every possible outcome for each treatment, and the nodes signify the paths influenced by decisions and those that the decision cannot impact, that is, decision and chance nodes.

Figure 2

This decision tree is an advancement from traditional tabular illustration since most people understand graphical representation of risks and choices quite easily. Next, the clinicians assist the patient in assigning a utility to all outcomes often between zero and one. They apply different techniques to achieve this task ranging from analogies based on trade-offs or gambling to simple linear soring.

If a patient allocates a utility of zero (0) to death and one (1.0) to survival while renal failure and disfigurement score 0.50, respectively, then multiplying the probability of every probable result in Table 1 by these utilities gives composite values for all options as illustrated by the grey column in Figure 1. From this example, the sum of the treatment values implies that drug treatment with a total of 0.55 is the best alternative for the patient.This procedure may appear complicated at first, and the outcome quite obvious. Still, the power of decision evaluation lies in its capability to integrate published proof and design treatment for each patient’s preference. For instance, an athlete patient may treasure physical health over appearance and thus assign disfigurement as a higher value than renal failure. As such, the patient would prefer surgery over drug treatment. One can also study the impact of various probabilities.

Also, this technique introduces a more comprehensive and rational approach to the medical decision-making process that is usually absent in the conventional, unstructured method. The traditional approach is often influenced by a distorted risk impression and anecdotal experience. Moreover, studies indicate that experienced professionals tasked with making rapid decisions, specially selects the initial plausible that comes to mind without comparing it with other options.

Shortcomings

First, there could be little or no published literature from which to derive probabilities, particularly in fields like anesthesia. For instance, the likelihood of complications resulting from using spinal anesthesia in a rare disorder could be unknown. Secondly, there could be many alternative treatments that could complicate the process if included in the procedure.For instance, in the hypothetical scenario above, a patient can undergo surgery followed by drug therapy and vice versa. Alternatively, there could be various kinds of operations or drug treatments, each with a unique range of possible outcomes. Third, it could be impossible or challenging to acquire a significant utility value. For example, the results could be so complicated for the patient to comprehend their actual consequences, or the outcomes could have insignificant relevance to the patient.

Fourth, the procedure is more complicated compared to the traditional unstructured decision-making process. Lastly, both doctors and patients could hesitate to take part in the process since it reduces the art of medicine to a numbers system, and the physicians could be less conversant with. They have to ignore their usually respected gut feelings.These shortcomings have contributed to the limited integration of decision analysis into medical practice. In anesthesia, there is only an occasional illustration of the technique often restricted to choices influenced by economics. For example, factors like side effects and cost determine the method to use instead of patients’ needs.

Modified Decision Analysis

The author proposes a method that retains several advantages of the conventional approach. These advantages include a more graphical and structured path towards decision-making and integration of proof where it is available but without dependence on quantitative analysis or patients’ scores possible outcomes. Instead, this method emphasizes clinical decision making and the doctors’ experience value instead of consulting patients.As such, this approach does not supersede the traditional technique in this respect. Additionally, by integrating the experience of anesthetists to substitute hard medical proof where it lacks, this approach loses the element of objectivity of the simple decision evaluation method. Nevertheless, in this aspect, this method closely rhymes with an experience-dependent naturalistic decision-making process followed by most professionals.The author argues that the proposed method has value as a clinical and teaching aid, as illustrated by the following hypothetical cases. Under the obstetric settings, the clinical decision-making process is usually under absolute pressure, and thus the following illustrations refer to this practice area.

Figure 2

Analysis of the Decision

The primary decision in this scenario is whether to apply general or regional anesthesia. There is a challenge that exists with conventional intuitive decision-making is such an acute case. While one might find it easy to regard the next option facing a person, it is quite challenging to proceed and consider the likely results of the options regarding frequency and severity.In such a case, it is possible to be unexpectedly influenced by individual experiences, and specific emphasis on present teaching such that the prospect of failed or challenging intubation overrides other concerns. A more graphical and structured analysis allows a person to study this decision.

Lastly, this technique influences a person to ask focused and practical literature questions. For instance, evaluating the probability of a woman treated with magnesium sulfate developing convulsions. Such information is essential in obstetric anesthetic practice, although few people are familiar with it, nor is it quickly obvious. For example, the landmark publication that determined magnesium sulfate as a preferable therapy after eclampsia did not cover this information.

Conclusion

Several theories exist that explaining various clinical decision-making processes. The modified decision analysis approach described in this article is one of the several models applied in this field. This method is significant as a teaching tool and for aiding clinical decision-making during retrospective and planning analyses of challenging cases. Nonetheless, it does eliminate standard approaches to choosing between two alternatives, especially in situations requiring immediate or urgent decisions. However, the method emphasizes the significance of applying existing evidence in a more focused and structured way that the ones often applied and positions this in the context of anesthetists’ experience. Therefore, one can use this method to complement the action of choosing therapy options for complicated scenarios.

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