Abstract

Accurate cancer databases enable auditing of patient management, and this knowledge facilitates optimizing care. A multi-institutional organization, the largest single provider of radiation oncology services in Australia, has developed its own national database (ND). All patients are entered on the ND as a prerequisite for generating a radiotherapy prescription. A significant component of the ND is automated, but manual input from the treating radiation oncologist (RO) is also required. The purpose of this study was to assess the compliance and accuracy of the data entered on this ND for head and neck cancer (HNC) patients. We included all HNC patients with either oral cavity cancer or oropharynx cancer (ICD-10 coding) treated between September 2021 and September 2022 to assess compliance. We randomly selected 25% of these cases and assigned them to 3 HNC ROs to manually review the accuracy of all clinical data points. There were 166 HNC patients, 139 oropharynx and 27 oral cavity. Compliance in the 166 patients was excellent (94% or higher) for the majority of data points - age, gender, diagnosis ICD code, diagnosis date, laterality, TNM classification, radiotherapy dose, fractionation and technique and start and completion dates. Compliance was good (85% or more) for smoking history, use of chemotherapy, and p16 status (oropharynx). Compliance was poor (43%) for specific chemotherapy regimens. Accuracy was high (92% or higher) for diagnosis ICD code, smoking history, use of chemotherapy; good (87% or higher) for p16 status (oropharynx), laterality and histopathology; and poor for date of diagnosis (75%), TNM classification (62%) and specific chemotherapy regimens (29%). The ND is a powerful tool for assessing patient care. Overall, compliance was very good. Accuracy was very good for most items, and we have highlighted areas where improvements can be made. This study shows that a compliant and accurate ND is achievable and supports the next goal of additional items to be included in the ND, specifically patient outcome data.

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