关键词: incident‐reporting natural language processing online feedback patient safety

Mesh : Humans Patient Safety Feedback England Risk Management / methods Medical Errors / prevention & control Internet Safety Management / methods

来  源:   DOI:10.1111/risa.14002

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:
安全报告系统广泛用于医疗保健,以识别患者安全的风险。但是,如果工作人员不注意或报告事件,其有效性就会受到损害。患者,然而,可能会观察和报告这些被忽视的事件,因为它们经历了后果,积极性很高,独立于组织。在线患者反馈可能特别有价值,因为它是一个报告渠道,允许患者报告而不必担心后果(例如,匿名)。利用这种潜力是具有挑战性的,因为在线反馈是非结构化的,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安全事件的可能性.分析了患者和家庭(n=146,685,单词=22,191,427,年份=2013-2019)对英格兰急性NHS信托(医院集团;n=134)的反馈。自动测量在识别事件方面具有良好的准确性(0.69)和出色的召回率(0.98);独立于员工报告的事件(r=-0.04至0.19);并且与医院死亡率相关(z=3.87;p<0.001)。发现的安全事件通常被报告为未引起注意(89%)或未解决(21%)。建议患者使用在线平台来了解他们认为错过或忽视的安全问题。在线利益相关者反馈类似于安全阀;独立且不受约束,它为报告在正规渠道中可能未被注意或未解决的安全问题提供了出路。
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