Voice Assistant for Outpatient Neurology Visits

NCT07647159 | Completed

• 1 other identifier: 2025-9
• Study Type: interventional
• Target: 320
• Locations: 1 country
• Sites: 1

Brief 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

Keywords

voice assistant for clinicians; speech recognition; large language model; ambient AI; electronic health record; neurology; outpatient appointment; patient satisfaction; documentation burden; pilot study

Outcome Measures

Primary Outcomes (2)

• Change in physician focused-attention time on the patient

  Time (in minutes) of focused physician attention on the patient, including visual contact and active interaction during examination, measured from three synchronized sources (EMIAS logs, screen video, webcam video)

  Up to 14 weeks: pre-intervention measurements collected per consultation during study Weeks 1-2 (Stage 1); post-intervention measurements collected per consultation during study Weeks 5-14 (Stage 2, after a 2-week adaptation period).

• Change in protocol-filling time

  Time (in minutes) spent filling and editing the visit protocol, including keyboard input, mouse clicks, copying and pasting, measured from the three synchronized sources.

  Up to 14 weeks: pre-intervention measurements collected per consultation during study Weeks 1-2 (Stage 1); post-intervention measurements collected per consultation during study Weeks 5-14 (Stage 2, after a 2-week adaptation period).

Secondary Outcomes (7)

• Change in total consultation time

  Up to 14 weeks: pre-intervention measurements collected per consultation during study Weeks 1-2 (Stage 1); post-intervention measurements collected per consultation during study Weeks 5-14 (Stage 2, after a 2-week adaptation period).

• Transcription quality (Word Error Rate, WER)

  Up to 14 weeks: assessed once per consultation on a stratified random sample of 30 audio recordings collected during study Weeks 5-14 (post-intervention period).

• Expert-rated quality of free-text protocol fields

  Up to 14 weeks: assessed once per protocol on a stratified random sample of 30 Service-generated protocols from consultations conducted during study Weeks 5-14 (post-intervention period).

• Quality of pre-filling for binary protocol fields

  Up to 14 weeks: assessed once per protocol on a stratified random sample of 30 Service-generated protocols from consultations conducted during study Weeks 5-14 (post-intervention period).

• Overall protocol quality (Visual Analogue Scale)

  Up to 14 weeks: assessed once per protocol on a stratified random sample of 30 Service-generated protocols from consultations conducted during study Weeks 5-14 (post-intervention period).

Study Arms (1)

Outpatient Neurology Visits with AI Voice Assistant

EXPERIMENTAL

All participating neurologists conducted outpatient consultations using the Service after a two-week adaptation period. Each consultation followed the approved script; the Service captured the audio, transcribed it in real time, generated a pre-filled visit protocol via a large language model, and returned it for physician review and signing.

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

Interventions

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

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.

Outpatient Neurology Visits with AI Voice Assistant

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• First-time outpatient neurology visit conducted by a participating physician according to the approved consultation script.
• Age 18 years or older.
• Signed informed consent to participate in the study.
• Signed consent to processing of personal data.
• Clinical condition that allowed the patient to complete the modified Patient Satisfaction Questionnaire 8 (PSQ-8).

You may not qualify if:

• Consultation longer than 45 minutes.
• Consultation shorter than 5 minutes, not requiring a full examination (e.g., prescription renewal, clarification of follow-up questions only).
• Technical failures preventing valid interpretation of the consultation or its timing (microphone failure, malfunction of the recording software, malfunction of the Service).
• Deviation from the approved consultation script.

Sponsors & Collaborators

• Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department: lead
• Information and Analytical Center of the Moscow Healthcare Department: collaborator

Study Sites (1)

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

Moscow, 125051, Russia

Location

MeSH Terms

Conditions

Burnout, Professional; Patient Satisfaction

Condition Hierarchy (Ancestors)

Occupational Stress; Occupational Diseases; Burnout, Psychological; Stress, Psychological; Behavioral Symptoms; Behavior; Treatment Adherence and Compliance; Health Behavior

Study Officials

• Yuriy Vasilev, MD, PhD

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

  STUDY DIRECTOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: Before-After comparison: the same five neurologists and five clinics observed during Stage 1 (without the Service) and Stage 2 (with the Service), with at least 30 timed consultations per physician in each stage.

Study Record Dates

• First Submitted: May 27, 2026
• First Posted: June 15, 2026
• Study Start: October 26, 2025
• Primary Completion: January 30, 2026
• Study Completion: January 30, 2026
• Last Updated: June 15, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will not share

Locations

RU (1)