Ambient Audio-Visual Capture for Clinical Documentation and Assessment

NCT07649772 | Not Yet Recruiting

• 1 other identifier: BF-AV-2026-01
• Study Type: interventional
• Target: 60
• Locations: 0 countries
• Sites: N/A

Brief Summary

AI-powered tools that automatically document clinical conversations are being adopted rapidly in outpatient settings but have not been evaluated in hospital wards. Existing tools use audio recording only, which cannot capture physical examination findings, procedural observations, or clinical safety behaviours - elements of a ward round that are visible but not audible. This study evaluates an ambient audio-visual (AV) capture system - BlackFrame - that uses both microphone and camera to generate accurate clinical documentation and structured educational feedback in a real inpatient surgical ward setting. Medical students and doctors in training participate in supervised ward round encounters with consenting adult inpatients. The BlackFrame AI platform generates: (a) a structured draft clinical note for the supervising clinician to review and countersign before any use in the patient record; and (b) formative feedback for the trainee, delivered within 30 minutes, covering clinical communication, examination technique, and documentation quality. The study measures whether AI-generated feedback improves trainee clinical performance over a placement, how much documentation time is saved, and whether the system is acceptable to patients and clinicians. No AI-generated text enters the patient record without explicit clinician review and sign-off. All participation is voluntary.

Conditions

Clinical Documentation; Artificial Intelligence; Surgical Education; Medical Education

Keywords

ambient scribe; audio-visual capture; ward round; formative feedback; clinical assessment; AI documentation; inpatient; software as a medical device; inter-rater reliability; trainee assessment

Outcome Measures

Primary Outcomes (1)

• Mean documentation time per encounter with versus without AI scribe assistance

  mean difference in time (minutes) to produce a clinical ward round note with versus without AI scribe assistance. Analysed using paired comparison with 95% confidence interval.

  Through study completion, approximately 12 weeks

Secondary Outcomes (3)

• Cohen's kappa between AI-generated and expert human assessment scores per checklist domain

  Through study completion, approximately 12 weeks

• Trainee-rated feedback quality score on 5-item Likert survey

  After first study encounter, approximately within 1 week of study enrolment

• Blinded expert rating of AI-assisted clinical note completeness and accuracy

  Through study completion, approximately 12 weeks

Study Arms (2)

Trainee participants

EXPERIMENTAL

Medical students (Year 3-5) and doctors in training (FY1 through registrar/ST grade) undertaking supervised clinical activities on the surgical ward at the study site. Participants receive AI-generated formative feedback within 30 minutes of each ward round encounter and complete baseline and follow-up clinical assessments.

Device: BlackFrame ambient audio-visual capture platform

Patient participants

EXPERIMENTAL

Adult inpatients aged 18 or over admitted under the surgical team at the study site, able to provide informed consent and clinically stable at the time of approach. Patients consent to ambient AV recording of their ward round encounter. Their care is unaffected by participation.

Device: BlackFrame ambient audio-visual capture platform

Interventions

BlackFrame ambient audio-visual capture platform DEVICE

Fixed camera and microphone array positioned visibly in the patient's room captures the ward round encounter. The AI platform processes the recording to generate: (a) a structured draft clinical note for supervising clinician review and countersignature; (b) a formative feedback report for the trainee covering clinical communication, examination technique, and documentation quality, delivered within 30 minutes of the encounter.

Patient participants; Trainee participants

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Trainee participants:
• Doctor in training (FY1 through registrar/ST grade) undertaking a supervised clinical activity at a participating NHS study site
• Able to provide written informed consent in English
• Patient participants:
• Adult inpatient aged 18 years or over
• Able to provide written informed consent in English
• Admitted under a surgical team at a participating study site
• Clinically stable at the time of approach

You may not qualify if:

• Trainee participants:
• Unwilling to be audio-visually recorded
• Unable to provide written informed consent
• Any trainee where participation could create a direct conflict with a concurrent formal assessment or appraisal process at that session
• Patient participants:
• Age under 18 years
• Unable to provide informed consent (including temporary incapacity due to acute illness, sedation, or delirium)
• Acute clinical deterioration at the time of approach
• Encounter involves sensitive disclosures in mental health, sexual health, or safeguarding unless a specific sub-protocol with additional consent measures is in place
• Patient has previously declined participation and does not wish to be re-approached
• Non-English speaking patients where no appropriate interpreter is available to support the consent process

Sponsors & Collaborators

• BlackFrame.ai: lead
• The Cleveland Clinic: collaborator
• Somerset NHS Foundation Trust: collaborator

Central Study Contacts

George Ryan

CONTACT

+447946196325; georgeryan448@msn.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: OTHER
• Intervention Model: SINGLE GROUP
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 1, 2026
• First Posted: June 16, 2026
• Study Start (Estimated): September 1, 2026
• Primary Completion (Estimated): November 30, 2026
• Study Completion (Estimated): November 30, 2026
• Last Updated: June 16, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will share