Voice Assistant for Outpatient Neurology Visits

Documentation duties account for a substantial portion of an outpatient physician's working time and reduce time available for direct patient interaction. Voice assistants based on automatic speech recognition and large language models are being developed to automate medical documentation across clinical specialties. However, such ambient AI-based services have not been systematically validated in Russian-language outpatient neurology practice integrated with a regional electronic health record platform. This pilot, multicenter, prospective, before-after study evaluated the feasibility and preliminary effectiveness of a voice assistant Service designed to automatically pre-fill the structured outpatient neurology visit protocol in the Moscow regional medical information system (EMIAS). The Service implements a pipeline of streaming speech-to-text transcription, two-speaker diarization, and large language model-based mapping of the dialogue between the physician and the patient onto the fields of the standardized neurology examination protocol. Five neurologists at five outpatient clinics in Moscow participated. The study comprised three stages: (1) baseline timing of consultations without the Service; (2) timing of consultations with the Service after a two-week adaptation period, with parallel evaluation of transcription and pre-fill quality and of physician and patient satisfaction; and (3) statistical analysis. Three hundred twenty consultations were timed (160 per stage). A stratified random sample of 30 audio-recording / generated-protocol pairs was used to evaluate Service quality; free-text fields were rated on a 5-domain Likert questionnaire and on a 10-point visual analogue scale, and binary fields were rated dichotomously to derive sensitivity, specificity, accuracy, Jaccard index, and false-positive rate. Patient satisfaction was assessed by the modified Patient Satisfaction Questionnaire 8 (PSQ-8); physician feedback was assessed by a custom questionna