在计算医院报告卡上基于消费者的综合衡量标准时征求患者和医生的偏好:两个离散选择实验的结果

Martin Emmert, Stefan Rohrbacher, Florian Meier, Laura Heppe, Cordula Drach, Anja Schindler, Uwe Sander, Christiane Patzelt, Cornelia Frömke, Oliver Schöffski, Michael Lauerer
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引用次数: 0

摘要

目的 计算综合指标是减少医院报告卡数据量的一种广泛使用的策略。因此,本研究旨在了解并比较患者和转诊医生对公开医院质量信息的偏好。方法基于系统的文献综述和定性分析,采用两个离散选择实验(DCE)来了解患者和转诊医生的偏好。离散选择实验采用分数因子设计。除了五个相同的属性外,每个研究组还分别确定了一个特定属性。总计纳入了 322 名患者(平均年龄 68.99 岁)和 187 名转诊医生(平均年龄 53.60 岁)。我们的模型显示,所有属性的系数都很明显(各系数均为 0.001)。患者对 "术后并发症发生率"(20.6%;水平范围 1.164)的评价最高,其次是 "出院时的行动能力"(19.9%;水平范围 1.127)和 "治疗病例数"(18.5%;水平范围 1.045)。相比之下,转诊医生最看重 "一年翻修手术率"(30.4%;水平范围为 1.989),其次是 "治疗病例数"(21.0%;水平范围为 1.372)和 "术后并发症发生率"(17.2%;水平范围为 1.123)。这可能会在计算基于消费者权重的综合指标时产生影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The elicitation of patient and physician preferences for calculating consumer-based composite measures on hospital report cards: results of two discrete choice experiments

The elicitation of patient and physician preferences for calculating consumer-based composite measures on hospital report cards: results of two discrete choice experiments

Purpose

The calculation of aggregated composite measures is a widely used strategy to reduce the amount of data on hospital report cards. Therefore, this study aims to elicit and compare preferences of both patients as well as referring physicians regarding publicly available hospital quality information

Methods

Based on systematic literature reviews as well as qualitative analysis, two discrete choice experiments (DCEs) were applied to elicit patients’ and referring physicians’ preferences. The DCEs were conducted using a fractional factorial design. Statistical data analysis was performed using multinomial logit models

Results

Apart from five identical attributes, one specific attribute was identified for each study group, respectively. Overall, 322 patients (mean age 68.99) and 187 referring physicians (mean age 53.60) were included. Our models displayed significant coefficients for all attributes (p < 0.001 each). Among patients, “Postoperative complication rate” (20.6%; level range of 1.164) was rated highest, followed by “Mobility at hospital discharge” (19.9%; level range of 1.127), and ‘‘The number of cases treated” (18.5%; level range of 1.045). In contrast, referring physicians valued most the ‘‘One-year revision surgery rate’’ (30.4%; level range of 1.989), followed by “The number of cases treated” (21.0%; level range of 1.372), and “Postoperative complication rate” (17.2%; level range of 1.123)

Conclusion

We determined considerable differences between both study groups when calculating the relative value of publicly available hospital quality information. This may have an impact when calculating aggregated composite measures based on consumer-based weighting.

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