Ambient Audio-Visual Capture for Clinical Documentation and Assessment (BLACKFRAME-AV-)

Ambient Audio-Visual Capture for Clinical Documentation, Assessment and Feedback in Medical Education

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Medical Education; Artificial Intelligence; Surgical Education; Clinical Documentation
• Intervention / Treatment: Device: BlackFrame ambient audio-visual capture platform

Detailed Description

BACKGROUND

Ambient AI scribes have achieved rapid uptake in outpatient and community settings but all published evaluations use audio-only capture. The inpatient ward round is a multimodal clinical event comprising verbal exchange, physical examination, procedural assessment, and non-verbal observation. Audio-only systems are structurally incapable of capturing observable clinical elements, representing construct under-representation under the Messick validity framework.

No published study has evaluated ambient audio-visual capture in a real inpatient setting, nor measured the educational impact of AI-generated formative feedback on ward rounds.

STUDY DESIGN

Mixed-methods feasibility and educational impact study. Surgical ward round at Yeovil District Hospital as the primary study context. Up to three ambient AV capture devices deployed simultaneously in separate side rooms on each study day. Ward rounds proceed sequentially through each room, allowing up to three consented encounters per study day.

PARTICIPANTS

Trainee participants: medical students (Year 3-5) and doctors in training (FY1 through registrar/ST grade) undertaking supervised clinical activities at the study site.

Patient participants: adult inpatients (age 18 or over) able to provide informed consent, admitted under the surgical team, clinically stable at the time of approach.

TARGET SAMPLE: 60-80 consented encounters across 20-30 trainee participants and up to 80 patient participants.

INTERVENTION

On each study day, eligible patients in up to three side rooms are consented before ward rounds begin. A BlackFrame ambient AV capture device is positioned visibly in each consented patient's room prior to the ward round, with clear patient-facing signage. Devices operate autonomously once positioned and do not require operator presence during the encounter.

The surgical ward round proceeds sequentially through each side room. After each encounter the AI platform produces: (a) a structured draft clinical note for supervising clinician review and countersignature before any use in the patient record; (b) a formative feedback report for the trainee covering clinical communication, examination technique, and documentation quality, delivered within 30 minutes.

OUTCOMES

Primary: (1) Change in trainee assessment scores from baseline to end-of-placement; (2) documentation time saved with versus without AI s

• Study Type: Interventional
• Enrollment (Estimated): 60
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: George Ryan; Phone Number: +447946196325; Email: georgeryan448@msn.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

Inclusion

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

Exclusion

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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Other
• Allocation: Non-Randomized
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Trainee participants; 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; 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.
Experimental: Patient participants; 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; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Cohen's kappa between AI-generated and expert human assessment scores per checklist domain	Cohen's kappa coefficient between AI-generated and independent expert human assessment scores, reported per checklist domain	Through study completion, approximately 12 weeks
Trainee-rated feedback quality score on 5-item Likert survey	trainee-rated feedback quality, perceived fairness, and utility (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	Structured rating score comparing AI-assisted versus standard ward round note on completeness, accuracy, and clinical safety content, rated by blinded clinical expert assessors	Through study completion, approximately 12 weeks

Collaborators and Investigators

• Sponsor: BlackFrame.ai
• Collaborators: The Cleveland Clinic; Somerset NHS Foundation Trust

Study record dates

Study Major Dates

• Study Start (Estimated): September 1, 2026
• Primary Completion (Estimated): November 30, 2026
• Study Completion (Estimated): November 30, 2026

Study Registration Dates

• First Submitted: June 1, 2026
• First Submitted That Met QC Criteria: June 10, 2026
• First Posted (Actual): June 16, 2026

Study Record Updates

• Last Update Posted (Actual): June 16, 2026
• Last Update Submitted That Met QC Criteria: June 10, 2026
• Last Verified: June 1, 2026

More Information

Terms related to this study

• Keywords: clinical assessment; inpatient; inter-rater reliability; ward round; software as a medical device; formative feedback; ambient scribe; audio-visual capture; AI documentation; trainee assessment
• Other Study ID Numbers: BF-AV-2026-01

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: As per protocol

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No