Department of Radiation Oncology, Prince of Wales Hospital, Level 1, Bright Building, Barker St, Randwick, NSW, 2031, Australia.
Prince of Wales Clinical School, Faculty of Medicine, University of New South Wales, Kensington, NSW, Australia.
J Med Syst. 2023 Jan 14;47(1):9. doi: 10.1007/s10916-023-01907-6.
Cancer centres rely on electronic information in oncology information systems (OIS) to guide patient care. We investigated the completeness and accuracy of routinely collected head and neck cancer (HNC) data sourced from an OIS for suitability in prognostic modelling and other research. Three hundred and fifty-three adults diagnosed from 2000 to 2017 with head and neck squamous cell carcinoma, treated with radiotherapy, were eligible. Thirteen clinically relevant variables in HNC prognosis were extracted from a single-centre OIS and compared to that compiled separately in a research dataset. These two datasets were compared for agreement using Cohen's kappa coefficient for categorical variables, and intraclass correlation coefficients for continuous variables. Research data was 96% complete compared to 84% for OIS data. Agreement was perfect for gender (κ = 1.000), high for age (κ = 0.993), site (κ = 0.992), T (κ = 0.851) and N (κ = 0.812) stage, radiotherapy dose (κ = 0.889), fractions (κ = 0.856), and duration (κ = 0.818), and chemotherapy treatment (κ = 0.871), substantial for overall stage (κ = 0.791) and vital status (κ = 0.689), moderate for grade (κ = 0.547), and poor for performance status (κ = 0.110). Thirty-one other variables were poorly captured and could not be statistically compared. Documentation of clinical information within the OIS for HNC patients is routine practice; however, OIS data was less correct and complete than data collected for research purposes. Substandard collection of routine data may hinder advancements in patient care. Improved data entry, integration with clinical activities and workflows, system usability, data dictionaries, and training are necessary for OIS data to generate robust research. Data mining from clinical documents may supplement structured data collection.
癌症中心依靠肿瘤信息系统（OIS）中的电子信息来指导患者护理。我们研究了从 OIS 中获取的头颈部癌症（HNC）常规数据的完整性和准确性，以评估其是否适合预后建模和其他研究。该研究纳入了 2000 年至 2017 年间诊断为头颈部鳞状细胞癌、接受放疗的 353 名成年人。从单一中心的 OIS 中提取了 13 个与 HNC 预后相关的临床变量，并与在研究数据集中单独编制的变量进行了比较。使用分类变量的 Cohen's kappa 系数和连续变量的组内相关系数比较了这两个数据集的一致性。研究数据的完整性为 96%，而 OIS 数据的完整性为 84%。性别数据的一致性为完美（κ=1.000），年龄（κ=0.993）、部位（κ=0.992）、T 分期（κ=0.851）和 N 分期（κ=0.812）、放疗剂量（κ=0.889）、分割（κ=0.856）和持续时间（κ=0.818）、化疗治疗（κ=0.871）的一致性很高，整体分期（κ=0.791）和生存状态（κ=0.689）的一致性很好，分级（κ=0.547）的一致性为中度，表现状态（κ=0.110）的一致性很差。其他 31 个变量记录较差，无法进行统计学比较。OIS 中对头颈部癌症患者的临床信息进行常规记录；然而，OIS 数据的准确性和完整性均不如为研究目的而收集的数据。常规数据的收集不规范可能会阻碍患者护理的进步。需要改进数据录入、与临床活动和工作流程的集成、系统可用性、数据字典和培训，以便 OIS 数据能够生成可靠的研究结果。从临床文档中进行数据挖掘可能会补充结构化数据的收集。
