Reducing Drug Name Confusion With Better Search Software

通过更好的搜索软件减少药物名称混淆

• 批准号: 7501496
• 负责人: King Lup Liu
• 金额: $ 38.48万
• 依托单位: PHARM I.R.
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-04-15 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7501496
• 关键词: Adverse effects; Algorithms; Classification; Community Pharmacy; Computer software; Computerized Medical Record; Confusion; Data; Databases; Detection; Development; Diagnosis; Drug Prescriptions; Effectiveness; Goals; Gold; Health; Health Professional; Hospitals; Indocid; Intervention; Keppra; Lopinavir/Ritonavir; Machine Learning; Measures; Medication Errors; Methods; Names; Numbers; Patients; Pattern; Performance; Pharmaceutical Preparations; Prevention; Probability; Process; Rate; Sensitivity and Specificity; Standards of Weights and Measures; Techniques; Testing; Time; Veterans; computerized; demographics; design; health administration; improved; patient safety; prescription document; prescription procedure; prevent; sound; tool

DESCRIPTION (provided by applicant): Confusions between drug names that look and sound alike (e.g., Keppra(r) and Kaletra(r), Indocid(r) and Endocet(r)) continue to occur frequently, and each confusion poses a threat to patient safety.2-5 Our long term objective is to design, build, test and continuously improve tools that minimize the harm caused by drug name confusion errors. For a patient to be harmed, an error must occur and it must go undetected until it reaches the patient. Harm is minimized either by preventing the error from occurring in the first place or by rapidly detecting the error so its adverse effects can be mitigated. Both prevention and mitigation efforts have been hindered by the lack of valid, reliable and efficient methods for assessing name confusion error rates. The gold standard for measuring medication error rates is direct observation of the prescribing-dispensing- administering process. This method is valid and reliable but is too time consuming and expensive to be widely used. As a result, many error reduction interventions have been designed, but few have been tested, and their effectiveness is, for the most part, unknown. Similarly, efforts to mitigate the effects of wrong drug errors are virtually non-existent because there has been no accurate and efficient way to detect such errors after they occur. The key to improving both prevention and mitigation of harm is the development of scalable, efficient, valid and reliable methods for detecting these drug name confusion errors. Our short-term goal is to develop and validate an algorithm for detecting drug name confusion errors by analyzing suspicious patterns in real-world prescription drug databases (in our case, integrated electronic medical records from the US Veterans Health Administration). We plan to test the following three hypotheses: 1. Computerized measures of drug name confusability can be used to identify wrong-drug errors in real-world prescription drug databases. 2. The number of errors detected will increase as the predicted probability of confusion increases. 3. The classification performance of the error detection algorithm (i.e., its accuracy, sensitivity and specificity) can be enhanced by applying machine learning techniques and by incorporating additional information from the electronic medical record (e.g., time between refills, diagnosis, lab values, demographics, etc.) To test these hypotheses, we propose studies with the following specific aims: 1. To design and implement an algorithm for the detection of suspicious patterns in prescription drug databases. 2. To test and validate this algorithm using real-world prescription data from the US Veterans Health Administration. 3. To use machine learning techniques to optimize and further validate the performance of the error detection algorithm, incorporating additional information from the electronic medical record. Health care professionals often confuse drug names that look and sound alike. Wrong drug errors occur in hospitals and in community pharmacies and can cause serious harm to patients. Our project seeks to improve patient safety by developing and testing new techniques for detecting wrong drug errors in integrated electronic medical records.

描述（由申请人提供）：外观和听起来相似的药物名称之间的混淆（例如 Keppra(r) 和 Kaletra(r)、Indocid(r) 和 Endocet(r)）持续频繁发生，并且每次混淆都构成威胁2-5 我们的长期目标是设计、构建、测试和不断改进工具，最大限度地减少药品名称混淆错误造成的危害。为了使患者受到伤害，错误必须发生，并且必须在错误到达患者之前未被发现。通过首先防止错误发生或快速检测错误以减轻其不利影响，可以最大限度地减少危害。由于缺乏有效、可靠和高效的方法来评估名称混淆错误率，预防和缓解工作都受到阻碍。衡量药物错误率的黄金标准是直接观察处方-配药-给药过程。该方法有效且可靠，但耗时且昂贵，难以广泛使用。因此，人们设计了许多减少错误的干预措施，但很少经过测试，而且其有效性在很大程度上是未知的。同样，减轻错误药物错误影响的努力实际上是不存在的，因为在此类错误发生后没有准确有效的方法来检测它们。改善预防和减轻危害的关键是开发可扩展、高效、有效和可靠的方法来检测这些药物名称混淆错误。我们的短期目标是开发和验证一种算法，通过分析现实世界处方药数据库（在我们的例子中是美国退伍军人健康管理局的综合电子医疗记录）中的可疑模式来检测药物名称混淆错误。我们计划测试以下三个假设： 1. 药物名称混淆性的计算机化测量可用于识别现实处方药数据库中的错误药物错误。 2. 检测到的错误数量将随着预测的混淆概率的增加而增加。 3. 通过应用机器学习技术并结合电子病历中的附加信息（例如，补充时间、诊断、实验室值、为了检验这些假设，我们提出了以下具体目标的研究： 1. 设计和实现一种算法，用于检测处方药数据库中的可疑模式。 2. 使用美国退伍军人健康管理局的真实处方数据测试和验证该算法。 3. 使用机器学习技术来优化并进一步验证错误检测算法的性能，并结合电子病历中的附加信息。医疗保健专业人员经常混淆看起来和听起来相似的药物名称。医院和社区药房发生错误用药错误，会给患者带来严重伤害。我们的项目旨在通过开发和测试用于检测集成电子病历中错误药物错误的新技术来提高患者安全。