Shah Nidhi R, Seger Andrew C, Seger Diane L, Fiskio Julie M, Kuperman Gilad J, Blumenfeld Barry, Recklet Elaine G, Bates David W, Gandhi Tejal K
Division of General Medicine, Brigham and Women's Hospital, 1620 Tremont Street, 3rd Floor, Boston, MA 02120, USA.
J Am Med Inform Assoc. 2006 Jan-Feb;13(1):5-11. doi: 10.1197/jamia.M1868. Epub 2005 Oct 12.
Computerized drug prescribing alerts can improve patient safety, but are often overridden because of poor specificity and alert overload. Our objective was to improve clinician acceptance of drug alerts by designing a selective set of drug alerts for the ambulatory care setting and minimizing workflow disruptions by designating only critical to high-severity alerts to be interruptive to clinician workflow. The alerts were presented to clinicians using computerized prescribing within an electronic medical record in 31 Boston-area practices. There were 18,115 drug alerts generated during our six-month study period. Of these, 12,933 (71%) were noninterruptive and 5,182 (29%) interruptive. Of the 5,182 interruptive alerts, 67% were accepted. Reasons for overrides varied for each drug alert category and provided potentially useful information for future alert improvement. These data suggest that it is possible to design computerized prescribing decision support with high rates of alert recommendation acceptance by clinicians.
计算机化药物处方警报可以提高患者安全性，但由于特异性差和警报过载，这些警报常常被忽略。我们的目标是通过为门诊护理环境设计一套选择性的药物警报，并仅将关键到高严重性的警报指定为会干扰临床医生工作流程的警报，从而尽量减少工作流程中断，以此提高临床医生对药物警报的接受度。在波士顿地区的31家医疗机构中，通过电子病历中的计算机化处方将警报呈现给临床医生。在我们为期六个月的研究期间共产生了18115次药物警报。其中，12933次（71%）为非干扰性警报，5182次（29%）为干扰性警报。在5182次干扰性警报中，67%被接受。每种药物警报类别的忽略原因各不相同，为未来警报改进提供了潜在有用信息。这些数据表明，有可能设计出临床医生接受率高的计算机化处方决策支持系统。
