患者推理:患者和护理伙伴对急诊护理诊断准确性的看法。

IF 3.1 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Medical Decision Making Pub Date : 2024-01-01 Epub Date: 2023-11-15 DOI:10.1177/0272989X231207829
Vadim Dukhanin, Kathryn M McDonald, Natalia Gonzalez, Kelly T Gleason
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引用次数: 0

摘要

目的:在验证急诊科或紧急护理中特定诊断准确性的患者报告测量的背景下,本研究调查了患者和护理伙伴对诊断准确性的看法,并探讨了他们在评估准确性时如何推理的变化。方法:在2022年2月,我们调查了一个在过去一个月去过急诊科或急诊就诊的全国成年人小组,以测试一项患者报告的措施。作为调查验证的一部分,我们要求自由文本回答为什么受访者表示他们(不)同意包括患者报告的诊断准确性的2个陈述:1)他们收到的健康问题的解释是真实的,2)解释描述了对健康问题的期望。根据归纳产生的主题对配对的自由文本回答进行定性分析。结果:共有1116名患者和护理伙伴提供了982个回答,编码为10个主题,并进一步分为3种推理类型。几乎三分之一(32%)的受访者在评估健康问题解释的准确性时仅使用确证推理(将解释与测试结果、患者随后的健康轨迹、他们的医学知识、症状或另一位医生的意见保持一致),26%仅使用基于感知的推理(对诊断过程的感知、所接受解释的不确定性或临床团队的态度),27%使用两种类型的推理。剩下的15%使用一般信念或不明确的逻辑(仅用于准确的诊断),以及将一般推理与基于感知和确证的推理相结合。结论:患者和护理伙伴在评估诊断准确性时使用多方面推理。随着医疗保健转向有意义的诊断合作生产和共同决策,深入了解患者推理和心理模型的变化,为临床实践提供信息。重点:对982份回复的分析检查了患者和护理伙伴如何判断他们在急诊或紧急护理中得到的诊断的准确性。在推理中,人们使用他们对过程的感知,以及诊断是否与他们拥有的其他事实信息相匹配。我们在诊断测量背景下引入“患者推理”,作为进一步研究的领域,为诊断共享决策和健康的共同生产提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Patient Reasoning: Patients' and Care Partners' Perceptions of Diagnostic Accuracy in Emergency Care.

Objectives: In the context of validating a measure of patient report specific to diagnostic accuracy in emergency department or urgent care, this study investigates patients' and care partners' perceptions of diagnoses as accurate and explores variations in how they reason while they assess accuracy.

Methods: In February 2022, we surveyed a national panel of adults who had an emergency department or urgent care visit in the past month to test a patient-reported measure. As part of the survey validation, we asked for free-text responses about why the respondents indicated their (dis)agreement with 2 statements comprising patient-reported diagnostic accuracy: 1) the explanation they received of the health problem was true and 2) the explanation described what to expect of the health problem. Those paired free-text responses were qualitatively analyzed according to themes created inductively.

Results: A total of 1,116 patients and care partners provided 982 responses coded into 10 themes, which were further grouped into 3 reasoning types. Almost one-third (32%) of respondents used only corroborative reasoning in assessing the accuracy of the health problem explanation (alignment of the explanation with either test results, patients' subsequent health trajectory, their medical knowledge, symptoms, or another doctor's opinion), 26% used only perception-based reasoning (perceptions of diagnostic process, uncertainty around the explanation received, or clinical team's attitudes), and 27% used both types of reasoning. The remaining 15% used general beliefs or nonexplicated logic (used only about accurate diagnoses) and combinations of general reasoning with perception-based and corroborative.

Conclusions: Patients and care partners used multifaceted reasoning in their assessment of diagnostic accuracy.

Implications: As health care shifts toward meaningful diagnostic co-production and shared decision making, in-depth understanding of variations in patient reasoning and mental models informs use in clinical practice.

Highlights: An analysis of 982 responses examined how patients and care partners reason about the accuracy of diagnoses they received in emergency or urgent care.In reasoning, people used their perception of the process and whether the diagnosis matched other factual information they have.We introduce "patient reasoning" in the diagnostic measurement context as an area of further research to inform diagnostic shared decision making and co-production of health.

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来源期刊
Medical Decision Making
Medical Decision Making 医学-卫生保健
CiteScore
6.50
自引率
5.60%
发文量
146
审稿时长
6-12 weeks
期刊介绍: Medical Decision Making offers rigorous and systematic approaches to decision making that are designed to improve the health and clinical care of individuals and to assist with health care policy development. Using the fundamentals of decision analysis and theory, economic evaluation, and evidence based quality assessment, Medical Decision Making presents both theoretical and practical statistical and modeling techniques and methods from a variety of disciplines.
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