Lee JaeHo, Han Hyewon, Ock Minsu, Lee Sang-il, Lee SunGyo, Jo Min-Woo
Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea; Department of Biomedical Informatics, Asan Medical Center, Seoul, Republic of Korea; Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Department of Pharmacy, Asan Medical Center, Seoul, Republic of Korea.
Int J Med Inform. 2014 Dec;83(12):929-40. doi: 10.1016/j.ijmedinf.2014.08.006. Epub 2014 Aug 30.
To evaluate the impact of a high-alert medication clinical decision support system called HARMLESS on point-of-order entry errors in a tertiary hospital.
HARMLESS was designed to provide three kinds of interventions for five high-alert medications: clinical knowledge support, pop-ups for erroneous orders that block the order or provide a warning, and order recommendations. The impact of this program on prescription order was evaluated by comparing the orders in 6 month periods before and after implementing the program, by analyzing the intervention log data, and by checking for order pattern changes.
During the entire evaluation period, there were 357,417 orders and 5233 logs. After HARMLESS deployment, orders that omitted dilution fluids and exceeded the maximum dose dropped from 12,878 and 214 cases to 0 and 9 cases, respectively. The latter nine cases were unexpected, but after the responsible programming error was corrected, there were no further such cases. If all blocking interventions were seen as errors that were prevented, this meant that 4137 errors (3584 of which were 'dilution fluid omitted' errors) were prevented over the 6-month post-deployment period. There were some unexpected order pattern changes after deployment and several unexpected errors emerged, including intramuscular or intravenous push orders for potassium chloride (although a case review revealed that the drug was not actually administered via these methods) and an increase in pro re nata (PRN; administer when required) orders for most drugs.
HARMLESS effectively implemented blocking interventions but was associated with the emergence of unexpected errors. After a program is deployed, it must be monitored and subjected to data analysis to fix bugs and prevent the emergence of new error types.
评估一种名为HARMLESS的高警示药品临床决策支持系统对一家三级医院医嘱录入错误的影响。
HARMLESS旨在为五种高警示药品提供三种干预措施：临床知识支持、对错误医嘱弹出阻止医嘱或发出警告的提示框以及医嘱建议。通过比较该项目实施前后6个月期间的医嘱、分析干预日志数据以及检查医嘱模式变化，评估该项目对处方医嘱的影响。
在整个评估期间，共有357417条医嘱和5233条日志。HARMLESS部署后，遗漏稀释液和超最大剂量的医嘱分别从12878例和214例降至0例和9例。后9例情况出乎意料，但在纠正了相关程序错误后，未再出现此类情况。如果将所有阻止干预视为预防的错误，这意味着在部署后的6个月期间预防了4137例错误（其中3584例为“遗漏稀释液”错误）。部署后出现了一些意外的医嘱模式变化，还出现了一些意外错误，包括氯化钾的肌内或静脉推注医嘱（尽管病例审查显示该药物实际上并非通过这些方法给药）以及大多数药物的必要时（PRN）医嘱增加。
HARMLESS有效地实施了阻止干预措施，但与意外错误的出现有关。项目部署后，必须进行监测并进行数据分析，以修复漏洞并防止新的错误类型出现。
