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Evaluating the Effectiveness of an AI-Powered Physician Assistant in Improving Patients' and Physicians' Satisfaction in Anaesthesiology Clinics

2026년 7월 31일 업데이트: Dileep Kumar, Aga Khan University

Evaluating the Effectiveness of an AI-Powered Physician Assistant in Improving Patients' and Physicians' Satisfaction in Anaesthesiology Clinics of a Tertiary Care Hospital

This trial aimed to evaluate the effectiveness of an AI-Powered Physician Assistant in improving patients' satisfaction with the quality of care. It also aims to evaluate physicians' satisfaction with the integration of a Physician Assistant into their clinical workflows. The primary research question is

A. What is the effect of an AI-powered Physician Assistant on patients' satisfaction with their quality of care compared to the standard care?

The secondary questions are as follows:

B1. How satisfied are physicians with integrating an AI-powered physician assistant into their daily clinical workflows?

B2. Which factors are significantly associated with patient satisfaction regarding the AI-Powered Physician Assistant?

B3. Is there a statistically significant difference in mean consultation time per patient between those receiving AI-assisted care and those receiving only standard of careonly ?

Participants will be enrolled from pre-operative outpatient clinics, including patients attending clinic for anaesthetic clearance prior to surgery and consultant anaesthetists providing care.

Patients will serve as the unit of randomization and will be assigned to one of two study arms on each clinic day. On each clinic day, the first 8 eligible patients presenting for consultation will be randomly assigned to the intervention group or the control group in a 1:1 ratio. Intervention patients will proceed to a dedicated waiting room for structured digital intake via an AI platform (demographics, symptoms, history, clinical data) and receive AI-assisted care. Control group patients will undergo routine standard care protocols. The consultant physician will evaluate both arms during each clinic session, reviewing physician assistant-generated patient summaries and charts for patients in both the intervention and control groups.

Each patient will be asked to fill out the survey at the end of the consultation with the physician. The consultants will be requested to fill out a survey at the end of the day.

연구 개요

연구 유형

중재적

등록 (추정된)

180

단계

  • 해당 없음

연락처 및 위치

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

연구 연락처

연구 연락처 백업

연구 장소

    • Sindh
      • Karachi, Sindh, 파키스탄, 74800
        • Aga Khan University Hospital
        • 연락하다:
        • 연락하다:
        • 수석 연구원:
          • Dileep Kumar

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

  • 성인
  • 고령자

건강한 자원 봉사자를 받아들입니다

예

설명

Patients and Patients' Proxies

Inclusion Criteria:

  • Consent to participate
  • Age 18 years and above
  • Initial patients
  • Possession of a phone
  • Read and write Urdu and/or English

Exclusion Criteria:

  • Emergency Care patients
  • Follow-up patients

Physicians:

Inclusion Criteria

  • Attending or Consultant Physician
  • Consent to participate
  • Workflow integrated with AI Assistant

Exclusion Criteria

- Residents and Senior Medical Officers

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: 무작위
  • 중재 모델: 병렬 할당
  • 마스킹: 하나의

무기와 개입

참가자 그룹 / 팔
개입 / 치료
실험적: AI-Assisted Care
The patients in this arm will receive AI-assisted care in addition to the standard care. The AI platform will take the patient's history, and then the patient will talk to the consultant as part of the routine flow.
The study participant allocated to the intervention arm will interact with the AI-physician assistant application before they consult with the physician. The application will collect medical history of the patient. This will then be followed by an AI-generated clinical summary, which their physicians will receive before the consultation begins. Physicians will review this summary and ask further questions of patients if required. Any additions and changes in the patient's history will also be made. Physicians will subsequently conduct a physical examination of the patient. After this, the physician will be able to view AI- and guideline-based suggestions for the patient's assessment and pre-operative management. The recommendations can be selected, modified, or not used as per the physician's expertise. All additions within the application can be either typed manually or verbalised via an AI-assisted scribe within the application.
간섭 없음: Standard Care
The patients in this arm will receive standard care. The standard of care for our study is what is usually applied in Anaesthesiology outpatient clinics, with consultants seeing the patients after the resident.

연구는 무엇을 측정합니까?

주요 결과 측정

결과 측정
측정값 설명
기간
Patient Satisfaction and Outpatient Experience Score (Adapted NHS Outpatient Survey - Patient Questionnaire)
기간: From enrollment to the end of the study, for each patient, for 8 weeks

Patient satisfaction will be evaluated across two domains: (1) effective utilization of waiting time and (2) receipt of patient-centered care during the outpatient visit.

