Voice Assistant for Outpatient Neurology Visits

Summary

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 questionnaire (including the Net Promoter Score) and by semi-structured in-depth interviews with thematic analysis using grounded theory.

The primary outcomes were the change between stages in (a) the time of focused physician attention to the patient and (b) the time spent filling and editing the protocol. Secondary outcomes addressed total consultation time, transcription quality (Word Error Rate), expert-rated quality of pre-filled fields, patient satisfaction, and physician satisfaction.

The study was conducted under the framework of the Moscow Healthcare Department experiment on the use of digital innovation technologies in health care (Order No. 153 of 21 February 2025), and was approved by the local independent ethics committee.

Conditions

• Burnout, Professional
• Documentation
• Medical Records Systems, Computerized
• Speech Recognition Software
• Health Services Research
• Neurology

Interventions

DEVICE

AI-based voice assistant Service for automated pre-filling of outpatient neurology visit protocol in EMIAS

The Service is software using artificial intelligence technologies that automatically pre-fills the structured outpatient neurology visit protocol in the Moscow regional medical information system (EMIAS) based on the audio-recorded dialogue between physician and patient. The pipeline comprises: (1) streaming audio capture by the EMIAS audio-recording client module, (2) chunk-wise transmission to the speech recognition service via REST API, (3) routing of transcription results through Apache Kafka topics to the pre-fill subsystem, (4) large language model-based mapping of the full transcript onto the JSON schema of the target document using clinical reference dictionaries, and (5) return of the structured pre-filled protocol to the physician's user interface. Audio is recorded via an active HD-capsule microphone placed at the physician's workstation. The physician reviews each pre-filled field, edits when necessary and signs the protocol.

Sponsors & Collaborators

• Information and Analytical Center of the Moscow Healthcare Department

  collaborator UNKNOWN

• Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

  lead OTHER

Principal Investigators

• Yuriy Vasilev, MD, PhD · Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

Study Design

Allocation

NA

Purpose

HEALTH_SERVICES_RESEARCH

Masking

NONE

Model

SINGLE_GROUP

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2025-10-26

Primary Completion

2026-01-30

Completion

2026-01-30

Countries

• Russia

Study Locations