Division of General Medicine and Primary Care, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA.
J Am Med Inform Assoc. 2011 Jul-Aug;18(4):479-84. doi: 10.1136/amiajnl-2010-000039. Epub 2011 May 12.
Clinical decision support systems can prevent knowledge-based prescription errors and improve patient outcomes. The clinical effectiveness of these systems, however, is substantially limited by poor user acceptance of presented warnings. To enhance alert acceptance it may be useful to quantify the impact of potential modulators of acceptance.
We built a logistic regression model to predict alert acceptance of drug-drug interaction (DDI) alerts in three different settings. Ten variables from the clinical and human factors literature were evaluated as potential modulators of provider alert acceptance. ORs were calculated for the impact of knowledge quality, alert display, textual information, prioritization, setting, patient age, dose-dependent toxicity, alert frequency, alert level, and required acknowledgment on acceptance of the DDI alert.
50,788 DDI alerts were analyzed. Providers accepted only 1.4% of non-interruptive alerts. For interruptive alerts, user acceptance positively correlated with frequency of the alert (OR 1.30, 95% CI 1.23 to 1.38), quality of display (4.75, 3.87 to 5.84), and alert level (1.74, 1.63 to 1.86). Alert acceptance was higher in inpatients (2.63, 2.32 to 2.97) and for drugs with dose-dependent toxicity (1.13, 1.07 to 1.21). The textual information influenced the mode of reaction and providers were more likely to modify the prescription if the message contained detailed advice on how to manage the DDI.
We evaluated potential modulators of alert acceptance by assessing content and human factors issues, and quantified the impact of a number of specific factors which influence alert acceptance. This information may help improve clinical decision support systems design.
临床决策支持系统可以预防基于知识的处方错误，改善患者结局。然而，这些系统的临床效果受到用户对提示接受程度低的极大限制。为了提高警示接受度，量化潜在调节因素可能会有所帮助。
我们构建了一个逻辑回归模型，以预测三种不同环境下药物-药物相互作用（DDI）警示的接受度。从临床和人为因素文献中评估了 10 个变量，作为对提供者警示接受度的潜在调节因素。计算了知识质量、警示显示、文本信息、优先级、环境、患者年龄、剂量依赖性毒性、警示频率、警示级别和确认要求对 DDI 警示接受度的影响的 OR。
分析了 50788 个 DDI 警示。提供者仅接受了 1.4%的非中断性警示。对于中断性警示，用户接受度与警示频率呈正相关（OR1.30，95%CI1.23 至 1.38）、显示质量（4.75，3.87 至 5.84）和警示级别（1.74，1.63 至 1.86）。在住院患者（2.63，2.32 至 2.97）和有剂量依赖性毒性的药物（1.13，1.07 至 1.21）中，警示接受度更高。警示信息影响反应模式，如果消息包含有关如何处理 DDI 的详细建议，提供者更有可能修改处方。
我们通过评估内容和人为因素问题来评估警示接受度的潜在调节因素，并量化了影响警示接受度的一些特定因素的影响。这些信息可能有助于改善临床决策支持系统的设计。
