A New Verification Process to Reduce Dispensing Error Rate in Community Pharmacie

降低社区药房配药错误率的新验证流程

• 批准号: 7801531
• 负责人: Jenna Lee
• 金额: $ 10.73万
• 依托单位: A&E INNOVATIONS
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-05-10 至 2010-11-09
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7801531
• 关键词: Algorithms; Cessation of life; Clinical; Code; Color; Communities; Community Pharmacy; Engravings; Goals; Hospitals; Injury; Lead; Link; Manuals; Measurement; Medical Errors; Medication Errors; Oral; Outpatients; Patients; Pharmaceutical Preparations; Pharmacists; Pharmacy facility; Printing; Process; Research; Shapes; Solid; Structure; Technology; Time; Variant; Work; base; cost; design; dosage; health care delivery; health care quality; image processing; imprint; improved; pill; public health relevance

DESCRIPTION (provided by applicant): The ultimate goal of the proposed research is to develop a closed-loop process to significantly reduce the dispensing error rate for community pharmacies. In the proposed dispensing process, almost all initial medication filling errors will be caught by an independent cross-checking based verification process. In addition, the verification process will be automated so that the tedious and error prone manual check by pharmacists will be eliminated from current clinical pharmacy practice. Each year, medical errors cause more than one million injuries and 44,000 deaths in USA alone. Among them, medication errors are responsible for over 7,000 deaths. Medication dispensing error is a major component of overall medication errors. It is responsible for up to 24% of medication errors in community pharmacies and up to 12.5% of medication errors in hospital outpatient pharmacies. Current medication dispensing process design is based on an error prone open loop structure, where double-check based final checking/verifications are manually operated and lead to the weakest link in the process. An automated closed-loop verification process must be developed to close this gap. Since the part pack dispensing is the dominant dispensing form in community pharmacies, the automated pills identification capability is the key enabling technology for the closed loop dispensing process. In principle, it is feasible to identify any pill based on its imprinted codes, size, shape and color. But it has not been proven that such identification can be reliably operated in automated fashion, which is a crucial requirement for closed- loop dispensing verification process for community pharmacies. In the proposed research, we will demonstrate the feasibility of automated solid dosage medication identification by aiming to: (1) Demonstrate the capability of identifying pills when the size, shape and color measurement including errors due to unavoidable variations in clinical pharmacy practices; and (2) Develop image processing algorithm for engraved imprinted code recognition, which poses a great challenge for imprinted code recognitions. PUBLIC HEALTH RELEVANCE: Each year, medication errors cause more than 7000 deaths in USA alone. This research will result in a much lower dispensing error rate in community pharmacies. The new dispensing verification process will: (1) dramatically reduce the "missed-catch" rate in final dispensing verification process; (2) improve health care quality by allowing pharmacists to spend more time on patients rather than on tedious and error prone manual medication checking; (3) reduce the healthcare delivery cost by automating the verification portion of the dispensing process.

描述（由申请人提供）：拟议研究的最终目标是开发一个闭环流程，以显着降低社区药房的调剂错误率。在拟议的配药过程中，几乎所有初始药物填充错误都将通过独立的基于交叉检查的验证过程捕获。此外，验证过程将实现自动化，以便药剂师从目前的临床药学实践中消除繁琐和容易出错的人工检查。每年，仅在美国，医疗事故就造成100多万人受伤，44,000人死亡。其中，用药错误导致7000多人死亡。配药错误是整体用药错误的主要组成部分。社区药房高达24%的用药错误和医院门诊药房高达12.5%的用药错误都是由它造成的。目前的药品调剂流程设计是基于一个容易出错的开环结构，其中基于双重检查的最终检查/验证是人工操作的，这导致了流程中最薄弱的环节。必须开发一个自动化的闭环验证过程来缩小这一差距。由于片式调剂是社区药房的主要调剂形式，因此药丸自动识别能力是闭环调剂过程的关键使能技术。原则上，根据药丸的印记代码、大小、形状和颜色来识别药丸是可行的。但目前还没有证明这种识别可以可靠地自动化操作，这是社区药房闭环配药验证过程的关键要求。在拟议的研究中，我们将通过以下目标来证明自动化固体剂量药物识别的可行性：(1)证明在临床药学实践中，当药片的大小、形状和颜色测量包括不可避免的变化所导致的误差时，能够识别药丸；(2)开发了用于刻印码识别的图像处理算法，这对刻印码识别提出了很大的挑战。