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预约路径:梅奥诊所门诊环境中通过因果模型进行收益管理。

Appointment Pathways: Yield Management via Cause-and-Effect Modeling in the Outpatient Setting at Mayo Clinic.

作者信息

Keister Adrian C, Munden Derek R, Bailey Brian S

机构信息

Reporting and Analytics, Enterprise Office of Access Management, Mayo Clinic, Rochester, Minnesota (Dr Keister and Mr Munden); and Reporting and Analytics, Enterprise Office of Access Management, Mayo Clinic, Scottsdale, Arizona (Mr Bailey).

出版信息

J Ambul Care Manage. 2023;46(4):298-305. doi: 10.1097/JAC.0000000000000473. Epub 2023 Aug 7.

Abstract

Patients have multiple outpatient appointments for various reasons. Analyzing patients' related appointments provides insight into referral patterns, leading to recommendations for ideal care and more efficient planning. We model these appointments with causal graphs via Judea Pearl's causal graph approach. Once we define the causal relationships in the appointment data, we leverage a graph database and visualization software to investigate valuable patterns and relationships in patient care over time. The Pathways tool allows yield management at specialty, provider, or appointment levels. Leaders use this tool to anticipate a patient's downstream appointments; the tool provides insights into staffing and the impact of growing demand.

摘要

患者因各种原因有多次门诊预约。分析患者的相关预约可以深入了解转诊模式,从而为理想的护理和更有效的规划提供建议。我们通过朱迪亚·珀尔的因果图方法用因果图对这些预约进行建模。一旦我们在预约数据中定义了因果关系,我们就利用图形数据库和可视化软件来研究患者护理随时间的有价值模式和关系。路径工具允许在专科、提供者或预约级别进行收益管理。领导者使用此工具来预测患者的下游预约;该工具提供了有关人员配置以及需求增长影响的见解。

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