Urbine Terry F, Schneider Philip J
Associate Research Scientist and Instructor, Department of Pharmacy Practice and Science, University of Arizona College of Pharmacy , Phoenix, Arizona .
Professor and Associate Dean, Department of Pharmacy Practice and Science, University of Arizona College of Pharmacy , Phoenix, Arizona .
Hosp Pharm. 2014 Sep;49(8):731-9. doi: 10.1310/hpj4908-731.
Preventing intravenous (IV) preparation errors will improve patient safety and reduce costs by an unknown amount.
To estimate the financial benefit of robotic preparation of sterile medication doses compared to traditional manual preparation techniques.
A probability pathway model based on published rates of errors in the preparation of sterile doses of medications was developed. Literature reports of adverse events were used to project the array of medical outcomes that might result from these errors. These parameters were used as inputs to a customized simulation model that generated a distribution of possible outcomes, their probability, and associated costs.
By varying the important parameters across ranges found in published studies, the simulation model produced a range of outcomes for all likely possibilities. Thus it provided a reliable projection of the errors avoided and the cost savings of an automated sterile preparation technology. The average of 1,000 simulations resulted in the prevention of 5,420 medication errors and associated savings of $288,350 per year. The simulation results can be narrowed to specific scenarios by fixing model parameters that are known and allowing the unknown parameters to range across values found in previously published studies.
The use of a robotic device can reduce health care costs by preventing errors that can cause adverse drug events.
预防静脉注射（IV）配制错误将提高患者安全性并降低成本，具体降低幅度未知。
评估与传统手工配制技术相比，使用机器人配制无菌药物剂量的经济效益。
基于已发表的无菌药物剂量配制错误率建立概率路径模型。利用不良事件的文献报告来预测这些错误可能导致的一系列医疗后果。这些参数被用作定制模拟模型的输入，该模型生成可能结果的分布、其概率以及相关成本。
通过在已发表研究中发现的范围内改变重要参数，模拟模型为所有可能情况生成了一系列结果。因此，它为避免的错误和自动化无菌配制技术的成本节约提供了可靠的预测。1000次模拟的平均值为每年预防5420次用药错误并节省288350美元。通过固定已知的模型参数并允许未知参数在先前发表的研究中发现的值范围内变化，模拟结果可以缩小到特定场景。
使用机器人设备可通过预防可能导致药物不良事件的错误来降低医疗成本。
