Auditory Perception of Drug Names: Neighborhood Effects

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，筛选过程本身就容易出错。 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个经验验证，用户友好的软件工具，可用于筛选拟议的药物名称对现有的药物名称的数据库。 给定一个名字作为输入，软件将返回一个现有名字的列表，按易混淆性的降序排列。 混淆性评级将基于临床医生和非专业人员听觉感知错误研究得出的经验证的客观标准。 听觉感知实验将基于吕斯Luce的邻域激活模型（NAM）。 NAM预测听觉感知错误取决于目标词的可懂度以及目标词感知“邻域”中词的相似性和频率。这种预测体现在吕斯的频率加权邻域概率规则（FWNPR）中。 以NAM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会随着频率加权邻域概率的减小而增加。 2)频率加权邻域概率对听觉知觉识别的影响在成年外行人、医生、护士和药剂师中是相同的。 3)可以开发一种模型，该模型准确地预测药物名称在听觉感知识别任务中的混淆概率。 为了验证这些假设，我们提出了三个具体目标的研究：1）从药剂师，医生，护士，和成人外行人使用嘈杂的听觉知觉识别任务产生混淆数据; 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，可用于支持药品名称审批过程中的决策。

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.