Suppr超能文献

实施电子触发器以识别急诊科的诊断错误。

Implementation of Electronic Triggers to Identify Diagnostic Errors in Emergency Departments.

作者信息

Vaghani Viralkumar, Gupta Ashish, Mir Usman, Wei Li, Murphy Daniel R, Mushtaq Umair, Sittig Dean F, Zimolzak Andrew J, Singh Hardeep

机构信息

Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center and Baylor College of Medicine, Houston, Texas.

Department of Clinical and Health Informatics, McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston.

出版信息

JAMA Intern Med. 2025 Feb 1;185(2):143-151. doi: 10.1001/jamainternmed.2024.6214.

Abstract

IMPORTANCE

Missed diagnosis can lead to preventable patient harm.

OBJECTIVE

To develop and implement a portfolio of electronic triggers (e-triggers) and examine their performance for identifying missed opportunities in diagnosis (MODs) in emergency departments (EDs).

DESIGN, SETTING, AND PARTICIPANTS: In this retrospective medical record review study of ED visits at 1321 Veterans Affairs health care sites, rules-based e-triggers were developed and implemented using a national electronic health record repository. These e-triggers targeted 6 high-risk presentations for MODs in treat-and-release ED visits. A high-risk stroke e-trigger was applied to treat-and-release ED visits from January 1, 2016, to December 31, 2020. A symptom-disease dyad e-trigger was applied to visits from January 1, 2018, to December 31, 2019. High-risk abdominal pain, unexpected ED return, unexpected hospital return, and test result e-triggers were applied to visits from January 1, 2019, to December 31, 2019. At least 100 randomly selected flagged records were reviewed by physician reviewers for each e-trigger. Data were analyzed between January 2024 and April 2024.

EXPOSURES

Treat-and-release ED visits involving high-risk stroke, symptom-disease dyads, high-risk abdominal pain, unexpected ED return, unexpected hospital return, and abnormal test results not followed up after initial ED visit.

MAIN OUTCOMES AND MEASURES

Trained physician reviewers evaluated the presence/absence of MODs at ED visits and recorded data on patient and clinician characteristics, types of diagnostic process breakdowns, and potential harm from MODs.

RESULTS

The high-risk stroke e-trigger was applied to 8 792 672 treat-and-release ED visits (4 967 283 unique patients); the symptom-disease dyad e-trigger was applied to 3 692 454 visits (2 070 979 patients); and high-risk abdominal pain, unexpected ED return, unexpected hospital return, and test result e-triggers were applied to 1 845 905 visits (1 032 969 patients), overall identifying 203, 1981, 170, 116 785, 14 879, and 2090 trigger-positive records, respectively. Review of 625 randomly selected patient records (mean [SD] age, 62.5 [15.2] years; 553 [88.5%] male) showed the following MOD counts and positive predictive values (PPVs) within each category: 47 MODs (PPV, 47.0%) for stroke, 31 MODs (PPV, 25.8%) for abdominal pain, 11 MODs (PPV, 11.0%) for ED returns, 23 MODs (PPV, 23.0%) for hospital returns, 18 MODs (PPV, 18.0%) for symptom-disease dyads, and 55 MODs (PPV, 52.4%) for test results. Patients with MODs were slightly older than those without (mean [SD] age, 65.6 [14.5] vs 61.2 [15.3] years; P < .001). Reviewer agreement was favorable (range, 72%-100%). In 108 of 130 MODs (83.1%; excluding MODs related to the test result e-trigger), the most common diagnostic process breakdown involved the patient-clinician encounter. In 185 total MODs, 20 patients experienced severe harm (10.8%), and 54 patients experienced moderate harm (29.2%).

CONCLUSIONS AND RELEVANCE

In this retrospective medical record review study, rules-based e-triggers were useful for post hoc detection of MODs in ED visits. Interventions to target ED work system factors are urgently needed to support patient-clinician encounters and minimize harm from diagnostic errors.

