使用基于系统的方法优化门诊癌症输液中心的吞吐量

Devon M. Zavacky, Anna M. Bustamante, Hayden C. Ratliff, R. Valdez
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引用次数: 0

摘要

在过去的30年里,美国各地建立了门诊输液中心,以满足日益增长的癌症治疗需求。虽然门诊治疗现在很普遍,但这些诊所仍然难以达到与服务需求相匹配的病人吞吐量水平。我们的研究考察了弗吉尼亚州中部一家输液中心的输液工作流程,该中心2022财年的患者吞吐率在全国输液中心中排名第二。我们将定性的利益相关者访谈和亲自观察与中心的定量患者预约数据进行了整理,以进行整体分析。接下来,我们使用流程映射和描述性统计来评估当前的吞吐量水平。最后,我们使用统计分析提出了未来吞吐量改进的策略,其中包括基于数据的试点测试。我们的分析证实了输液中心需要改进工艺。我们发现,患者空闲时间、药物类型和人员配置似乎是影响吞吐量的关键因素。此外,我们的结果表明,预约缓冲时间和药物预混合是对患者吞吐量最有利的因素。接下来的步骤应该集中在增强我们的预测建模和实现我们提出的吞吐量改进解决方案上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing Outpatient Cancer Infusion Center Throughput Using a Systems-Based Approach
Over the last 30 years, outpatient infusion centers have been constructed across the United States to meet rising demand for cancer care. While outpatient care is now commonplace, these clinics still struggle to achieve patient throughput levels that match demand for their services. Our study examined the infusion workflow at a central Virginia infusion center whose patient throughput rate in fiscal year 2022 fell in the second quartile of infusion centers nationwide. We collated qualitative stakeholder interviews and in-person observations with the center’s quantitative patient appointment data, to conduct a holistic analysis. Next, we evaluated current throughput levels with process mapping and descriptive statistics. Finally, we used statistical analysis to propose a strategy for future throughput improvement that included a data-based pilot test. Our analysis confirmed a need for process improvement at the infusion center. We found that patient idle times, drug types, and staffing appeared to be the key factors impacting throughput. Additionally, our results showed that appointment buffer times and drug pre-mixing were the most leverageable factors on patient throughput. Next steps should focus on enhancing our predictive modeling and implementing our proposed throughput improvement solutions.
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