Wang Jiamin, Zhang Wenjing, Sun Kexin, Su Mingzhu, Zhang Yuqing, Su Jun, Sun Xiaojie
Centre for Health Management and Policy Research of Shandong University (Shandong Provincial Key New Think Tank), Jinan, 250012, China.
NHC Key Lab of Health Economics and Policy Research, Shandong University, Jinan, 250012, China.
Glob Health Res Policy. 2025 Mar 3;10(1):13. doi: 10.1186/s41256-025-00411-3.
Inpatient cancer patients often carry the dual burden of the cancer itself and comorbidities, which were recognized as one of the most urgent global public health issues to be addressed. Based on a case study conducted in a tertiary hospital in Shandong Province, this study developed a framework for the extraction of hospital information system data, identification of basic comorbidity characteristics, estimation of the comorbidity burden, and examination of the associations between comorbidity patterns and outcome measures. In the case study, demographic data, diagnostic data, medication data and cost data were extracted from the hospital information system under a stringent inclusion and exclusion process, and the diagnostic data were coded by trained coders with the 10th revision of the International Classification of Diseases (ICD-10). Comorbidities in this study was assessed using the NCI Comorbidity Index, which identifies multiple comorbidities. Rates, numbers, types and severity of comorbidity for inpatient cancer patients together form the characterization of comorbidities. All prevalent conditions in this cohort were included in the cluster analysis. Patient characteristics of each comorbidity cluster were described. Different comorbidity patterns of inpatient cancer patients were identified, and the associations between comorbidity patterns and outcome measures were examined. This framework can be adopted to guide the patient care, hospital administration and medical resource allocation, and has the potential to be applied in various healthcare settings at local, regional, national, and international levels to foster a healthcare environment that is more responsive to the complexities of cancer and its associated conditions. The application of this framework needs to be optimized to overcome a few limitations in data acquisition, data integration, treatment priorities that vary by stage, and ethics and privacy issues.
住院癌症患者常常承受着癌症本身和合并症的双重负担，这被认为是全球最亟待解决的公共卫生问题之一。基于在山东省一家三级医院开展的案例研究，本研究构建了一个框架，用于提取医院信息系统数据、识别基本合并症特征、评估合并症负担以及检验合并症模式与结局指标之间的关联。在该案例研究中，人口统计学数据、诊断数据、用药数据和费用数据在严格的纳入和排除流程下从医院信息系统中提取，诊断数据由经过培训的编码人员依据《国际疾病分类第10版》（ICD - 10）进行编码。本研究使用美国国立癌症研究所合并症指数评估合并症，该指数可识别多种合并症。住院癌症患者合并症的发生率、数量、类型和严重程度共同构成了合并症的特征描述。该队列中的所有普遍存在的病症都纳入了聚类分析。描述了每个合并症聚类的患者特征。识别了住院癌症患者不同的合并症模式，并检验了合并症模式与结局指标之间的关联。该框架可用于指导患者护理、医院管理和医疗资源分配，并且有可能在地方、区域、国家和国际层面的各种医疗环境中应用，以营造一个对癌症及其相关病症的复杂性更具响应性的医疗环境。该框架的应用需要进行优化，以克服数据获取、数据整合、因阶段而异的治疗优先级以及伦理和隐私问题等方面的一些局限性。
