An automatic speech recognition approach for controlled medications prescription with natural language processing

The prescription and documentation of controlled medications require strict regulatory compliance and high transcription accuracy to prevent medication errors and ensure traceability. In many hospitals, these processes are still performed manually, increasing the risk of transcription errors, administrative delays, and non-compliance with regulatory standards, particularly for medications classified under fractions II and III of the Mexican General Health Law. Addressing this challenge requires intelligent systems capable of accurately transcribing and structuring medical prescriptions from spoken language. This study presents the design and development of an Automatic Speech Recognition (ASR) system integrated with Natural Language Processing (NLP) to support the generation and transcription of controlled medication prescriptions. The system architecture was developed following an analysis of the clinical workflow for medication requests, management, prescription, and transcription, conducted in collaboration with healthcare professionals from the hospital's Pharmacovigilance Department in Puebla, Mexico, and aligned with hospital operational standards. The methodology involved evaluating and fine-tuning three ASR models to improve transcription accuracy for medication names, dosages, and prescription instructions. NLP techniques were subsequently applied to identify and structure key prescription entities, ensuring compliance with national health regulations. Among the evaluated models, the Wav2Vec2 architecture developed by Jonatas Grosman demonstrated the best performance and was selected for implementation. Experimental results show that the optimized ASR model achieved a Word Error Rate (WER) of 6.30%, a precision of 94.72%, a recall of 91.73%, and an F1-score of 93.22%. These results demonstrate the effectiveness of the proposed approach in improving transcription accuracy while reducing false positives in prescription generation. The proposed system highlights the potential of ASR–NLP integration to enhance efficiency, accuracy, and regulatory compliance in hospital pharmacovigilance processes.