The digital transformation of health systems and the increasing adoption of data-driven public health strategies have intensified the need for methods capable of capturing, structuring, and analyzing information derived from clinical interactions. In the Brazilian Unified Health System (SUS), orthopedic rehabilitation and therapeutic exercise prescription rely heavily on communication between healthcare professionals and patients, particularly with regard to understanding instructions, reporting symptoms, and identifying barriers to treatment continuity. However, much of this information remains embedded in unstructured spoken interactions, limiting its use for monitoring and evaluation purposes. This study presents a prospective methodological protocol for the future development and validation of a speech analytics architecture designed to analyze verbal interactions in orthopedic rehabilitation within the SUS. The proposed framework integrates automatic speech recognition, speaker diarization, semantic processing with large language models (LLMs), biomedical entity extraction, and retrieval-grounded analytical components to generate structured indicators from clinical speech. In addition, the manuscript includes an illustrative simulation based on administrative proxy data converted into synthetic narratives in order to exemplify the expected structure of downstream analytical outputs. This simulation does not constitute validation of the full audio-based pipeline, but rather serves to clarify the proposed analytical workflow. Overall, the protocol establishes a structured methodological basis for future empirical studies aimed at evaluating the technical performance, semantic validity, and potential public health utility of speech analytics in rehabilitation monitoring, under appropriate ethical, regulatory, and data protection safeguards.
Neto et al. (Fri,) studied this question.
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