Munavalli Jyoti R, Rao Shyam Vasudeva, Srinivasan Aravind, van Merode G G
CAPHRI School for Public Health and Primary Care, Maastricht University, The Netherlands; BNM Institute of Technology, India.
Forus Health, India; Maastricht University Medical Centre, The Netherlands.
Health Informatics J. 2020 Dec;26(4):2383-2406. doi: 10.1177/1460458220905380. Epub 2020 Feb 21.
Scheduling of resources and patients are crucial in outpatient clinics, particularly when the patient demand is high and patient arrivals are random. Generally, outpatient clinic systems are push systems where scheduling is based on average demand prediction and is considered for long term (monthly or bimonthly). Often, planning and actual scenario vary due to uncertainty and variability in demand and this mismatch results in prolonged waiting times and under-utilization of resources. In this article, we model an outpatient clinics as a multi-agent system and propose an intelligent real-time scheduler that schedules patients and resources based on the actual status of departments. Two algorithms are implemented: one for resource scheduling that is based on predictive demand and the other is patient scheduling which performs path optimization depending on the actual status of departments. In order to match resources with stochastic demand, a coordination mechanism is developed that reschedules the resources in the outpatient clinics in real time through auction-bidding procedures. First, a simulation study of intelligent real-time scheduler is carried out followed by implementation of the same in an outpatient clinic of Aravind Eye Hospital, Madurai, India. This hospital has huge patient demand and the patient arrivals are random. The results show that the intelligent real-time scheduler improved the performance measures like waiting time, cycle time, and utilization significantly compared to scheduling of resources and patients in isolation. By scheduling resources and patients, based on system status and demand, the outpatient clinic system becomes a pull system. This scheduler transforms outpatient clinics from open loop system to closed-loop system.
资源和患者的调度在门诊诊所中至关重要，尤其是当患者需求高且患者到达时间随机时。一般来说，门诊诊所系统是推式系统，其中调度基于平均需求预测，并从长期（每月或每两个月）进行考虑。由于需求的不确定性和变异性，计划和实际情况往往会有所不同，这种不匹配会导致等待时间延长和资源利用不足。在本文中，我们将门诊诊所建模为多智能体系统，并提出一种智能实时调度器，该调度器根据各科室的实际状态对患者和资源进行调度。实现了两种算法：一种是基于预测需求的资源调度算法，另一种是根据科室实际状态进行路径优化的患者调度算法。为了使资源与随机需求相匹配，开发了一种协调机制，通过拍卖投标程序实时重新调度门诊诊所的资源。首先，对智能实时调度器进行了模拟研究，随后在印度马杜赖的阿拉文德眼科医院的门诊诊所中进行了实施。该医院患者需求巨大，患者到达时间随机。结果表明，与单独进行资源和患者调度相比，智能实时调度器显著改善了诸如等待时间、周期时间和利用率等性能指标。通过根据系统状态和需求调度资源和患者，门诊诊所系统变成了拉式系统。这种调度器将门诊诊所从开环系统转变为闭环系统。
