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Clinical decision making using decision trees in nursing scenarios

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What does decision analysis entail?

Decision analysis is used when one has many choices to pick from and no clear choice or action is available. It is also used when patients or decision-makers have to be informed using the various possible outcomes. It follows the theory of decision-making termed as subjective expected utility theory. The theory is a normative model because it outlines the actions followed by a decision-maker when using available information in a rational and logical manner. The theory postulates that a decision maker has to take the highest probable option that would lead to an outcome that matches his or her values. To match the outcomes with the values is referred to as maximizing expected utility.

When using decisions analysis, one constructs a decision tree for the presented problems. Once a tree is made, numerical values are added to the various branches. An analysis is then executed and the “optimum” choice identified. The branches represent the values attached to the outcome and the probability of that outcome occurring. An advantage of using the decision-analytic approach is that it allows one to integrate evidence from research together with the considered choices. Generally, the probabilities selected for the decision models are derived from research evidence which makes it clear to understand the degree of uncertainty that is linked to the outcomes. It also increases the clarity of the choices made. Hence, for some decision problems, a decision analysis that involves diagnostic and intervention may be appropriate to help in assisting clinicians to make evidence-based decisions.

Clinical practice and decision analysis

When it comes to clinical practices, decisions taken are always under uncertain conditions. These decisions occur in circumstances that can be termed as “irreducible” uncertainty. Like when dealing with leg ulcers, it is hard to establish if an ulcer will recur and if a patient will adhere to a suggested treatment. Additionally, the final decision on ulcer management may depend on the feeling of patients towards a treatment instead of considering if the patient has a high probability of developing another ulcer.

How to structure the decision.

With the leg ulcer management case provided above, three decision arises.  These are the possible choices to follow:

  • One can choose to get no treatment.
  • Use high-compression hosiery
  • Wear a moderate-compression hosiery.

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Building the tree, one incorporates the different unknowns that may occur by chance. These are if the patient will adhere to a given treatment and if the leg ulcer will recur or not. In a tree, the first set of branches are referred to as decision nodes. They represent the situations where one can make a choice or not about an issue. The next set of branches are known as chance nodes. The chance nodes represent the decision points where one cannot predict an outcome with certainty.

Chance and utilities

In the decision tree, numbers are assigned to the chance nodes to indicate the degree of uncertainty in the decision made.  In a research done by Nelson et al (2003), they captured the values that can assist in the case scenario presented above on leg ulcers. They estimated that: An ulcer has a 64% chance or recurring if it is not treated. The probability of patient adhering to the various options available is 57% for the high-compression treatment and 82% for the moderate-compression treatment.  Still, the research indicated that the likelihood of the ulcer recurring if one adhered to the treatment was 34%for the moderate compression treatment and 32% for the high-compression treatment.

The probabilities selected are always indicated using percentages of decimals. It is always important to remember that the probabilities in all the chance branches from a node are supposed to add up to 100% or 1 if one opts to use the decimals.  At the end of the branches, one places some numbers which signify the utilities linked to each outcome. The numbers are chosen according to the weight the decision maker associates to a particular outcome. The utilities are mostly measured on a scale of zero to one hundred. Zero represents the worst possible outcome that can occur whereas 100 represent the best possible outcome that can occur. Utilities can be calculated in various ways and online articles can offer extensive details and concepts to follow when calculating the utilities.

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An issue that develops when using utilities is whose measured utilities should be incorporated in the model and the manner they should be evaluated. The issue arises because one can either use the estimated value of the practitioners on their thought on what patient attach to outcomes or the patient’s own evaluations.  Research indicated that the utilities from practitioners and patient are varying depending on the outcomes intended.  It is, therefore, crucial to be certain on the way different utilities affects the results of the analysis. Apart from individual utilities, one can also use the societal level evaluations like the quality-adjusted life years- QALY. This makes it imperative to be conscious of the source of utility ratings when looking at the results of any analysis. One needs to assess the similarity of individual patient values in comparison to that used by the decision model. Like in the management of the leg ulcer 0.35 is the value given to a leg ulcer and having pain at rest. The figure is derived from an off the shelf measure of utility instead of using an individualized utility. The figures have been calculated by Harvard and presented in a table. The table outlines various utility values for various health conditions.

Calculating decisions options values.

