在医院环境中对药物干预的患者优先考虑:一项回顾性横截面研究
Maude Plourde1,2,3, Chantal Gilbert1,2, Mélanie Noël2
1Faculty of Pharmacy, Université Laval, Pavillon Ferdinand-Vandry, 1050 Avenue de la Médecine, Québec, Québec G1V 0A6, Canada.
药房软件数据可以有效地优先考虑患者的药物干预. 机器学习模型可以预测哪些患者需要干预,从而改善医院的资源配置.
科学领域:
- 临床药房 临床药房
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 有限的资源需要有效的患者优先级对药物干预.
- 药房软件数据为目标患者识别提供了潜在的途径.
- 医院护理单位在系统识别需要药剂师干预的患者方面面临挑战.
研究的目的:
- 描述一种使用药房软件优先考虑住院患者的方法.
- 为了比较接受与不接受药物干预的患者的特征.
- 探索药房软件数据对干预措施的预测能力.
主要方法:
- 850例住院病人的回顾性横截面研究 (2019年11月至2020年4月).
- 基于入院诊断和抗菌药物管理计划的优先级.
- 机器学习算法用于开发基于规则的干预预测模型.
主要成果:
- 45%的入院者接受了药物审查,81%的入院者进行了干预.
- 接受干预的患者具有较低的肌素清除值和特定的药物配置文件.
- 两种可解释的模型确定了药物干预的6或17个预测因素.
结论:
- 药房软件数据可以根据定义的标准有效地确定患者优先级.
- 基于规则的模型对需要药物干预的患者的系统识别有希望.
- 需要进一步的研究来验证和完善这些预测模型.
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