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填补安全指标的空白:开发以患者为中心的框架,以识别和分类与门诊护理中的诊断过程相关的患者报告的故障。

Filling a gap in safety metrics: development of a patient-centred framework to identify and categorise patient-reported breakdowns related to the diagnostic process in ambulatory care.

机构信息

Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA

Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.

出版信息

BMJ Qual Saf. 2022 Jul;31(7):526-540. doi: 10.1136/bmjqs-2021-013672. Epub 2021 Oct 16.

Abstract

BACKGROUND

Patients and families are important contributors to the diagnostic team, but their perspectives are not reflected in current diagnostic measures. Patients/families can identify some breakdowns in the diagnostic process beyond the clinician's view. We aimed to develop a framework with patients/families to help organisations identify and categorise patient-reported diagnostic process-related breakdowns (PRDBs) to inform organisational learning.

METHOD

A multi-stakeholder advisory group including patients, families, clinicians, and experts in diagnostic error, patient engagement and safety, and user-centred design, co-developed a framework for PRDBs in ambulatory care. We tested the framework using standard qualitative analysis methods with two physicians and one patient coder, analysing 2165 patient-reported ambulatory errors in two large surveys representing 25 425 US respondents. We tested intercoder reliability of breakdown categorisation using the Gwet's AC1 and Cohen's kappa statistic. We considered agreement coefficients 0.61-0.8=good agreement and 0.81-1.00=excellent agreement.

RESULTS

The framework describes 7 patient-reported breakdown categories (with 40 subcategories), 19 patient-identified contributing factors and 11 potential patient-reported impacts. Patients identified breakdowns in each step of the diagnostic process, including missing or inaccurate main concerns and symptoms; missing/outdated test results; and communication breakdowns such as not feeling heard or misalignment between patient and provider about symptoms, events, or their significance. The frequency of PRDBs was 6.4% in one dataset and 6.9% in the other. Intercoder reliability showed good-to-excellent reliability in each dataset: AC1 0.89 (95% CI 0.89 to 0.90) to 0.96 (95% CI 0.95 to 0.97); kappa 0.64 (95% CI 0.62, to 0.66) to 0.85 (95% CI 0.83 to 0.88).

CONCLUSIONS

The PRDB framework, developed in partnership with patients/families, can help organisations identify and reliably categorise PRDBs, including some that are invisible to clinicians; guide interventions to engage patients and families as diagnostic partners; and inform whole organisational learning.

摘要

背景

患者及其家属是诊断团队的重要成员,但他们的观点并未反映在当前的诊断措施中。患者/家属可以识别出超出临床医生视角的一些诊断过程中的失误。我们旨在与患者/家属共同制定一个框架,帮助组织识别和分类患者报告的诊断过程相关失误(PRDBs),以为组织学习提供信息。

方法

一个由患者、家属、临床医生以及诊断错误、患者参与和安全以及以用户为中心的设计方面的专家组成的多利益相关者顾问小组共同制定了一个用于门诊护理的 PRDB 框架。我们使用两位医生和一位患者编码员使用标准的定性分析方法对该框架进行了测试,对两项代表 25425 名美国受访者的大型调查中的 2165 例患者报告的门诊差错进行了分析。我们使用 Gwet 的 AC1 和 Cohen 的 kappa 统计量来测试分类的代码间可靠性。我们认为 0.61-0.8 表示良好的一致性,0.81-1.00 表示极好的一致性。

结果

该框架描述了 7 种患者报告的失误类别(40 个子类别)、19 种患者确定的促成因素和 11 种潜在的患者报告的影响。患者识别出诊断过程中每一步的失误,包括遗漏或不准确的主要关注点和症状;缺失/过时的测试结果;以及沟通失误,例如未被倾听或患者与提供者之间对症状、事件或其重要性的理解不一致。一个数据集的 PRDB 发生率为 6.4%,另一个数据集为 6.9%。在每个数据集的代码间可靠性都显示出良好到极好的可靠性:AC1 为 0.89(95%CI 0.89-0.90)到 0.96(95%CI 0.95-0.97);kappa 为 0.64(95%CI 0.62-0.66)到 0.85(95%CI 0.83-0.88)。

结论

与患者/家属合作制定的 PRDB 框架可以帮助组织识别和可靠地分类 PRDBs,包括一些临床医生看不见的失误;指导患者和家属参与诊断的干预措施;并为整个组织学习提供信息。

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