癌症中心依赖肿瘤信息系统（OIS）中的电子信息来指导患者护理。我们调查了从OIS中获取的头颈部癌症（HNC）数据的完整性和准确性，以确定其在预后模型和其他研究中的适用性。符合条件的对象是在2000年至2017年被诊断为头颈鳞癌并接受放疗治疗的353名成年人。从单个中心的OIS中提取了13个与HNC预后有关的临床变量，并分别与研究数据集中所编译的数据进行比较。使用Cohen的kappa系数对分类变量进行比较，使用类内相关系数对连续变量进行比较，比较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（κ=1.000）、年龄（κ=0.993）、部位（κ=0.992）、T（κ=0.851）和N（κ=0.812）阶段、放疗剂量（κ=0.889）、分割（κ=0.856）和持续时间（κ= 0.818）以及化疗治疗（κ=0.871）的协议完全一致，整体阶段（κ=0.791）和生死状态（κ=0.689）的协议差异显著，等级中度（κ=0.547），而性能状态（κ=0.110）很差。另外31个变量捕捉差，无法进行统计比较。在HNC患者的OIS中记录临床信息是常规做法；然而，与为研究目的收集的数据相比，OIS数据不够正确和完整。例行数据采集的不足可能妨碍患者护理的进展。需要改进数据输入、与临床活动和工作流程的整合、系统可用性、数据字典和培训，才能使OIS数据产生强大的研究。从临床文档中挖掘数据可以补充结构化数据的采集。©2023.作者等。

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.© 2023. The Author(s).