COMPUTER ASSISTED PATIENT INTERVIEWING IN CLINICAL PHARMACY

随着临床药剂师通过以下方式更多地参与直接患者护理 发布用药信息并帮助识别潜在药物 相互作用导致的健康危害，他们必须跟上新药 信息，为患者分配更多的一对一时间，并保持 有效的面试技巧。为了支持这些活动，中超开始 1990年与美国国立卫生研究院临床中心药剂部合作。 目标是开发一种计算机面试系统，收集 用药记录，向患者分发用药信息，以及 检测与药物治疗方案相关的可能的不良事件，从而 为非候选人的患者提供更多的药剂师时间 进行电脑面试。此系统生成的警告可能有助于 关注药剂师与患者之间的互动。 最初版本的用药历史系统开发、测试和 在91财年报告，包括针对患者的系统面谈脚本 人口统计学、健康史和药物使用情况。一份简明的总结报告是 采访结束后制作的。 92财年采访脚本和药物不良反应(ADR)词库 和一份全面的报告，按身体分类描述ADR 准备了系统并提交给USP，以帮助实现未来的标准化 ADR术语。数据库模块被设计为支持不良药物 创建了反应和药物-药物相互作用检测和程序 患者用药信息和医生用药监测信息 取回。此外，还建立了药物不良反应的神经网络模型 开发的检测显示出比以前的 多层次的系统评价方案。 完成和整合所有程序模块和数据库组件， 并开始对完成的系统进行正式测试和评估 计划在93财年举行。

As clinical pharmacists become more involved in direct patient care by dispensing medication information and helping to identify potential drug interaction-induced health hazards, they must keep abreast of new drug information, allocate more one-on-one time for patients, and maintain effective interviewing skills. In support of these activities, CSL began a collaboration in 1990 with the NIH Clinical Center Pharmacy Department. The objective was to develop a computer interviewing system that collects medication histories, dispenses medication information to patients, and detects possible untoward events related to medication regimens, thereby making more pharmacist time available for patients who are not candidates for computer interviewing. Warnings generated by this system could aid in focusing the pharmacist-patient interaction. The initial version of the medication history system developed, tested, and reported in FY91, included system interview scripts for patient demographics, health history, and drug usage. A concise summary report is produced after the interview. In FY92 the interview scripts and adverse drug reaction (ADR) thesaurus were refined and a comprehensive report describing ADR's sorted by body systems was prepared and submitted to USP to assist in standardizing future ADR terminology. Database modules were designed to support adverse drug reaction and drug-drug interaction detection and programs were created for patient drug information and physician drug monitoring information retrieval. Also, a neural network module for adverse drug reaction detection was developed that demonstrated improvement over a previous multi-level system evaluation scheme. Completion and integration of all program modules and database components, and initiation of formal testing and evaluation of the completed system are planned for FY93.