A Large Language Model in Outpatient Care
2026년 6월 7일 업데이트: Tien Yin Wong, Tsinghua University
A Prospective Randomized Controlled Trial of a Large Language Model in Outpatient Care
The goal of this clinical trial is to learn how the use of a large language model (LLM) based tool affects outpatient clinical care in adult patients attending general hospital outpatient clinics. The main questions it aims to answer are:
Does the use of an LLM-based tool affect the efficiency of outpatient visits? Does the use of an LLM-based tool affect the experience of doctors and patients during outpatient care?
Researchers will compare outpatient visits supported by an LLM-based tool to standard outpatient visits without such a tool, to see whether and how the tool influences the care process and the experiences of doctors and patients.
Participants will:
Take part in outpatient visits that may or may not involve an LLM-based tool, depending on their assigned group Complete a short questionnaire about their visit experience after the consultation
연구 개요
상태
아직 모집하지 않음
정황
연구 유형
중재적
등록 (추정된)
3500
단계
- 해당 없음
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
아니
설명
Inclusion Criteria:
Doctors:
- Licensed physicians providing outpatient consultations at a participating study hospital
- Expected to complete a sufficient number of outpatient clinic sessions during the study period
- Provides written informed consent
Patients:
- Age 18 years or older
- Attending an outpatient consultation with a participating doctor
- Able to interact with the tool using an internet-connected device such as a smartphone
- Provides written informed consent
Exclusion Criteria:
Patients:
- Psychiatric conditions, unstable vital signs, or other medical situations considered unsuitable for AI-based interaction
- Declines to provide informed consent
공부 계획
이 섹션에서는 연구 설계 방법과 연구가 측정하는 내용을 포함하여 연구 계획에 대한 세부 정보를 제공합니다.
연구는 어떻게 설계됩니까?
디자인 세부사항
- 주 목적: 건강 서비스 연구
- 할당: 무작위
- 중재 모델: 크로스오버 할당
- 마스킹: 하나의
무기와 개입
참가자 그룹 / 팔 |
개입 / 치료 |
|---|---|
|
간섭 없음: Standard Outpatient Care (No AI)
Neither doctors nor patients use a large language model based tool.
Outpatient consultations and documentation are conducted following routine clinical practice.
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실험적: Outpatient Care With a Large Language Model Tool
Before the consultation, patients complete an AI-based pre-consultation interaction.
During the visit, a large language model based tool is available to support the outpatient consultation process.
Doctors may refer to the tool during the visit.
|
A large language model based tool is introduced into the outpatient consultation workflow to support the consultation and documentation process.
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실험적: Outpatient Care With a Large Language Model Tool and Workflow Support
Before the consultation, patients complete an AI-based pre-consultation interaction.
During the visit, a large language model based tool is used together with additional workflow support to integrate the tool's output into the outpatient consultation process.
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Additional workflow support is provided to integrate the output of the large language model based tool into the consultation process, approximating a more integrated deployment of the tool.
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연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
|
Duration of the Outpatient Consultation
기간: During the outpatient visit
|
Time of the outpatient consultation, measured in milliseconds
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During the outpatient visit
|
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Doctor-Reported Efficiency of the Consultation
기간: Immediately after the consultation
|
Doctor's self-rated efficiency of the consultation, measured on a 5-point Likert scale (1 = very low to 5 = very high), with higher scores indicating higher perceived efficiency.
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Immediately after the consultation
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Doctor-Reported Satisfaction With the Consultation Process
기간: Immediately after the consultation
|
Doctor's satisfaction with the consultation process, measured on a 5-point Likert scale (1 = very low to 5 = very high), with higher scores indicating higher satisfaction.
|
Immediately after the consultation
|
2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
|
Doctor-Reported Efficiency of Obtaining Patient Information
기간: Immediately after the consultation
|
Doctor's self-rated efficiency in obtaining the patient's clinical information (such as symptoms, history, prior examinations) during the consultation, measured on a 5-point Likert scale (1 = very low to 5 = very high); higher scores indicate higher efficiency.
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Immediately after the consultation
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Doctor-Reported Cognitive Effort in Clinical Decision-Making
기간: Immediately after the consultation
|
Doctor's self-rated cognitive effort invested in clinical decision-making during the consultation, measured on a 5-point Likert scale (1 = very low to 5 = very high); higher scores indicate greater effort.
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Immediately after the consultation
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Doctor-Reported Burden of Clinical Documentation
기간: Immediately after the consultation
|
Doctor's self-rated burden of completing the outpatient medical record for the consultation, measured on a 5-point Likert scale (1 = very low to 5 = very high); higher scores indicate greater burden.
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Immediately after the consultation
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Doctor's Intention to Continue Using the Tool
기간: Within 1 week after the participating doctor completes all enrolled consultations
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Doctor's intention to continue using the large language model based tool in routine practice, measured on a 5-point Likert scale (1 = strongly unwilling to 5 = strongly willing); higher scores indicate stronger intention.
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Within 1 week after the participating doctor completes all enrolled consultations
|
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Patient Trust in the Physician
기간: Immediately after the consultation
|
Patient's level of trust in the physician after the visit, measured on a 5-point Likert scale (1 = very low to 5 = very high); higher scores indicate greater trust.
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Immediately after the consultation
|
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Patient Satisfaction With the Visit
기간: Immediately after the consultation
|
Patient's satisfaction with the visit, measured on a 5-point Likert scale (1 = very low to 5 = very high); higher scores indicate greater satisfaction.
|
Immediately after the consultation
|
|
Patient-Perceived Physician Attentiveness
기간: Immediately after the consultation
|
Patient-perceived attentiveness of the physician during the visit, assessed by a multi-item measure and reported as a composite score on a 1-5 scale; higher scores indicate greater perceived attentiveness.
|
Immediately after the consultation
|
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Patient Satisfaction With the AI Pre-Consultation (Arm 2 and Arm 3 )
기간: Immediately after the consultation
|
Patient's satisfaction with the AI-based pre-consultation interaction, measured on a 5-point Likert scale (1 = very low to 5 = very high); higher scores indicate greater satisfaction.
Assessed only in Arm 2 and Arm 3.
|
Immediately after the consultation
|
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Patient's Intention to Use AI Pre-Consultation in the Future (Arm 2 and Arm 3)
기간: Immediately after the consultation
|
Patient's intention to use AI-based pre-consultation again in the future, measured on a 5-point Likert scale (1 = strongly unwilling to 5 = strongly willing); higher scores indicate stronger intention.
Assessed only in Arm 2 and Arm 3.
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Immediately after the consultation
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공동 작업자 및 조사자
여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.
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 6월 12일
기본 완료 (추정된)
2026년 9월 4일
연구 완료 (추정된)
2026년 9월 4일
연구 등록 날짜
최초 제출
2026년 5월 29일
QC 기준을 충족하는 최초 제출
2026년 6월 7일
처음 게시됨 (실제)
2026년 6월 11일
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
2026년 6월 11일
QC 기준을 충족하는 마지막 업데이트 제출
2026년 6월 7일
마지막으로 확인됨
2026년 6월 1일
추가 정보
이 연구와 관련된 용어
기타 연구 ID 번호
- THU-01-2026-0055
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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. .