Background Real-world data (RWD) is essential in head and neck cancer (HNC) research, offering insights into outcomes among diverse, comorbid patients often underrepresented in clinical trials.

Methods We developed RT-HaND-C, a multi-source clinical dataset integrating structured EHR data, unstructured data extracted using an AI-driven NLP tool, and previously manually-curated datasets, with extensive demographic, disease, laboratory, treatment, outcome and radiotherapy dosimetry data for all HNC oncology patients seen at our centre (2010–2023). The dataset underwent rigorous evaluation for accuracy, completeness, consistency, and usability.

Results The retrospective cohort comprises 2,895 HNC patients with over 1.8 million data points across over 2000 data categories. Accuracy assessments exceeded 98% for most variables. An example of usability testing showing rapid extraction and evaluation of longitudinal weight patterns post-radical radiotherapy is depicted.

Conclusions RT-HaND-C represents a novel, high-quality RWD resource and evaluation framework. The dataset is available for research and collaboration, with ongoing efforts to enhance completeness and support prospective updates.

The authors have declared no competing interest.

https://github.com/GSTT-Radiotherapy-Physics/RT-HaND/tree/main/Documentation/Reference%20Docs

This work was supported by the Wilson and Olegario Philanthropy. This work was supported by the Radiation Research Unit at the Cancer Research UK City of London Centre Award [C7893/A28990].

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The Information Governance team of Guy's and St Thomas' NHS Foundation Trust approved the secure storage of unanonymised data within the on-site data lake, ensuring controlled access and compliance with all system security policies. Research use of the data is carried out under the Research Ethics Committee (REC) of the NHS Health Research Authority approved Guy's Cancer Cohort framework (Reference: 18/NW/0297, IRAS Project ID: 231443).

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