Accuracy depends on the quality of input data and the robustness of the AI model. When trained on validated clinical coding guidelines and historical coding outcomes, AI systems can significantly increase accuracy and reduce common attribution errors. They are designed to support, not replace, expert human coders.

No. AI is used to enhance coding workflows, not replace human expertise. Coders remain responsible for reviewing, validating, and confirming all recommendations. AI simply accelerates processing and reduces manual errors, allowing coders to focus on complex or ambiguous cases.

Yes. Telefónica Tech’s AI solutions are built with robust governance, data privacy controls, and regulatory compliance frameworks aligned to NHS, GDPR, and local health authority standards. All processing is secure, transparent, and auditable.

AI models use structured and unstructured clinical data such as clinician notes, discharge summaries, diagnostic reports, and approved clinical coding guidelines. Only permitted data sources defined by the organisation’s governance framework are used for training and inference.

By automating repetitive tasks, pre-populating code suggestions, and highlighting areas needing review, AI reduces processing time per case. When scaled across thousands of coding decisions, it delivers substantial operational efficiency.

More accurate clinical codes help ensure better reporting, improved clinical decision-making, and more reliable healthcare analytics. This translates into improved patient pathways, service planning, and overall quality of care.