一种数据驱动的方法来优化门诊药房服务的等待时间:中断时间序列分析
Hazzaa Alghamdi1, Talal S Alshihayb2,3, Yazeed Alharbi1
1Pharmaceutical Care Division, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia.
Exploratory research in clinical and social pharmacy
|November 24, 2025
概括
数据驱动干预显著减少了门诊药房等待时间,提高了运营效率. 虽然注意到了即时的改善,但需要进一步的研究来确保长期的有效性和通用性.
科学领域:
- 医疗保健管理的管理
- 运营研究 运营研究
- 药房实践 在药房实践.
背景情况:
- 门诊药房的运营效率对于患者满意度和治疗坚持至关重要.
- 长时间的药房等待时间会对患者体验和健康结果产生负面影响.
研究的目的:
- 分析数据驱动干预对减少第三级医院门诊药房患者等待时间的影响.
- 为了实现从发票到患者服务的30分钟或更短的服务目标.
主要方法:
- 利用了来自"Qsmart"票务系统的数据 (2022年10月 - 2023年11月).
- 进行描述性分析以确定高峰时间和人员配置模式.
- 用人中断时间序列分析 (ITSA) 来评估干预的有效性.
主要成果:
- 峰值服务时间确定在上午9点至11点之间,机票数量在上午10点达到最高.
- 这项干预措施立即减少了等待时间 (0.1540; 95% CI: 0.0421, 0.2659).
- 干预后没有观察到明显的逐步改善 (没有额外的斜率变化).
结论:
- 数据驱动的干预有效地减少了门诊药房等待时间,显示出立即的积极影响.
- 战略运营调整可以提高药房服务效率和患者满意度.
- 需要进一步的研究来证实这些发现在各种医疗保健环境中的可持续性和适用性.
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