BACKGROUND In shared decision-making, a key step is quantifying the patient's preferences in relation to all the possible outcomes of the compared clinical options. According to utility theory, this can be done by eliciting utility coefficients (UCs) from the patient. The obtained UCs are then used in decision models (e.g., decision trees). The elicitation process involves the choice of one or more elicitation methods, which is not easy for decision-makers who are unfamiliar with the theoretical framework. Moreover, to our knowledge there are no tools that integrate functionalities for UC elicitation with functionalities to run decision models that include the elicited values. OBJECTIVES The first aim of this work is to provide decision support to the clinicians for the selection of the elicitation method. The second aim is to bridge the gap between UC elicitation and the exploitation of those UCs in shared decision-making. METHODS Based on evidence from the utility theory literature, we developed a set of production rules that recommend the optimal elicitation method(s) according to the patient's profile and health state. We then complemented this decision support tool with a functionality for quantifying and running decision trees defined through the commercial software TreeAge. RESULTS The result is an integrated framework for shared decision-making. Given the primary aim of this work, we focus for result evaluation on the elicitation tool. It was tested on 51 volunteers, who expressed UCs for four purposely selected health states. The insights on the collected UCs validated the rules included in the decision support system. The usability of the tool was assessed through the System Usability Scale, obtaining positive results. CONCLUSION We developed an integrated environment to facilitate shared decision-making in the clinical practice. The next step is the validation of the entire framework and its use besides shared decision-making. As a matter of fact, it may also be exploited to target cost-utility analysis to a specific patient population.

中文翻译：

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在共享决策的集成环境中淘汰和利用效用系数。

背景技术在共同的决策中，关键步骤是相对于所比较的临床选择的所有可能结果量化患者的偏好。根据效用理论，这可以通过从患者身上得出效用系数（UCs）来完成。然后，将获得的UC用于决策模型（例如，决策树）。激发过程涉及一种或多种激发方法的选择，这对于不熟悉理论框架的决策者来说并不容易。而且，据我们所知，没有工具可以将UC激发的功能与可以运行包含所激发值的决策模型的功能相集成。目的这项工作的首要目的是为临床医生选择诱发方法提供决策支持。第二个目标是弥合UC启发与共享决策中对这些UC的利用之间的差距。方法基于效用理论文献的证据，我们制定了一套生产规则，根据患者的状况和健康状况推荐了最佳诱导方法。然后，我们为该决策支持工具提供了补充功能，该功能可量化和运行通过商业软件TreeAge定义的决策树。结果结果是一个用于共享决策的集成框架。鉴于这项工作的主要目的，我们将重点放在启发工具上进行结果评估。在51位志愿者中进行了测试，他们表达了针对四种有选择的健康状态的UC。对收集的UC的见解验证了决策支持系统中包含的规则。通过系统可用性量表评估了该工具的可用性，获得了积极的结果。结论我们开发了一个集成环境，以促进临床实践中的共同决策。下一步是验证整个框架及其在共享决策中的用途。实际上，也可以利用它来针对特定患者人群进行成本效用分析。