Auditory Perception of Drug Names: Neighborhood Effects

药物名称的听觉感知：邻里效应

• 批准号: 6617155
• 负责人: BRUCE L. LAMBERT
• 金额: $ 48.94万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-06-01 至 2007-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6617155
• 关键词: auditory discrimination; behavioral /social science research tag; clinical research; computer program /software; computer system design /evaluation; drug design /synthesis /production; human subject; model design /development; noise; nurses; pharmacists; physicians; psychological models; sound perception

DESCRIPTION (PROVIDED BY APPLICANT): One in six medication errors involves name confusion (e.g., Vioxx/Videx). Drug companies and regulators screen names prior to approval, but the screening process is itself error prone due to an overreliance on subjective assessments of similarity. Our long-term objective is to minimize the incidence of name confusion errors. Our short-term goal is to develop an empirically validated, user-friendly software tool that can be used to screen proposed drug names against databases of existing drug names. Given a name as input, the software will return a list of existing names ranked in descending order of confusability. Confusability ratings will be based on validated, objective criteria derived from studies of clinicians' and lay persons' auditory perceptual errors. Auditory perception experiments will be based on Luce's Neighborhood Activation Model (NAM). The NAM predicts that errors in auditory perception depend on the intelligibility of the target word as well as the similarity and frequency of words in the target word's perceptual "neighborhood." This prediction is embodied in Luce's Frequency-Weighted neighborhood Probability Rule (FWNPR). Using NAM as the theoretical framework, we will test three hypotheses: 1) The number of errors in auditory perceptual identification will increase as frequency-weighted neighborhood probability decreases. 2) The effects of frequency-weighted neighborhood probability on auditory perceptual identification will be the same among adult lay people, physicians, nurses, and pharmacists. 3) A model can be developed that accurately predicts a drug name's probability of confusion in an auditory perceptual identification task. To test these hypotheses, we propose studies with the three specific aims: 1) to generate confusion data from pharmacists, physicians, nurses, and adult lay people using a noisy auditory perceptual identification task; 2) to use the confusions-in conjunction with computational tools and the theoretical model-to develop and refine a model for predicting confusions; 3) to incorporate the best predictive models into a user-friendly software tool that can be used to support decision-making during the drug name approval process.

描述（由申请人提供）：六分之一的用药错误涉及名称混淆（例如，Vioxx/Videx）。 制药公司和监管机构在批准之前对名称进行筛选，但由于过度依赖对相似性的主观评估，筛选过程本身很容易出错。 我们的长期目标是尽量减少名称混淆错误的发生率。 我们的短期目标是开发一种经过经验验证、用户友好的软件工具，可用于根据现有药物名称数据库筛选提议的药物名称。 给定名称作为输入，软件将返回按易混淆性降序排列的现有名称列表。 混淆性评级将基于对临床医生和非专业人士听觉感知错误的研究得出的经过验证的客观标准。 听觉感知实验将基于卢斯的邻里激活模型（NAM）。 NAM 预测听觉感知中的错误取决于目标词的可理解性以及目标词感知“邻域”中词的相似性和频率。 这一预测体现在 Luce 的频率加权邻域概率规则 (FWNPR) 中。 使用NAM作为理论框架，我们将测试三个假设：1）听觉感知识别中的错误数量将随着频率加权邻域概率的降低而增加。 2) 频率加权邻域概率对听觉感知识别的影响在成年非专业人士、医生、护士和药剂师中是相同的。 3）可以开发一个模型，准确预测听觉感知识别任务中药物名称混淆的概率。 为了检验这些假设，我们提出了具有三个具体目标的研究：1）使用嘈杂的听觉感知识别任务生成来自药剂师、医生、护士和成年非专业人士的混淆数据； 2）使用混淆——结合计算工具和理论模型——开发和完善预测混淆的模型； 3）将最佳预测模型纳入用户友好的软件工具中，可用于支持药物名称审批过程中的决策。