摘要

重要性

漏诊可导致可预防的患者伤害。

目的

开发并实施一系列电子触发工具(电子触发器),并检验其在识别急诊科(ED)漏诊机会(MODs)方面的表现。

设计、背景和参与者:在这项对1321个退伍军人事务医疗保健机构的急诊就诊记录进行的回顾性研究中,基于规则的电子触发器利用国家电子健康记录库得以开发并实施。这些电子触发器针对治疗后即出院的急诊就诊中6种MODs的高风险表现。一种高风险中风电子触发器应用于2016年1月1日至2020年12月31日治疗后即出院的急诊就诊。一种症状-疾病二元组电子触发器应用于2018年1月1日至2019年12月31日的就诊。高风险腹痛、意外急诊复诊、意外住院复诊和检查结果电子触发器应用于2019年1月1日至2019年12月31日的就诊。每位医师审阅者至少随机抽取100条标记记录,对每个电子触发器进行审查。数据于2024年1月至2024年4月进行分析。

暴露因素

涉及高风险中风、症状-疾病二元组、高风险腹痛、意外急诊复诊、意外住院复诊以及急诊初次就诊后未跟进的异常检查结果的治疗后即出院的急诊就诊。

主要结局和测量指标

经过培训的医师审阅者评估急诊就诊时MODs的存在与否,并记录患者和临床医生特征、诊断过程故障类型以及MODs可能造成的伤害等数据。

结果

高风险中风电子触发器应用于8792672次治疗后即出院的急诊就诊(4967283名不同患者);症状-疾病二元组电子触发器应用于3692454次就诊(2070979名患者);高风险腹痛、意外急诊复诊、意外住院复诊和检查结果电子触发器应用于1845905次就诊(1032969名患者),分别共识别出203条、1981条、170条、116785条、14879条和2090条触发阳性记录。对625条随机抽取的患者记录(平均[标准差]年龄,62.5[15.2]岁;553名[88.5%]男性)的审查显示,每个类别中的MOD计数和阳性预测值(PPV)如下:中风47例MODs(PPV,47.0%),腹痛31例MODs(PPV,25.8%),急诊复诊11例MODs(PPV,11.0%),住院复诊23例MODs(PPV,23.0%),症状-疾病二元组18例MODs(PPV,18.0%),检查结果55例MODs(PPV,52.4%)。有MODs的患者比没有的患者年龄稍大(平均[标准差]年龄,65.6[14.5]岁对61.2[15.3]岁;P<0.001)。审阅者之间的一致性良好(范围,72%-100%)。在130例MODs中的108例(83.1%;不包括与检查结果电子触发器相关的MODs)中,最常见的诊断过程故障涉及患者-临床医生的接触。在总共185例MODs中,20名患者受到严重伤害(10.8%),54名患者受到中度伤害(29.2%)。

结论与意义

在这项回顾性病历审查研究中,基于规则的电子触发器有助于事后检测急诊就诊中的MODs。迫切需要针对急诊工作系统因素的干预措施,以支持患者-临床医生的接触,并将诊断错误造成的伤害降至最低。

相似文献

4
Diagnostic errors related to acute abdominal pain in the emergency department.急诊科与急性腹痛相关的诊断错误。
Emerg Med J. 2016 Apr;33(4):253-9. doi: 10.1136/emermed-2015-204754. Epub 2015 Nov 3.
5
Types and origins of diagnostic errors in primary care settings.初级保健环境中诊断错误的类型和来源。
JAMA Intern Med. 2013 Mar 25;173(6):418-25. doi: 10.1001/jamainternmed.2013.2777.

本文引用的文献

1
Machine Learning to Enhance Electronic Detection of Diagnostic Errors.机器学习助力增强诊断错误的电子检测
JAMA Netw Open. 2024 Sep 3;7(9):e2431982. doi: 10.1001/jamanetworkopen.2024.31982.
8
Use of e-triggers to identify diagnostic errors in the paediatric ED.利用电子触发器识别儿科急诊中的诊断错误。
BMJ Qual Saf. 2022 Oct;31(10):735-743. doi: 10.1136/bmjqs-2021-013683. Epub 2022 Mar 22.
9
Defining Fever.发热的定义。
Open Forum Infect Dis. 2021 Mar 31;8(6):ofab161. doi: 10.1093/ofid/ofab161. eCollection 2021 Jun.

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验