在UNC健康开发药物订单自动验证框架
Noemie M Kanene1, Kayla Waldron2, Mary-Haston Vest3
1MedStar Georgetown University Hospital, Washington, DC, USA.
概括
自动验证 (AV) 可以简化医院的药物订单审查. 一个开发的风险评估工具发现,只有6.89%的订单对AV的风险很低,这表明,如果仔细实施,可能会提高效率.
科学领域:
- 医疗信息学 医疗信息学
- 临床药房 临床药房
- 药物安全 药物安全
背景情况:
- 自动验证 (AV) 自动化了电子健康记录中的药物验证,绕过了药剂师的审查.
- 成功实施需要解决大批量低风险药物订单的安全性和有效性问题.
- 需要一个可复制的框架来识别医院系统内适合AV的药物.
研究的目的:
- 为了确定AV药物的风险分层的参数.
- 开发一个可复制的框架模型来识别适合AV的药物在UNC健康.
主要方法:
- 使用修改后的Delphi方法来达成风险分层参数的共识.
- 从2023年10月起,风险分层工具已被追溯到药物订单.
- 该研究评估了潜在的自验证订单的不良事件风险.
主要成果:
- 对于AV风险评估工具 (AVRAT),55个标准达成了共识.
- 标记高风险AV订单的关键标准包括年龄,功能,血红蛋白,血小板,体重和RRT.
- 使用AVRAT进行的概念验证评估表明,6.89%的订单对AV的风险很低.
结论:
- 一项概念验证研究成功开发了AV利用的框架.
- 在医院系统中,AV有可能减少药物订单审查时间.
- 相对较小的比例的药物订单可能有资格获得AV.
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