Automated Detection of Medical Errors

自动检测医疗错误

• 批准号: 7124706
• 负责人: PETER D STETSON
• 金额: $ 13.5万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-30 至 2008-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7124706
• 关键词: automated medical record system; behavioral /social science research tag; biomedical automation; clinical research; health care quality; health services research tag; human data; information retrieval; medical records; method development; patient care management; patient safety /medical error

DESCRIPTION: The long-term goal of this proposal is to use the electronic medical record, including narrative text, to understand and encode the process of care for individual patients in order to improve patient safety. Achieving this goal has the potential to help detect adverse events, and to differentiate medical errors from appropriately tailored care. The specific aims for this proposal are as follows: 1) To understand and encode the process of care for individual patients using data in the electronic medical record, including narrative text. 2) To use a more detailed understanding of patients' processes of care to improve automated adverse event detection. 3) To match processes of care for individual patients against accepted care pathways in order to identify discrepancies. We will capitalize on three core technologies that are in active use by clinicians and researchers in our busy clinical setting: 1) a Web-based clinical information system and its associated clinical data repository (WebCIS), 2) a full medical language parser (MedLEE), and 3) a semi-structured, electronic physician documentation system built by the applicant specifically to support this project (eNote). Methods will include evaluating the performance (sensitivity, specificity and positive predictive value) of our system, DETER+MINE (DETecting ERrors Mining Narrative Electronically), to model the care process and detect adverse events and pathway deviations. We will utilize explicit process criteria and manual, retrospective chart review as a gold standard. This research is intended to provide proof of concept that combining natural language processing of clinical narrative with traditional sources of coded data is required for effective screening with automated defection systems. This approach has the potential to impact significantly on our ability to detect and investigate medical errors, adverse medical events, and pathway deviations by reducing reliance on costly and slow manual chart reviews.

描述： 该提案的长期目标是使用电子病历（包括叙述文本）来理解和编码个体患者的护理过程，以提高患者的安全。 实现这一目标有可能帮助检测不良事件，并将医疗错误与适当定制的护理区分开来。该提案的具体目标如下： 1）使用电子病历中的数据（包括叙述文本）来理解和编码个体患者的护理过程。 2) 更详细地了解患者的护理过程，以改进自动不良事件检测。 3) 将个体患者的护理流程与公认的护理途径进行匹配，以发现差异。我们将利用临床医生和研究人员在繁忙的临床环境中积极使用的三项核心技术：1) 基于 Web 的临床信息系统及其相关的临床数据存储库 (WebCIS)，2) 完整的医学语言解析器 (MedLEE) ），以及 3）申请人专门为支持该项目而构建的半结构化电子医生文档系统（eNote）。 方法将包括评估我们的系统 DETER+MINE（以电子方式检测错误挖掘叙事）的性能（敏感性、特异性和阳性预测值），以对护理过程进行建模并检测不良事件和路径偏差。我们将利用明确的流程标准和手动、回顾性图表审查作为黄金标准。 这项研究旨在提供概念证明，即通过自动缺陷系统进行有效筛查需要将临床叙述的自然语言处理与传统编码数据源相结合。这种方法有可能通过减少对昂贵且缓慢的手动图表审查的依赖，对我们检测和调查医疗错误、不良医疗事件和路径偏差的能力产生重大影响。