Optimal approach to improving trauma triage decisions: a cost-effectiveness analysis

Abstract

Objectives: To identify the optimal target of a future intervention to improve physician decision making in trauma triage.

Study design: Comparison of incremental costeffectiveness ratios (ICERs) of current practice with hypothetical interventions targeting either physicians' decisional thresholds (attitudes toward transferring patients to trauma centers) or perceptual sensitivity (ability to identify patients who meet transfer guidelines).

Methods: Taking the societal perspective, we constructed a Markov decision model, drawing estimates of triage patterns, mortality, utilities, and costs from the literature. We assumed that an intervention to change the decisional threshold would reduce undertriage but also increase overtriage more than an intervention to change perceptual sensitivity. We performed a series of 1-way sensitivity analyses and studied the most influential variables in a Monte Carlo simulation.

Results: The ICER of an intervention to change perceptual sensitivity was $62,799 per qualityadjusted life-year (QALY) gained compared with current practice. The ICER of an intervention to change the decisional threshold was $104,975/ QALY gained compared with an intervention to change perceptual sensitivity. These findings were most sensitive to the relative cost of hospitalizing patients with moderate to severe injuries and their relative risk of dying at non-trauma centers. In probabilistic sensitivity analyses, at a willingness-to-pay threshold of $100,000/QALY gained, there was a 62% likelihood that an intervention to change perceptual sensitivity was the most cost-effective alternative.

Conclusions: Even a minor investment in changing decision making in trauma triage could greatly improve quality of care. The optimal intervention depends on the characteristics of the individual trauma systems.

Figure 1. The cost-effectiveness decision model

The square node on the left represents the decision to participate in an intervention that improves compliance with clinical practice guidelines or not. The hollow circular nodes represent downstream events following each decision. Patients seen at non-trauma centers and either admitted or transferred will then enter into a time-dependent Markov process, represented by a circular nodes with an “M” (sub-tree). Patients cycle through the health states in the sub-tree (injured, well, or disabled) until they die either as a consequence of their injury or because of age-adjusted life-expectancy.

Figure 2. Modeling physician decision-making in trauma using the framework of signal detection theory

Perceptual sensitivity is the ability to discriminate between patients who do and do not meet clinical guidelines for transfer. Decisional threshold is a willingness to tolerate different types of mistakes. An illustrative decision threshold, is shown, meaning that values above the threshold will elicit a decision to transfer. Varying decisional thresholds will yield different false positive (p[FP]) and true positive (p[TP]) probabilities.

Figure 3. Three way sensitivity analysis

The shaded areas to the right of each diagonal represent different combinations of relative effectiveness and cost at which the intervention to change perceptual sensitivity costs less than $100,000 per QALY gained. For example, for the line denoting a ratio of 1.9, all three shaded areas depict the area where changing perceptual sensitivity is favored. The areas to the left of each diagonal line represent combinations of effectiveness and cost at which either no intervention (ratio=1.9) or the intervention to change decisional thresholds (ratio=1.7 and 1.5) is favored.