Gonzalez Sepulveda Juan Marcos, Johnson F Reed, Reed Shelby D, Muiruri Charles, Hutyra Carolyn A, Mather Richard C
Department of Population Health Sciences, Duke School of Medicine, Durham, NC, USA.
Optimal Care at Optum.
Med Decis Making. 2023 Feb;43(2):214-226. doi: 10.1177/0272989X221115058. Epub 2022 Jul 29.
While clinical practice guidelines underscore the need to incorporate patient preferences in clinical decision making, incorporating meaningful assessment of patient preferences in clinical encounters is challenging. Structured approaches that combine quantitative patient preferences and clinical evidence could facilitate effective patient-provider communication and more patient-centric health care decisions. Adaptive conjoint or stated-preference approaches can identify individual preference parameters, but they can require a relatively large number of choice questions or simplifying assumptions about the error with which preferences are elicited.
We propose an approach to efficiently diagnose preferences of patients for outcomes of treatment alternatives by leveraging prior information on patient preferences to generate adaptive choice questions to identify a patient's proximity to known preference phenotypes. This information can be used for measuring sensitivity and specificity, much like any other diagnostic procedure. We simulated responses with varying levels of choice errors for hypothetical patients with specific preference profiles to measure sensitivity and specificity of a 2-question preference diagnostic.
We identified 4 classes representing distinct preference profiles for patients who participated in a previous first-time anterior shoulder dislocation (FTASD) survey. Posterior probabilities of class membership at the end of a 2-question sequence ranged from 87% to 89%. We found that specificity and sensitivity of the 2-question sequences were robust to respondent errors. The questions appeared to have better specificity than sensitivity.
Our results suggest that this approach could help diagnose patient preferences for treatments for a condition such as FTASD with acceptable precision using as few as 2 choice questions. Such preference-diagnostic tools could be used to improve and document alignment of treatment choices and patient preferences.
Approaches that combine patient preferences and clinical evidence can facilitate effective patient-provider communication and more patient-centric healthcare decisions. However, diagnosing individual-level preferences is challenging, and no formal diagnostic tools exist.We propose a structured approach to efficiently diagnose patient preferences based on prior information on the distribution of patient preferences in a population.We generated a 2-question test of preferences for the outcomes associated with the treatment of first-time anterior shoulder dislocation.The diagnosis of preferences can help physicians discuss relevant aspects of the treatment options and proactively address patient concerns during the clinical encounter.
虽然临床实践指南强调在临床决策中纳入患者偏好的必要性，但在临床诊疗过程中对患者偏好进行有意义的评估具有挑战性。将定量的患者偏好与临床证据相结合的结构化方法，有助于促进医患之间的有效沟通，并做出更以患者为中心的医疗决策。适应性联合分析或陈述性偏好方法可以识别个体偏好参数，但可能需要相对大量的选择问题，或者对偏好引出过程中的误差做出简化假设。
我们提出一种方法，通过利用患者偏好的先验信息来生成适应性选择问题，以确定患者与已知偏好表型的接近程度，从而有效地诊断患者对治疗方案结果的偏好。这些信息可用于测量敏感性和特异性，这与其他任何诊断程序非常相似。我们针对具有特定偏好特征的假设患者，模拟了不同程度选择误差的回答，以测量两问题偏好诊断的敏感性和特异性。
我们为参与先前首次前肩脱位（FTASD）调查的患者确定了4种代表不同偏好特征的类别。在两问题序列结束时，类别归属的后验概率在87%至89%之间。我们发现两问题序列的特异性和敏感性对回答误差具有鲁棒性。这些问题的特异性似乎优于敏感性。
我们的结果表明，这种方法可以通过仅使用两个选择问题，以可接受的精度帮助诊断患者对FTASD等病症治疗的偏好。这种偏好诊断工具可用于改善并记录治疗选择与患者偏好的一致性。
将患者偏好与临床证据相结合的方法，可以促进医患之间的有效沟通，并做出更以患者为中心的医疗决策。然而，诊断个体层面的偏好具有挑战性，且不存在正式的诊断工具。我们提出一种结构化方法，基于人群中患者偏好分布的先验信息，有效地诊断患者偏好。我们针对首次前肩脱位治疗相关结果生成了一个两问题的偏好测试。偏好诊断有助于医生在临床诊疗过程中讨论治疗方案的相关方面，并主动解决患者的担忧。
