Zhou Yuan, Viswanatha Amith, Abdul Motaleb Ammar, Lamichhane Prabin, Chen Kay-Yut, Young Richard, Gurses Ayse P, Xiao Yan
Department of Industrial, Manufacturing, and Systems Engineering, The University of Texas at Arlington, Arlington, Texas, USA.
Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, Texas, USA.
Comput Ind Eng. 2023 Mar;17. doi: 10.1016/j.cie.2023.109069. Epub 2023 Feb 18.
Primary care plays a vital role for individuals and families in accessing care, keeping well, and improving quality of life. However, the complexities and uncertainties in the primary care delivery system (e.g., patient no-shows/walk-ins, staffing shortage, COVID-19 pandemic) have brought significant challenges in its operations management, which can potentially lead to poor patient outcomes and negative primary care operations (e.g., loss of productivity, inefficiency). This paper presents a decision analytics approach developed based on predictive analytics and hybrid simulation to better facilitate management of the underlying complexities and uncertainties in primary care operations. A case study was conducted in a local family medicine clinic to demonstrate the use of this approach for patient no-show management. In this case study, a patient no-show prediction model was used in conjunction with an integrated agent-based and discrete-event simulation model to design and evaluate double-booking strategies. Using the predicted patient no-show information, a prediction-based double-booking strategy was created and compared against two other strategies, namely random and designated time. Scenario-based experiments were then conducted to examine the impacts of different double-booking strategies on clinic's operational outcomes, focusing on the trade-offs between the clinic productivity (measured by daily patient throughput) and efficiency (measured by visit cycle and patient wait time for doctor). The results showed that the best productivity-efficiency balance was derived under the prediction-based double-booking strategy. The proposed hybrid decision analytics approach has the potential to better support decision-making in primary care operations management and improve the system's performance. Further, it can be generalized in the context of various healthcare settings for broader applications.
初级保健在个人和家庭获得医疗服务、保持健康以及提高生活质量方面发挥着至关重要的作用。然而，初级保健服务提供系统中的复杂性和不确定性（例如患者爽约/临时就诊、人员短缺、新冠疫情）给其运营管理带来了重大挑战，这可能会导致患者治疗效果不佳以及初级保健运营出现负面情况（例如生产力损失、效率低下）。本文提出了一种基于预测分析和混合模拟开发的决策分析方法，以更好地应对初级保健运营中潜在的复杂性和不确定性。在一家当地家庭医学诊所进行了案例研究，以展示该方法在患者爽约管理中的应用。在这个案例研究中，将患者爽约预测模型与基于智能体和离散事件的集成模拟模型结合使用，来设计和评估双重预约策略。利用预测的患者爽约信息，创建了一种基于预测的双重预约策略，并与其他两种策略（即随机预约和指定时间预约）进行比较。然后进行了基于场景的实验，以检验不同双重预约策略对诊所运营结果的影响，重点关注诊所生产力（以每日患者接待量衡量）和效率（以就诊周期和患者等待医生的时间衡量）之间的权衡。结果表明，基于预测的双重预约策略实现了最佳的生产力 - 效率平衡。所提出的混合决策分析方法有潜力更好地支持初级保健运营管理中的决策制定并提高系统性能。此外，它可以在各种医疗环境中进行推广以实现更广泛的应用。
