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在线患者反馈作为安全阀:未被注意到和未解决的安全事件的自动化语言分析。

Online patient feedback as a safety valve: An automated language analysis of unnoticed and unresolved safety incidents.

机构信息

Department of Psychological & Behavioural Science, London School of Economics, London, UK.

Department of Psychology, Oslo New University College, Oslo, Norway.

出版信息

Risk Anal. 2023 Jul;43(7):1463-1477. doi: 10.1111/risa.14002. Epub 2022 Aug 9.

Abstract

Safety reporting systems are widely used in healthcare to identify risks to patient safety. But, their effectiveness is undermined if staff do not notice or report incidents. Patients, however, might observe and report these overlooked incidents because they experience the consequences, are highly motivated, and independent of the organization. Online patient feedback may be especially valuable because it is a channel of reporting that allows patients to report without fear of consequence (e.g., anonymously). Harnessing this potential is challenging because online feedback is unstructured and lacks demonstrable validity and added value. Accordingly, we developed an automated language analysis method for measuring the likelihood of patient-reported safety incidents in online patient feedback. Feedback from patients and families (n = 146,685, words = 22,191,427, years = 2013-2019) about acute NHS trusts (hospital conglomerates; n = 134) in England were analyzed. The automated measure had good precision (0.69) and excellent recall (0.98) in identifying incidents; was independent of staff-reported incidents (r = -0.04 to 0.19); and was associated with hospital-level mortality rates (z = 3.87; p < 0.001). The identified safety incidents were often reported as unnoticed (89%) or unresolved (21%), suggesting that patients use online platforms to give visibility to safety concerns they believe have been missed or ignored. Online stakeholder feedback is akin to a safety valve; being independent and unconstrained it provides an outlet for reporting safety issues that may have been unnoticed or unresolved within formal channels.

摘要

安全报告系统在医疗保健中被广泛用于识别患者安全风险。但是,如果工作人员没有注意到或报告事件,它们的有效性就会受到影响。然而,患者可能会观察并报告这些被忽视的事件,因为他们经历了这些后果,并且具有高度的积极性,独立于组织。在线患者反馈可能特别有价值,因为它是一种报告渠道,允许患者在没有后果担忧(例如匿名)的情况下进行报告。利用这种潜力具有挑战性,因为在线反馈是无结构的,缺乏可证明的有效性和附加值。因此,我们开发了一种自动语言分析方法,用于测量在线患者反馈中患者报告的安全事件的可能性。对来自英格兰的患者和家属(n=146685,单词=22191427,年份=2013-2019)关于急性 NHS 信托基金(医院集团;n=134)的反馈进行了分析。自动测量方法在识别事件方面具有良好的精度(0.69)和优异的召回率(0.98);与员工报告的事件独立(r=-0.04 至 0.19);并且与医院级别的死亡率相关(z=3.87;p<0.001)。识别出的安全事件通常被报告为未被注意到(89%)或未解决(21%),这表明患者使用在线平台来关注他们认为被忽视或忽略的安全问题。在线利益相关者反馈类似于安全阀;它是独立和不受限制的,为报告可能在正式渠道中未被注意到或未解决的安全问题提供了一个出口。

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