Division of Allergy, Immunology, and Rheumatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, Calif.
Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, Calif.
J Allergy Clin Immunol Pract. 2023 Apr;11(4):1243-1252.e6. doi: 10.1016/j.jaip.2023.01.026. Epub 2023 Feb 1.
Frontline providers frequently make time-sensitive antibiotic choices, but many feel poorly equipped to handle antibiotic allergies.
We hypothesized that a digital decision support tool could improve antibiotic selection and confidence when managing β-lactam allergies.
A digital decision support tool was designed to guide non-allergist providers in managing patients with β-lactam allergy labels. Non-allergists were asked to make decisions in clinical test cases without the tool, and then with it. These decisions were compared using paired t tests. Users also completed surveys assessing their confidence in managing antibiotic allergies.
The tool's algorithm was validated by confirming its recommendations aligned with that of five allergists. Non-allergist providers (n = 102) made antibiotic management decisions in test cases, both with and without the tool. Use of the tool increased the proportion of correct decisions from 0.41 to 0.67, a difference of 0.26 (95% CI, 0.22-0.30; P < .001). Users were more likely to give full-dose antibiotics in low-risk situations, give challenge doses in medium-risk situations, and avoid the antibiotic and/or consult allergy departments in high-risk situations. A total of 98 users (96%) said the tool would increase their confidence when choosing antibiotics for patients with allergies.
A point-of-care clinical decision tool provides allergist-designed guidance for non-allergists and is a scalable system for addressing antibiotic allergies, irrespective of allergist availability. This tool encouraged appropriate antibiotic use in low- and medium-risk situations and increased caution in high-risk situations. A digital support tool should be considered in quality improvement and antibiotic stewardship efforts.
一线医务人员经常需要做出具有时间紧迫性的抗生素选择,但许多人感到自己在处理抗生素过敏方面的能力不足。
我们假设一个数字化决策支持工具可以改善处理β-内酰胺类抗生素过敏时的抗生素选择和信心。
设计了一个数字化决策支持工具来指导非过敏专家在管理具有β-内酰胺类抗生素过敏标签的患者时做出决策。非过敏专家在没有工具和使用工具的情况下分别对临床案例做出决策,然后对这些决策进行配对 t 检验比较。用户还完成了评估他们管理抗生素过敏信心的调查。
通过确认其推荐与五名过敏专家的建议一致,验证了该工具的算法。非过敏专家(n=102)在测试案例中使用和不使用工具做出了抗生素管理决策。使用该工具将正确决策的比例从 0.41 提高到 0.67,差异为 0.26(95%CI,0.22-0.30;P<0.001)。在低风险情况下,用户更有可能给予全剂量抗生素;在中风险情况下,更有可能给予挑战剂量;在高风险情况下,更有可能避免使用抗生素和/或咨询过敏科。共有 98 名用户(96%)表示该工具将增加他们在为过敏患者选择抗生素时的信心。
一种即时护理临床决策工具为非过敏专家提供了过敏专家设计的指导,是解决抗生素过敏问题的可扩展系统,无论过敏专家是否可用。该工具鼓励在低风险和中风险情况下合理使用抗生素,并在高风险情况下提高警惕。数字化支持工具应在质量改进和抗生素管理努力中得到考虑。