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）：六分之一的药物错误涉及名称混乱（例如Vioxx/videx）。 制药公司和监管机构在批准之前屏幕名称，但是由于对相似性的主观评估过分评估，筛查过程本身很容易出错。 我们的长期目标是最大程度地减少名称混乱错误的发生率。 我们的短期目标是开发经验验证，用户友好的软件工具，可用于针对现有药物名称的数据库筛选建议的药物名称。 给定名称为输入，该软件将返回以混淆性降序排名的现有名称的列表。 混淆性评级将基于经过验证的客观标准，这些标准来自对临床医生和外行人的听觉知觉错误的研究。 听觉感知实验将基于Luce的邻居激活模型（NAM）。 NAM预测听觉感知中的错误取决于目标单词的可理解性以及目标单词感知“邻居”中单词的相似性和频率。 该预测体现在Luce的频率加权社区概率规则（FWNPR）中。 使用NAM作为理论框架，我们将检验三个假设：1）听觉感知识别中的错误数量将随着频率加权的邻居概率的降低而增加。 2）在成年外行人，医生，护士和药剂师中，频率加权社区概率对听觉感知识别的影响将相同。 3）可以开发一个模型，该模型可以准确预测吸毒名称在听觉感知识别任务中混淆的可能性。 为了检验这些假设，我们提出了三个具体目的的研究：1）使用嘈杂的听觉知觉识别任务来产生药剂师，医生，护士和成年外行人的混淆数据； 2）使用与计算工具的混乱结合以及理论模型来开发和完善预测混乱的模型； 3）将最佳预测模型纳入用户友好的软件工具中，该工具可用于在药物名称批准过程中支持决策。