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

2026년 6월 10일 업데이트: BlackFrame.ai

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.

연구 개요

상태

아직 모집하지 않음

정황

개입 / 치료

상세 설명

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

연구 유형

중재적

등록 (추정된)

60

단계

  • 해당 없음

연락처 및 위치

이 섹션에서는 연구를 수행하는 사람들의 연락처 정보와 이 연구가 수행되는 장소에 대한 정보를 제공합니다.

연구 연락처

참여기준

연구원은 적격성 기준이라는 특정 설명에 맞는 사람을 찾습니다. 이러한 기준의 몇 가지 예는 개인의 일반적인 건강 상태 또는 이전 치료입니다.

자격 기준

공부할 수 있는 나이

  • 어린이
  • 성인
  • 고령자

건강한 자원 봉사자를 받아들입니다

예

설명

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

공부 계획

이 섹션에서는 연구 설계 방법과 연구가 측정하는 내용을 포함하여 연구 계획에 대한 세부 정보를 제공합니다.

연구는 어떻게 설계됩니까?

디자인 세부사항

  • 주 목적: 다른
  • 할당: 무작위화되지 않음
  • 중재 모델: 단일 그룹 할당
  • 마스킹: 없음(오픈 라벨)

무기와 개입

참가자 그룹 / 팔
개입 / 치료
실험적: 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.
장치: 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.
실험적: 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.
장치: 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.

연구는 무엇을 측정합니까?

주요 결과 측정

결과 측정
측정값 설명
기간
Mean documentation time per encounter with versus without AI scribe assistance
기간: 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

2차 결과 측정

결과 측정
측정값 설명
기간
Cohen's kappa between AI-generated and expert human assessment scores per checklist domain
기간: 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
기간: 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
기간: 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

공동 작업자 및 조사자

여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.

스폰서

BlackFrame.ai

협력자

The Cleveland Clinic
Somerset NHS Foundation Trust

연구 기록 날짜

이 날짜는 ClinicalTrials.gov에 대한 연구 기록 및 요약 결과 제출의 진행 상황을 추적합니다. 연구 기록 및 보고된 결과는 공개 웹사이트에 게시되기 전에 특정 품질 관리 기준을 충족하는지 확인하기 위해 국립 의학 도서관(NLM)에서 검토합니다.

연구 주요 날짜

연구 시작 (추정된)

2026년 9월 1일

기본 완료 (추정된)

2026년 11월 30일

연구 완료 (추정된)

2026년 11월 30일

연구 등록 날짜

최초 제출

2026년 6월 1일

QC 기준을 충족하는 최초 제출

2026년 6월 10일

처음 게시됨 (실제)

2026년 6월 16일

연구 기록 업데이트

마지막 업데이트 게시됨 (실제)

2026년 6월 16일

QC 기준을 충족하는 마지막 업데이트 제출

2026년 6월 10일

마지막으로 확인됨

2026년 6월 1일

추가 정보

이 연구와 관련된 용어

키워드

기타 연구 ID 번호

  • BF-AV-2026-01

개별 참가자 데이터(IPD) 계획

개별 참가자 데이터(IPD)를 공유할 계획입니까?

예

IPD 계획 설명

As per protocol

약물 및 장치 정보, 연구 문서

미국 FDA 규제 의약품 연구

아니

미국 FDA 규제 기기 제품 연구

아니

이 정보는 변경 없이 clinicaltrials.gov 웹사이트에서 직접 가져온 것입니다. 귀하의 연구 세부 정보를 변경, 제거 또는 업데이트하도록 요청하는 경우 register@clinicaltrials.gov. 문의하십시오. 변경 사항이 clinicaltrials.gov에 구현되는 즉시 저희 웹사이트에도 자동으로 업데이트됩니다. .

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정황

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약물 개입

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스폰서 / 협력자

위치

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