论文标题
将机器学习与离散事件模拟集成，以改善护理管理中的健康转诊处理
Integrating Machine Learning with Discrete Event Simulation for Improving Health Referral Processing in a Care Management Setting
论文作者
论文摘要
入院后护理管理协调患者的转诊，以改善医院，尤其是老年人和长期病人的患者。在护理管理环境中，健康转诊是由托管护理组织（MCO）的专业部门处理的，该部门与许多其他实体进行互动，包括住院医院，保险公司和入院后护理提供者。在本文中，提出了一个机器学习引导的离散事件仿真框架，以改善健康推荐处理。开发了基于随机福雷林的预测模型来预测LOS和推荐类型。构建了两个仿真模型，以分别合并预测功能后代表转介处理系统和智能系统的AS配置。通过将推荐处理系统的预测模块合并以计划和优先转介，可以在减少平均转介创建延迟时间方面增强了整体性能。这项研究将强调放电后护理管理在改善健康质量和降低相关成本方面的作用。此外，本文演示了如何使用集成的系统工程方法来改进复杂的医疗保健系统的过程。
Post-discharge care management coordinates patients' referrals to improve their health after being discharged from hospitals, especially elderly and chronically ill patients. In a care management setting, health referrals are processed by a specialized unit in the managed care organization (MCO), which interacts with many other entities including inpatient hospitals, insurance companies, and post-discharge care providers. In this paper, a machine-learning-guided discrete event simulation framework to improve health referrals processing is proposed. Random-forest-based prediction models are developed to predict the LOS and referral type. Two simulation models are constructed to represent the as-is configuration of the referral processing system and the intelligent system after incorporating the prediction functionality, respectively. By incorporating a prediction module for the referral processing system to plan and prioritize referrals, the overall performance was enhanced in terms of reducing the average referral creation delay time. This research will emphasize the role of post-discharge care management in improving health quality and reducing associated costs. Also, the paper demonstrates how to use integrated systems engineering methods for process improvement of complex healthcare systems.
