# INTRODUCTION Protocol deviations (PD) in clinical trials present ongoing challenges, impacting data quality, patient safety, and trial efficiency. Traditionally documented as unstructured text, these deviations are difficult to analyse across trials, obscuring patterns and masking systemic issues. To address this, CRUK National Biomarker Centre's Digital Cancer Research (DCR) Team, in collaboration with The Christie NHS Foundation Trust, have developed Version 1.0 of the Protocol Deviation Monitoring (PDM) Digital Healthcare Product (DHP). This version is designed specifically for use by clinical trial sites, powered by Natural Language Processing (NLP) and shaped through a user-centred design process. Gathering requirements from clinical trial professionals has ensured the tool aligns with real-world clinical needs, featuring an intuitive interface and targeted functionality. The Protocol Deviation Monitoring DHP enables automated standardisation of unstructured PD text to structured categories using NLP. Data categories assigned to the data have been based on *unpublished cdisc* () protocol deviation categories which have been peer reviewed by a clinician and data managers for further additions and refinements. This standardardised categorisation of data allows a suite of visualisations to be created at both the site and study level, providing valuable insights for sites. Systematically tracking and visualising protocol deviations allows sites to identify and address recurring issues proactively, supporting higher data quality and patient safety. These visualisations facilitate a proactive, data-driven approach to quality assurance and contribute to more robust trial management. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://decmt.gitbook.io/protocol-deviation-digital-healthcare-product/introduction.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.