Department of Surgery, University of Adelaide, The Queen Elizabeth Hospital, Woodville, South Australia, Australia.
Department of Neurosurgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia.
Br J Surg. 2024 Oct 1;111(10). doi: 10.1093/bjs/znae253.
Up to half of all surgical adverse events are due to non-technical errors, making non-technical skill assessment and improvement a priority. No specific tools are available to retrospectively identify non-technical errors that have occurred in surgical patient care. This original study aimed to develop and provide evidence of validity and inter-rater reliability for the System for Identification and Categorization of Non-technical Error in Surgical Settings (SICNESS).
A literature review, modified Delphi process, and two pilot phases were used to develop and test the SICNESS tool. For each pilot, 12 months of surgical mortality data from the Australian and New Zealand Audit of Surgical Mortality were assessed by two independent reviewers using the SICNESS tool. Main outcomes included tool validation through modified Delphi consensus, and inter-rater reliability for: non-technical error identification and non-technical error categorization using Cohen's κ coefficient, and overall agreement using Fleiss' κ coefficient.
Version 1 of the SICNESS was used for pilot 1, including 412 mortality cases, and identified and categorized non-technical errors with strong-moderate inter-rater reliability. Non-technical error exemplars were created and validated through Delphi consensus, and a novel mental model was developed. Pilot 2 included an additional 432 mortality cases. Inter-rater reliability was near perfect for leadership (κ 0.92, 95% c.i. 0.82 to 1.00); strong for non-technical error identification (κ 0.89, 0.84 to 0.93), communication and teamwork (κ 0.89, 0.79 to 0.99), and decision-making (κ 0.85, 0.79 to 0.92); and moderate for situational awareness (κ 0.79, 0.71 to 0.87) and overall agreement (κ 0.69, 0.66 to 0.73).
The SICNESS is a reliable and valid tool, enabling retrospective identification and categorization of non-technical errors associated with death, occurring in real surgical patient interactions.
多达一半的手术不良事件是由于非技术错误造成的，因此评估和提高非技术技能是当务之急。目前尚无特定工具可用于回顾性识别外科患者护理中发生的非技术错误。本原始研究旨在开发一种工具，并提供系统识别和分类手术环境中非技术错误（SICNESS）的有效性和组内一致性的证据。
通过文献回顾、改良 Delphi 流程和两个试点阶段开发和测试 SICNESS 工具。对于每个试点，两名独立审查员使用 SICNESS 工具评估来自澳大利亚和新西兰手术死亡率审计的 12 个月手术死亡率数据。主要结果包括通过改良 Delphi 共识进行工具验证，以及使用 Cohen's κ 系数进行非技术错误识别和非技术错误分类的组内一致性，以及使用 Fleiss' κ 系数进行总体一致性。
使用 SICNESS 的版本 1 进行试点 1，包括 412 例死亡率病例，并具有较强的中度组内一致性来识别和分类非技术错误。通过 Delphi 共识创建和验证了非技术错误范例，并开发了新的心理模型。试点 2 包括另外 432 例死亡率病例。领导力的组内一致性接近完美（κ 0.92，95%置信区间为 0.82 至 1.00）；非技术错误识别（κ 0.89，0.84 至 0.93）、沟通和团队合作（κ 0.89，0.79 至 0.99）和决策（κ 0.85，0.79 至 0.92）具有很强的一致性；情境意识（κ 0.79，0.71 至 0.87）和总体一致性（κ 0.69，0.66 至 0.73）具有中度一致性。
SICNESS 是一种可靠且有效的工具，可用于回顾性识别与死亡相关的真实外科患者交互中非技术错误，并对其进行分类。
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