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.
調査の概要
詳細な説明
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
研究の種類
入学 (推定)
段階
- 適用できない
連絡先と場所
研究連絡先
- 名前：George Ryan
- 電話番号：+447946196325
- メール：georgeryan448@msn.com
参加基準
適格基準
就学可能な年齢
- 子
- 大人
- 高齢者
健康ボランティアの受け入れ
説明
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
研究計画
研究はどのように設計されていますか？
デザインの詳細
- 主な目的：他の
- 割り当て：非ランダム化
- 介入モデル：単一グループの割り当て
- マスキング：なし（オープンラベル）
武器と介入
参加者グループ / アーム |
介入・治療 |
|---|---|
|
実験的：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.
|
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.
|
|
実験的：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.
|
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.
|
この研究は何を測定していますか？
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Mean documentation time per encounter with versus without AI scribe assistance
時間枠：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.
|
Through study completion, approximately 12 weeks
|
二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Cohen's kappa between AI-generated and expert human assessment scores per checklist domain
時間枠：Through study completion, approximately 12 weeks
|
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
時間枠：After first study encounter, approximately within 1 week of study enrolment
|
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
時間枠：Through study completion, approximately 12 weeks
|
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
|
協力者と研究者
研究記録日
主要日程の研究
研究開始 (推定)
一次修了 (推定)
研究の完了 (推定)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
キーワード
その他の研究ID番号
- BF-AV-2026-01
個々の参加者データ (IPD) の計画
個々の参加者データ (IPD) を共有する予定はありますか?
IPD プランの説明
医薬品およびデバイス情報、研究文書
米国FDA規制医薬品の研究
米国FDA規制機器製品の研究
この情報は、Web サイト clinicaltrials.gov から変更なしで直接取得したものです。研究の詳細を変更、削除、または更新するリクエストがある場合は、register@clinicaltrials.gov。 までご連絡ください。 clinicaltrials.gov に変更が加えられるとすぐに、ウェブサイトでも自動的に更新されます。