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首页> 美国卫生研究院文献>Clinical Medicine Research >C-C4-02: Using a Natural Language Processor to Remove All Elements of Personal Health Information (PHI) to Deidentify Clinical Annotations for the Specimen Retrieval System (SRS)
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C-C4-02: Using a Natural Language Processor to Remove All Elements of Personal Health Information (PHI) to Deidentify Clinical Annotations for the Specimen Retrieval System (SRS)

机译：C-C4-02：使用自然语言处理器删除个人健康信息（PHI）的所有元素以消除对标本检索系统（SRS）的临床注释

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PHI in HMORN medical records must be systematically removed prior to use and sharing data with co-investigators at other institutions. The problem is particularly acute when one needs to link clinical and follow-up data with pathologic specimens from cancer patients in studies of prognostic and predictive tumor markers. Natural language processing (NLP) can be customized to identify and remove PHI in selected clinical records by substituting nonsense characters. Such a process of deidentification can proceed largely without human intervention and, if successful, can allow efficient linkage of clinical notes with similarly deidentified specimens for laboratory investigators at other institutions. The Shared Pathology Informatics Network (SPIN) was funded by the Cancer Diagnosis Program of the National Cancer Institute to develop a computerized program that would search pathology department text files and reports from several institutions and retrieve, in a database, all records that met search criteria. SPIN was designed to access information from each institution’s electronic records without affecting the records and collating data from several sites into a single report. Our current project, the Specimen Retrieval System, requires the identification of large numbers of cancer specimens of specific types from Kaiser Permanente Northwest (KPNW) and, subsequently, other Cancer Research Network sites, retrieval of those specimens and linkage with clinical annotations that describe staging, treatment and outcome. We have used the existing databases of the KPNW Tumor Registry and Department of Pathology to identify cases. The EPIC electronic medical record and several other computerized text files provided the clinical notes for these patients. Using SPIN technology we then processed text files to ‘scrub’ them of PHI. All records were then manually inspected to assess the completeness of the process, which elements of PHI persisted and which had been successfully ‘scrubbed’ of PHI. We processed several hundred files from more than 100 patients and will report detailed statistical analysis. This technology has demonstrated significant capability to facilitate searches for pathologic specimens and clinical annotations using conventional reports from pathology departments. It is a valuable tool to remove PHI, deidentify medical records and ease sharing of clinical information with investigators at other sites.
机译：在使用HMORN医疗记录中的PHI之前，必须先将其删除，并与其他机构的共同调查人员共享数据。当需要在预后和预测性肿瘤标志物研究中将临床和随访数据与癌症患者的病理样本联系起来时，这一问题尤为严重。可以定制自然语言处理（NLP），通过替换无意义的字符来识别和删除所选临床记录中的PHI。取消身份验证的过程可以在很大程度上无需人工干预即可进行，如果成功，则可以使临床笔记与相似身份确定的标本有效链接，以供其他机构的实验室研究人员使用。共享病理信息网络（SPIN）由美国国家癌症研究所的癌症诊断计划资助，开发了一个计算机化程序，该程序可以搜索病理部门的文本文件和来自多个机构的报告，并在数据库中检索所有符合搜索条件的记录。 SPIN旨在访问每个机构的电子记录中的信息，而不会影响记录，并将多个站点的数据整理到一个报告中。我们当前的项目“标本检索系统”要求从Kaiser Permanente Northwest（KPNW）以及随后的其他癌症研究网络站点中识别大量特定类型的癌症标本，检索这些标本并与描述分期的临床注释联系起来，治疗和结果。我们使用了KPNW肿瘤注册处和病理学系的现有数据库来识别病例。 EPIC电子病历和其他一些计算机文本文件为这些患者提供了临床笔记。然后，我们使用SPIN技术处理文本文件以“清理” PHI。然后，对所有记录进行人工检查，以评估流程的完整性，PHI的哪些元素持续存在以及哪些已成功“清理”了PHI。我们处理了来自100多个患者的数百个文件，并将报告详细的统计分析。该技术已显示出使用病理部门的常规报告促进搜索病理标本和临床注释的强大能力。它是删除PHI，取消身份证明医疗记录并简化与其他站点的研究人员共享临床信息的宝贵工具。

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