Service Operations Management in the Mental Health Care Sector

本研究将调查统计和数学模型，以深入了解等候名单的规划和管理，特别是精神卫生保健系统。心理健康服务的常见操作方面包括：1)病人（顾客）被“分配”给一名临床医生（服务人员），在随机访问期间接受治疗服务；2)精神科患者在治疗过程中有较高的缺席率；3)精神科治疗方案在持续时间和结果分布方面变化很大。根据这些共同的方面，我将研究预测精神卫生保健服务的需求和等待时间的方法，并阐明导致精神病患者等待时间异常长的因素。使用数学模型，将调查操作决策的影响，例如，分配给临床医生的患者数量的能力上限，以及应该通过哪些方面对患者进行聚集以减少等待时间的变化。我将用经验数据验证我的数学模型。在学术水平上，本研究项目将对运筹学、卫生保健和服务部门的运营管理、精神病学护理计划和管理等领域做出贡献。它将解决与排队系统建模相关的重要问题，这些排队系统具有返回的异构客户，这些客户被分配给异构服务器，以便在随机交互期间接收服务。在实践层面上，本研究将有助于心理卫生保健领域的决策者制定计划方法。在当前大流行期间，由于家庭和工作环境中的高度压力以及孤独感的延长，对精神科护理的需求增加，因此改进等候名单管理的必要性变得更加明显。这些改进对于后covid -19时代至关重要。在加拿大和其他地方，特别是精神病患者，以及一般接受服务的顾客，将从更有效和高效地符合现实的计划中受益。

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.