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）：看起来和声音的毒品名称之间的混淆（例如，Keppra（R）和Kaletra（R），Indocid（R）和Endocet（R））继续发生经常发生，并且每个混乱都对患者的安全构成威胁。2-5我们的长期目标是设计，构建，测试，测试，不断地改善该工具的损害，并限制了药物的损害。为了使患者受到伤害，必须发生错误，并且必须没有发现直到到达患者之前。通过防止误差首先发生或快速检测到误差，可以减轻损害，从而减轻损害。由于缺乏有效，可靠和有效的方法来评估名称混乱错误率，预防和缓解工作都受到了阻碍。测量药物错误率的黄金标准是对处方分解过程的直接观察。此方法是有效且可靠的，但耗时过于耗时且昂贵，无法广泛使用。结果，已经设计了许多误差干预措施，但很少有人测试，并且它们的有效性在大多数情况下是未知的。同样，减轻错误药物错误的效果的努力实际上是不存在的，因为没有准确，有效地检测这些错误发生后。改善预防和缓解伤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乱错误。我们的短期目标是通过分析现实世界处方药数据库中的可疑模式来开发和验证一种用于检测药物名称混乱错误的算法（在我们的情况下，是美国退伍军人卫生管理局的集成电子病历）。我们计划测试以下三个假设：1。药物名称混淆性的计算机测量方法可用于确定现实处方药数据库中错误的药物错误。 2。检测到的错误数量将随着混乱的预测概率增加而增加。 3。可以通过应用机器学习技术并合并电子病历中的其他信息来增强错误检测算法的分类性能（即，其准确性，敏感性和特异性）可以增强。处方药数据库中的模式。 2。使用美国退伍军人卫生管理局的实际处方数据测试和验证该算法。 3。使用机器学习技术来优化和进一步验证错误检测算法的性能，并包含电子病历中的其他信息。医疗保健专业人员通常会混淆外观和听起来一样的毒品名称。错误的药物错误发生在医院和社区药房中，并可能对患者造成严重伤害。我们的项目旨在通过开发和测试新技术来提高患者的安全，以检测到集成的电子病历中的错误药物错误。