Service Operations Management in the Mental Health Care Sector

这项研究将调查统计和数学模型，以发展对一般等待名单的规划和管理的洞察力，特别是精神卫生保健系统。精神健康服务的常见操作方面包括：1)患者(客户)被“分配”给临床医生(服务器)，在随机访问期间接受治疗服务；2)精神病患者在治疗过程中缺勤率很高；3)精神治疗计划在持续时间和结果分布方面非常不同。从这些共同的方面了解到，我将研究预测精神健康护理服务需求和等待时间的方法，并阐明导致精神病患者等待时间特别长的因素。使用数学模型，将调查运营决策的影响，例如，容量上限对分配给临床医生的患者数量的影响，以及应根据哪些方面对患者进行分组以减少等待时间的变化。我将使用经验数据来验证我的数学模型。在学术层面上，这项研究计划将有助于运筹学、医疗保健和服务部门的运营管理以及精神护理规划和管理领域的研究。它将解决与排队系统的建模有关的重要问题，这些排队系统具有返回的异类客户，这些客户被分配到异类服务器以在随机数量的交互期间接收服务。在实践层面上，这项研究将帮助精神卫生保健领域的决策者制定规划方法。由于家庭和工作环境的高度压力和长期的孤独，在当前大流行病期间对精神护理的需求增加的背景下，改进等待名单管理的必要性变得更加明显。这些改进对后新冠肺炎时代至关重要。特别是精神病患者，以及在加拿大和其他地方接受服务的一般客户，将从更有效和高效地符合现实的规划中受益。

This research will investigate statistical and mathematical models to develop insights into planning and management of waiting lists in general, and mental health care systems in particular. Common operational aspects of mental health services include: 1) a patient (customer) is "assigned" to a clinician (server) to receive treatment services during a random number of visits; 2) psychiatric patients have high no-show rates in their treatment sessions; and 3) psychiatric treatment plans are highly variable in terms of duration and the distribution of their outcomes. Informed by these common aspects, I will investigate methods to forecast demand and waiting time for mental health care services and to shed light on factors that drive exceptionally long waiting times for psychiatric patients. Using the mathematical models, the impacts of operational decisions, for example, capacity cap on the number of patients assigned to a clinicians, and aspects by which patients should be clustered to reduce the variability in waiting times, will be investigated. I will validate my mathematical models using empirical data. At an academic level, this research program will contribute to the fields of operations research, operations management in the health care and service sectors, and psychiatric care planning and management. It will address important issues regarding the modeling of queueing systems with returning heterogeneous customers that are assigned to heterogeneous servers to receive service during a random number of interactions. At a practical level, this research will aid decision makers in the field of mental health care with their planning methods. The need for improvements in management of waiting lists has become even more evident in the context of increased demand for psychiatric care during the current pandemic, due to high levels of stress in family and work settings and extended loneliness. These improvements will be essential for the post-Covid-19 era. Psychiatric patients in particular, and customers receiving service in general, in Canada and elsewhere, will benefit from planning that matches realities more effectively and efficiently.