Evaluated post-consultation using a structured survey adapted from the NHS Outpatient Survey: Section A (Wait Time): Wait duration (A1) and wait time utility (A2). Section B (Doctor Interaction): Patient-centered care, consultation duration, listening, and clarity (B2-B7). Section C (AI Assistant - Intervention Arm Only): Perceived listening, trust, and privacy (C1-C6). Section D (Overall Impression): Respect, dignity, and care- quality (D1-D3).

Ordinal items are assigned numerical scores to calculate a composite mean score and domain sub-scores (range: 1.0-5.0). Higher scores indicate greater satisfaction.

From enrollment to the end of the study, for each patient, for 8 weeks

2차 결과 측정

결과 측정
측정값 설명
기간
Daily Physician Satisfaction and Workflow Efficiency Score (End-of-Day Physician Survey)
기간: From enrollment to the end of the study, for each physician, for 8 weeks

Physician satisfaction and workflow efficiency will be evaluated using an end-of-day survey administered to physicians whose patients were recruited into the study. Evaluated using a two-part structured questionnaire:

Section 1 (Comparative Workflow): Binary comparisons across 5 domains (consultation duration, patient engagement, documentation efficiency, cognitive workload, and focus on patient care), with 3-point follow-up sub-questions for favorable intervention responses.

Section 2 (Intervention Utility): 6 intervention-specific items assessing accuracy/trust, workflow efficiency, workload relief, and overall satisfaction (5-point ordinal scales; 1 = Strongly Disagree, 5 = Strongly Agree).

Daily composite mean scores are calculated from Sections 1 and 2 (range: 1.0-5.0). Higher scores indicate greater satisfaction.

From enrollment to the end of the study, for each physician, for 8 weeks
Total Physician Consultation Duration (Stopwatch Timestamps)
기간: From enrollment to the end of the study, for each patient, for 8 weeks

Evaluates total physician consultation duration (in minutes) for each patient during their outpatient clinic encounter.

Consultation time refers to the active time spent by the physician while the patient is inside the consultation room. It encompasses the time taken to inquire about symptoms, conduct physical examinations, prescribe treatments, and provide counseling.

Duration is objectively measured in minutes using manual stopwatch timestamps, initiated the moment the patient enters the consultation room and concluded when the patient departs.

Mean total consultation times will be calculated per patient and compared between the intervention and standard care arms. Lower or more optimized consultation durations reflect enhanced workflow efficiency.

From enrollment to the end of the study, for each patient, for 8 weeks

기타 결과 측정

결과 측정
측정값 설명
기간
Process flow evaluation outcome - Mean Queuing Time (Timestamp Tracking)
기간: From enrollment to the end of the study, for each patient, for 8 weeks

Recorded using objective timestamps across two sequential intervals:

Interval 1: Completion of counter registration to initiation of vital signs measurement.

Interval 2: Completion of vital signs measurement to patient entry into the physician consultation room.

Intervals are combined into a single aggregated total queuing time per patient visit. Lower mean queuing times indicate improved operational workflow efficiency.

From enrollment to the end of the study, for each patient, for 8 weeks

공동 작업자 및 조사자

여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.

수사관

  • 수석 연구원: Dileep Kumar, Aga Khan University

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 8월 30일

기본 완료 (추정된)

2026년 10월 30일

연구 완료 (추정된)

2026년 10월 30일

연구 등록 날짜

최초 제출

2026년 7월 28일

QC 기준을 충족하는 최초 제출

2026년 7월 31일

처음 게시됨 (실제)

2026년 8월 3일

연구 기록 업데이트

마지막 업데이트 게시됨 (실제)

2026년 8월 3일

QC 기준을 충족하는 마지막 업데이트 제출

2026년 7월 31일

마지막으로 확인됨

2026년 7월 1일

추가 정보

이 연구와 관련된 용어

개별 참가자 데이터(IPD) 계획

개별 참가자 데이터(IPD)를 공유할 계획입니까?

예

IPD 계획 설명

The plan is to publish the study, and informed consent has been obtained from each participant. Each participant's data will be de-identified at the source level for both the application and survey.

IPD 공유 액세스 기준

Through Publication Only

IPD 공유 지원 정보 유형

  • 수액
  • CSR

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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