After assigning values to the various decisions present, one has to calculate the option which has the highest value. The chosen value logically represents the option with the highest probability of maximizing the individual’s preferences. To get the values one multiplies the utility and probability values assigned to each chance outcome and then add them up. The tree is folded back from right to left until one gets a final value for each of the calculated decision.  From the projected figures of the leg ulcers given, the expected value for having no treatment is 0.58, the one for treatment using a high-compression hosiery is 0.7 whereas that for treatment using a moderate-compression hosiery is 0.74.  From the obtained values, the best decision that one can select to treat a patient with a leg ulcer is the one with the moderate-compression hosiery.

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Sensitivity analysis

The results achieved in a decision analysis depends on the values assigned in the decision model. To determine the way the variability in the values affects the results one calculates a sensitivity analysis. For the case provided on leg ulcers, an uncertainty is attributed to the probability of the ulcer recurring without the treatment.  An uncertainty also arises when one ponders if the patient will comply with the treatment selected. A patient may even have a varying value linked with the likelihood of living with a leg ulcer from that presented by the model.

Sensitivity analysis establishes the way the optimum option would behave in different decisions context.  It helps in identifying a threshold where a value change results in a change in the optimum treatment decision. Like the case study provided, when one carries out a sensitivity analysis, one realizes that the optimum treatment option selected is not affected by the change in patient value. Even the recurrence rate of the ulcers is not affected when an individual is treated with a moderate-compression hosiery. However, the decision is sensitive to change when looking at the likelihood of the ulcer recurring without the treatment and even when looking at patient compliance with the two types of treatment.

Practice implications

According to the case provided above, the decision to recommend a compression treatment or not so that one can prevent the leg recurrence of leg ulcers depends on the likelihood of the patient complying with the treatment regime and also the likelihood of the ulcers recurring. It demonstrates the need to use decision analysis as a tool to aid in resolving clinical problems. It allows one to apply the available research evidence to individual patient situations.  If one had a patient who has a 39% ulcer recurrence probability and the compliance to the high compression is low, the best optimum treatment to pick the moderate-compression hosiery.

In the past, decision analysis has been mostly applied in medical practice rather than in nursing practice.  But few pieces of research have used the approach. In a given research, some researchers used the tool to establish suitable interventions to use when handling psychiatric patients who are at risk of violence. Additionally, it was used to analyze the effectiveness of four debriding agents used in the management of pressure ulcers. The only setback was that the two pieces of research used estimates of probability instead of using an evidence-based research. They have also not been incorporated in practice. The effectiveness and usefulness of decisions analysis within nursing have not been fully tested in everyday clinical situations.

Nevertheless, the tool has been used as a teaching aid for nursing student s learning on how to use clinical decision analysis and it has made them make significantly better decisions.  This has been showcased by comparing them with a group of experts while using a control group. That notwithstanding decision analysis is not applicable to every problem in the nursing practice. It can only be applied in circumstances where one has a choice among alternative actions, has sufficient time to ponder on various alternatives, the patient views are essential and does not need an immediate right answer.

When using the tool in such circumstances, it is essential to explicitly use research evidence to inform the decision made. The tool is effective as the assumptions used to inform the model initially and it relies on the provision of probability estimates that are accurate. If one cannot access or generate probabilities from research evidence, one should get experts estimates on the probability of the events occurring. However, these estimates have to be subjected to a type of bias. The numerical values assigned to the preferences of an individual also raises some concerns.  Nonetheless, the decision analysis as a tool has some benefits. This is because it makes the intrinsic assumptions in the decision process clear. With its clarity, it opens up discussion and debate. This makes it a better tool in comparison to other forms of decisions making used in nursing practice like intuition.

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Conclusion

Decision analysis is an essential tool that can be used by nurses because it offers one a platform to allow to deal with problematic decisions. With the technique, one examines in detail these problems, utilizes evidence-based research to make a decision and recommends the best solution in complex situations where one do not have the right and ready answer. Additionally, the tool supports holistic care because patients and caregivers get an opportunity to express their feeling of receiving various treatment options. Since the decisions are set in a structure that resembles a tree, it becomes easy to establish how and why any decision was chosen. Since the path taken is clear, one gets a valuable basis to reflect and disco on the clinical decisions within the nursing practice.


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