Investigation of misfiled cases in the PACS environment and a solution to prevent filing errors for chest radiographs
Investigation of misfiled cases in the PACS environment and a solution to prevent filing errors for chest radiographs
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DOI：
10.1016/j.acra.2004.11.008
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发表时间：
2005-01-01
影响因子：
4.8
通讯作者：
Doi, K
中科院分区：
文献类型：
--
作者：
Morishita, J;Watanabe, H;Doi, K
理由和目标。本研究的目的是调查在两家医院的图片归档和通信系统环境中的误归档病例，并证明基于模板匹配技术的后前位胸片自动患者识别方法的潜在实用性，该技术旨在防止归档错误。我们调查了一家医院25个月内从不同方式获得的误归档病例，以及另一家医院17个月内的误归档胸片病例。为了调查胸部X光片的自动患者识别和识别方法的有用性，在后者医院的临床环境中完成了一项前瞻性研究。在一家医院的不同模式和在另一家医院的胸部X光片的误归档病例总数分别为327和22。这两家医院的误归档病例主要是人为错误造成的（例如，不正确地手动输入患者信息，不正确地使用识别卡，其中前一名患者的识别卡用于下一名患者的图像采集）。前瞻性研究表明，计算机化方法的有用性，发现误报的情况下，高性能（即，86.4%的正确警告率为不同的病人和1.5%的错误警告率为相同的病人）。我们证实了两家医院存在错报病例的情况。用于胸部X射线照片的自动患者识别和标识方法将有助于防止错误图像被存储在图片存档和通信系统环境中。
Rationale and Objective. The aim of the study was to survey misfiled cases in a picture archiving and communication system environment at two hospitals and to demonstrate the potential usefulness of an automated patient recognition method for posteroanterior chest radiographs based on a template-matching technique designed to prevent filing errors.Materials and Methods. We surveyed misfiled cases obtained from different modalities in one hospital for 25 months, and misfiled cases of chest radiographs in another hospital for 17 months. For investigating the usefulness of an automated patient recognition and identification method for chest radiographs, a prospective study has been completed in clinical settings at the latter hospital.Results. The total numbers of misfiled cases for different modalities in one hospital and for chest radiographs in another hospital were 327 and 22, respectively. The misfiled cases in the two hospitals were mainly the result of human errors (eg, incorrect manual entries of patient information, incorrect usage of identification cards in which an identification card for the previous patient was used for the next patient's image acquisition). The prospective study indicated the usefulness of the computerized method for discovering misfiled cases with a high performance (ie, an 86.4% correct warning rate for different patients and 1.5% incorrect warning rate for the same patients).Conclusion. We confirmed the occurrence of misfiled cases in the two hospitals. The automated patient recognition and identification method for chest radiographs would be useful in preventing wrong images from being stored in the picture archiving and communication system environment.