Journal
JOURNAL OF MEDICAL SYSTEMS
Volume 47, Issue 1, Pages -Publisher
SPRINGER
DOI: 10.1007/s10916-023-01907-6
Keywords
Head and neck cancer; Electronic health records; Oncology information systems; Data quality; Radiation oncology
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This study investigated the completeness and accuracy of routinely collected head and neck cancer data from an oncology information system (OIS) for research purposes. The OIS data was found to be less complete and accurate compared to the research dataset. This highlights the need for improved data collection practices to advance patient care.
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 (kappa = 1.000), high for age (kappa = 0.993), site (kappa = 0.992), T (kappa = 0.851) and N (kappa = 0.812) stage, radiotherapy dose (kappa = 0.889), fractions (kappa = 0.856), and duration (kappa = 0.818), and chemotherapy treatment (kappa = 0.871), substantial for overall stage (kappa = 0.791) and vital status (kappa = 0.689), moderate for grade (kappa = 0.547), and poor for performance status (kappa = 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.
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