Improving Outpatient Medication Lists Using Temporal Reasoning and Clinical Texts

使用时间推理和临床文本改进门诊药物清单

• 批准号: 7774682
• 负责人: Li Zhou
• 金额: $ 4.88万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7774682
• 关键词: Improving; Outpatient; Medication; Lists; Using

DESCRIPTION (provided by applicant): Accurate and complete medication lists are critical inputs to effective medication reconciliation to prevent medication prescribing and administration errors. Previous research aggregated structured medication data form multiple sources to generate and maintain a reconciled medication list. Medications documented in clinical texts also need to be reconciled. However, most reconciliation methods currently have limited capability to process textual data and temporal information (e.g., dates, duration and status). Our goal is to pilot and test methodologies and applications in the fields of natural language processing (NLP) and temporal reasoning to facilitate the use of electronic clinical texts in order to improve the "correctness" and "completeness" of medication lists. Clinic notes and free-text "comments" fields in medication lists in an ambulatory electronic medical record system will be considered in the study. An NLP system and a temporal reasoning system will be adapted to automatically extract medication and associated temporal information from clinical texts and encode the medications using a controlled terminology. Multiple knowledge bases will be used to develop a mechanism to represent the timing of medication use, detect the changes (e.g., active or inactive), and then to organize medications into appropriate groups (e.g., by ingredient or by status). The feasibility and efficiency of the proposed methods and tools in improving the process of medication reconciliation will be assessed. Domain experts will serve as judges to assess the success of capturing, coding, and organizing the medications and temporal information and also to evaluate whether our methods are complementary to those currently used for medication management.

描述（由申请人提供）：准确和完整的药物清单是有效药物核对的关键输入，以防止药物处方和管理错误。以前的研究汇总了来自多个来源的结构化药物数据，以生成和维护协调的药物列表。还需要核对临床文本中记录的药物。然而，大多数协调方法当前具有有限的能力来处理文本数据和时间信息（例如，日期、持续时间和状态）。我们的目标是试点和测试自然语言处理（NLP）和时间推理领域的方法和应用程序，以促进电子临床文本的使用，以提高药物清单的“正确性”和“完整性”。本研究将考虑门诊电子病历系统中药物列表中的临床记录和自由文本“评论”字段。NLP系统和时间推理系统将适于从临床文本中自动提取药物和相关的时间信息，并使用受控术语对药物进行编码。将使用多个知识库来开发一种机制，以表示药物使用的时间，检测变化（例如，活性的或非活性的），然后将药物组织成适当的组（例如，按成分或按状态）。所提出的方法和工具在改善用药过程中的可行性和效率 将对和解进行评估。领域专家将担任法官，以评估捕获，编码和组织药物和时间信息的成功，并评估我们的方法是否与目前用于药物管理的方法互补。