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Abstract：
OBJECTIVE: We designed a computer-based, integrated intelligent reminder system to reduce the deficiencies and errors in ultrasound (US) reports. In this study, we assessed the performance of this system and evaluated its impact on the quality of US reporting.
METHODS: Ultrasound reporting deficiencies or errors were divided into 2 categories: missing items (including outpatient or inpatient number and clinical diagnosis) and content errors (including measurement data, sex-related, and laterality errors). The intelligent reminder system was designed in Visual Basic for Applications (Microsoft Corporation, Redmond, WA) and integrated with the US system. It automatically detects reporting errors before printing of the report and provides real-time prompts for correction of the errors. We compared the US reporting deficiencies and errors during the 20 months before and after implementation of the system.
RESULTS: Before implementation of the system, deficiencies/errors were detected in 2.26% (8841 of 391,230) of US reports compared with 0.12% (530 of 444,215) of reports after implementation of the system (P < .0001). After adoption of the system, the reported item deficiencies were improved more than the content deficiencies, with the most notable improvement in clinical diagnosis. Sex-related errors were reduced from 7 cases to nil after use of the intelligent reminder system. No laterality errors were found before and after the implementation of the system.
CONCLUSIONS: The intelligent reminder system within the US system significantly reduced deficiencies and errors, improving the quality of the report.
METHODS: Ultrasound reporting deficiencies or errors were divided into 2 categories: missing items (including outpatient or inpatient number and clinical diagnosis) and content errors (including measurement data, sex-related, and laterality errors). The intelligent reminder system was designed in Visual Basic for Applications (Microsoft Corporation, Redmond, WA) and integrated with the US system. It automatically detects reporting errors before printing of the report and provides real-time prompts for correction of the errors. We compared the US reporting deficiencies and errors during the 20 months before and after implementation of the system.
RESULTS: Before implementation of the system, deficiencies/errors were detected in 2.26% (8841 of 391,230) of US reports compared with 0.12% (530 of 444,215) of reports after implementation of the system (P < .0001). After adoption of the system, the reported item deficiencies were improved more than the content deficiencies, with the most notable improvement in clinical diagnosis. Sex-related errors were reduced from 7 cases to nil after use of the intelligent reminder system. No laterality errors were found before and after the implementation of the system.
CONCLUSIONS: The intelligent reminder system within the US system significantly reduced deficiencies and errors, improving the quality of the report.
摘要：
目的：我们设计了一个基于计算机的，集成智能提醒系统，以减少超声（US）报告中的缺陷和错误。在这项研究中,我们评估了该系统的性能,并评估了其对美国报告质量的影响.
方法：超声报告缺陷或错误分为2类：缺失项目（包括门诊或住院人数和临床诊断）和内容错误（包括测量数据，性相关,和侧向错误)。智能提醒系统是在VisualBasicforApplications（MicrosoftCorporation，雷德蒙德,WA）并与美国系统集成。它在打印报告之前自动检测报告错误，并提供实时提示以更正错误。我们比较了该系统实施前后20个月的美国报告缺陷和错误。
结果：在实施系统之前，在实施该系统后，在2.26％（391,230份中的8841份）的美国报告中发现了缺陷/错误，而在0.12％（44,215份中的530份）的报告中发现了缺陷/错误（P<.0001）。系统采用后,报告的项目缺陷比内容缺陷得到了更多的改进，临床诊断的改善最为显著。使用智能提醒系统后，性别相关错误从7例减少到零。在系统实施前后均未发现侧向错误。
结论：美国系统内的智能提醒系统大大减少了缺陷和错误，提高报告质量。
方法：超声报告缺陷或错误分为2类：缺失项目（包括门诊或住院人数和临床诊断）和内容错误（包括测量数据，性相关,和侧向错误)。智能提醒系统是在VisualBasicforApplications（MicrosoftCorporation，雷德蒙德,WA）并与美国系统集成。它在打印报告之前自动检测报告错误，并提供实时提示以更正错误。我们比较了该系统实施前后20个月的美国报告缺陷和错误。
结果：在实施系统之前，在实施该系统后，在2.26％（391,230份中的8841份）的美国报告中发现了缺陷/错误，而在0.12％（44,215份中的530份）的报告中发现了缺陷/错误（P<.0001）。系统采用后,报告的项目缺陷比内容缺陷得到了更多的改进，临床诊断的改善最为显著。使用智能提醒系统后，性别相关错误从7例减少到零。在系统实施前后均未发现侧向错误。
结论：美国系统内的智能提醒系统大大减少了缺陷和错误，提高报告质量。
