A configurable discrete event simulation to model patient flow along cancer pathways

Many European healthcare systems are increasingly challenged in delivering services to ageing populations. The COVID-19 pandemic exacerbated the situation, particularly in the UK where the NHS had little spare capacity and was forced to suspend many routine services during the first and second wave of the pandemic. Cancer services have been particularly impacted, with patients currently facing long waiting times. Addressing current bottlenecks is crucial for clearing the accumulated backlog and meeting the national performance targets. We developed a configurable discrete event simulation model of cancer pathways and a corresponding bespoke software tool (“Cancer Pathway Simulator”) for use in partnership with hospital analysts. Our approach involves strong engagement with clinicians and operational managers to identify the right model configuration and parameter values for the pathway of interest. We discuss a case study in which we collaborated with a London-based hospital and applied the simulation tool to the lung cancer pathway. We conducted a bottleneck analysis and utilised a design of experiments approach to analyse the system. Results from the simulation were used to identify system bottlenecks and system characteristics that significantly affect the mean throughput time. This paper presents our tool and demonstrates its usefulness in informing decision-making regarding resource allocation.诸多欧洲医疗体系在为老年人口提供医疗服务时正面临日益严峻的挑战。新冠疫情大流行进一步加剧了这一困境，尤以英国国民保健署（National Health Service, NHS）为甚：该机构几乎没有富余产能，在疫情第一波与第二波流行期间被迫暂停了大量常规医疗服务。肿瘤诊疗服务受到的冲击尤为严重，当前患者需面对漫长的等待时长。解决当前存在的系统瓶颈，对于清理累积的诊疗积压、达成国家绩效目标至关重要。我们研发了一款可配置的肿瘤诊疗路径离散事件仿真（discrete event simulation）模型，以及一款配套的定制化软件工具——“肿瘤路径仿真器（Cancer Pathway Simulator）”，供医院分析师合作使用。我们的研究方法高度重视与临床医师及运营管理人员的协作，以针对目标诊疗路径确定合适的模型配置与参数取值。本文将介绍一项案例研究：我们与伦敦当地一家医院合作，将该仿真工具应用于肺癌诊疗路径。我们开展了瓶颈分析，并采用实验设计（design of experiments）方法对该系统进行分析。仿真结果被用于识别对平均诊疗时长存在显著影响的系统瓶颈与系统特征。本文介绍了我们研发的这款工具，并展示了其在为医疗资源配置相关决策提供参考方面的实用价值。