癌症路径患者流可配置离散事件仿真模型
用于模拟癌症路径中患者流程的可配置离散事件仿真模型，应用于肺癌路径瓶颈分析。
基本信息
资源简介
癌症路径患者流可配置离散事件仿真模型
【数据集背景】
本数据集源站标题为《A configurable discrete event simulation to model patient flow along cancer pathways》。配套论文《A configurable discrete event simulation to model patient flow along cancer pathways》发表于 Journal of the Operational Research Society（2025），DOI：10.1080/01605682.2025.2557921。源站首次发布：2025-09-12。
【数据内容】
源站原始描述：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…
【数据结构与技术规格】
文件数 2 个，合计 105.80 KB。tjor_a_2557921_sm8686.docx：application/vnd.openxmlformats-officedocument.wordprocessingml.document，65.86 KB。tjor_a_2557921_sm8685.docx：application/vnd.openxmlformats-officedocument.wordprocessingml.document，39.94 KB。数据集 DOI：10.6084/m9.figshare.30111235.v1。
【主题与分类】
源站学科分类：Medicine、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Science Policy、Computational Biology。主题标签：Cancer care services、operational research、discrete event simulation、design of experiments。
【适用方向】
源站学科分类标注为 Medicine、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Science Policy、Computational Biology。
【获取与许可】
源站页面：https://tandf.figshare.com/articles/dataset/A_configurable_discrete_event_simulation_to_model_patient_flow_along_cancer_pathways/30111235
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：A configurable discrete event simulation to model patient flow along cancer pathways. Taylor & Francis. Gjerloev, Amalia; Impelluso, Giulia; Grieco, Luca; Jani, Yogini; Pagel, Christina; Crowe, Sonya
【使用注意】
· 源站最后修改于 2026-08-24，引用时请注意版本时效。
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：A configurable discrete event simulation to model patient flow along cancer pathways
- DOI：10.6084/m9.figshare.30111235.v1
- 发布日期：2025-09-12
- 文件数：2 个
- 文件总体积：105.80 KB
- 源站页面：https://tandf.figshare.com/articles/dataset/A_configurable_discrete_event_simulation_to_model_patient_flow_along_cancer_pathways/30111235
命令行下载
curl -L -o "tjor_a_2557921_sm8686.docx" "https://ndownloader.figshare.com/files/57897502"
curl -L -o "tjor_a_2557921_sm8685.docx" "https://ndownloader.figshare.com/files/57897505"
文件清单
tjor_a_2557921_sm8686.docx（65.86 KB）tjor_a_2557921_sm8685.docx（39.94 KB）
文件总体积 105.80 KB（源站 API 实测）。
数据说明
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.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.6084/m9.figshare.30111235.v1（source: https://tandf.figshare.com/articles/dataset/A_configurable_discrete_event_simulation_to_model_patient_flow_along_cancer_pathways/30111235）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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