Clinical DocumentationBLACKFRAME-AV-NCT07649772
Ambient Audio-Visual Capture for Clinical Documentation and Assessment
A study testing BlackFrame ambient audio-visual capture platform in people with Clinical Documentation and Artificial Intelligence, run by BlackFrame.ai.
Trial 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 … Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Clinical Documentation, Artificial Intelligence, Surgical Education
- What is being tested?
- BlackFrame ambient audio-visual capture platform
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 60 participants
- Where
- No sites listed yet
- Study dates
- 1 Sep 2026to 30 Nov 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- 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
Who cannot join
- 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
Show all 11Show fewer
- 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
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: 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.
Read more
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.
Read the 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
- Being tested
- BlackFrame ambient audio-visual capture platform
- 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
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
BlackFrame ambient audio-visual capture platformDevice
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. Receives: Device: BlackFrame ambient audio-visual capture platform |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Through study completion, approximately 12 weeks
Secondary outcomes (3)
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
Measured: 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)
Measured: 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
Measured: Through study completion, approximately 12 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Jun 2026
- Study start1 Sep 2026 (estimated)
- Primary completion30 Nov 2026 (estimated)
- Study completion30 Nov 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Clinical DocumentationStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Non-randomised
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 60 participants (estimated)
Sponsor and investigators
- Lead sponsor
- BlackFrame.ai
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- The Cleveland Clinic, Somerset NHS Foundation Trust
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07649772
- Sponsor's protocol ID
- BF-AV-2026-01
- Acronym
- BLACKFRAME-AV-
- Status last verified
- Jun 2026
- First submitted
- 1 Jun 2026
- Last updated
- 16 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: As per protocol
Study at a glance
From the registry record, last updated by the sponsor on 16 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Ambient Audio-Visual Capture for Clinical Documentation, Assessment and Feedback in Medical Education
- ClinicalTrials.gov ID
- NCT07649772
- Acronym / sponsor ID
- BLACKFRAME-AV- / BF-AV-2026-01
- Keywords
- ambient scribe, audio-visual capture, ward round, formative feedback, clinical assessment, AI documentation, inpatient, software as a medical device
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 60 participants (estimated)
- Lead sponsor
- BlackFrame.aiIndustry (pharma, biotech, devices)
- Collaborators
- The Cleveland Clinic, Somerset NHS Foundation Trust
- Study start
- 1 Sep 2026 (estimated)
- Primary completion
- 30 Nov 2026 (estimated)
- Study completion
- 30 Nov 2026 (estimated)
- First posted
- 16 Jun 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Primary purpose
- Other
- Allocation
- Non-randomised
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Trainee participants | Experimental | Device: BlackFrame ambient audio-visual capture platformMedical 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. |
| Patient participants | Experimental | Device: BlackFrame ambient audio-visual capture platformAdult 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. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (8 listed)
- 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
- 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
Key exclusion criteria (11 listed)
- 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
- Age under 18 years
- Unable to provide informed consent (including temporary incapacity due to acute illness, sedation, or delirium)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| BlackFrame ambient audio-visual capture platformFixed 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. | Device | Patient participants, Trainee participants |
Primary outcome
Mean documentation time per encounter with versus without AI scribe assistance
Time frame: Through study completion, approximately 12 weeks
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.
3 secondary outcomes
Cohen's kappa between AI-generated and expert human assessment scores per checklist domain
Time frame: Through study completion, approximately 12 weeks
Trainee-rated feedback quality score on 5-item Likert survey
Time frame: After first study encounter, approximately within 1 week of study enrolment
Blinded expert rating of AI-assisted clinical note completeness and accuracy
Time frame: Through study completion, approximately 12 weeks
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Jun 2026.
- Individual participant dataSharing planned: Yes. As per protocol
Related trials for Clinical Documentation
Active, not recruiting
Ambient AI for Reducing Nursing Staff Documentation Time
Nursing Documentation Burden
Not yet recruiting
A Pragmatic Trial of AI-Assisted Discharge Summary Generation
Inpatient Internal Medicine Patients, Patient Discharge
Recruiting
Ambient AI Scribe (Voa Health) in Outpatient Clinics: Draft Notes, Documentation Burden, and Well-Being
Burnout, Professionals, Medical Records Systems, Computerized
Not yet recruitingObservational study
Ambient AI Documentation in Oncology Consultations
Neoplasms, Clinical Documentation
Active, not recruiting
Methodology of Super-Early Training of Developer Engineers and Leading Analysts With a Diagnosis of ADHD/ASD for Ensuring the Engineering Sovereignty of the State: Time Horizon: 2026-2038-2050
Attention Deficit Hyperactivity Disorder (ADHD)
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Questions about this trial
What is this clinical trial studying?
A study testing BlackFrame ambient audio-visual capture platform in people with Clinical Documentation and Artificial Intelligence, run by BlackFrame.ai.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 8 inclusion and 11 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 16 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact George Ryan (+447946196325, georgeryan448@msn.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Mean documentation time per encounter with versus without AI scribe assistance (time frame: Through study completion, approximately 12 weeks). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 16 Jun 2026.