药剂师主导的外科药物处方优化和预测服务改善了患者的治疗结果 - - 一项基于机器学习的研究
Xianlin Li1,2, Xiunan Yue2, Lan Zhang3
1Department of Pharmacy, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
Frontiers in pharmacology
|March 31, 2025
概括
机器学习模型集成到药剂师领导的服务中,提高了手术处方的安全性. 这种手术药物处方优化和预测 (SMPOP) 服务减少了住院时间和费用.
科学领域:
- 药理学 药理学是指药理学的学科.
- 医疗信息学 医疗信息学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 由于复杂的药物需求,优化手术患者的处方是至关重要的.
- 提高药物安全性和患者结果需要创新的方法.
- 一个由药剂师领导的外科药物处方优化和预测 (SMPOP) 服务是使用基于机器学习 (ML) 的警告模型开发的.
研究的目的:
- 实施和评估一个由药剂师领导的SMPOP服务,与基于ML的警告模型集成.
- 改善药物安全性和手术环境中的患者结果.
- 评估SMPOP服务对处方适当性,药物不良反应 (ADR) 和医疗保健成本的影响.
主要方法:
- 一个回顾性队列设计,在三级医院在三个阶段 (2019-2024) 进行前性实施.
- 使用6983个潜在的处方错误开发了一个ML模型;随机森林模型显示了最高的准确性 (AUC=0.893).
- 基于处方适当性,不良反应,停留时间和住院费用的SMPOP服务有效性的评估.
主要成果:
- ML模型在识别潜在的处方错误方面取得了高准确性.
- 实施SMPOP服务导致药剂师医生沟通改善和接受干预 (71.3%).
- 住院时间,住院总费用和药物费用 (p < 0.05) 显著减少.
结论:
- SMPOP服务有效地提高了处方的适当性,并减少了药物不良反应.
- ML和药剂师领导的服务的整合显著降低了住院时间和相关成本.
- 医疗保健服务的持续创新对于改善患者护理和资源管理至关重要.
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