Massive Online Open Validation and Evaluation of Large Language Models for Resource Constrained Settings - a Study of Contextual Awareness, Safety, and Accuracy (SilentMOOVE)
Healthcare settings in low and middle income countries, clinicians are frequently required to manage a wide range of acute conditions under the constraints of time pressure, limited diagnostic resources, and variable availability of up-to-date clinical guidelines. Large language models (LLMs), which can synthesize and generate medical information in a natural language, have been proposed as potential clinical decision support tools. Most evaluations of LLMs in medicine have relied on standardized benchmark examinations or hypothetical case vignettes, often derived from high-income country contexts. MOOVE (Massive online open validation and evaluation) framework was developed to address the need for rigorous and context-sensitive evaluation of LLMs through a staged approach.
Study is a non-interventional, prospective study designed to test LLMs safely in real clinical settings, evaluate their performance against human care, and explore how feasible their future implementation in healthcare might be.
연구 개요
상태
상태
정황
정황
개입 / 치료
개입 / 치료
상세 설명
SilentMOOVE (Silent massive online open validation and evaluation) phase of the MOOVE project, which represents the intermediate step between previous hypothetical testing study and future interventional trial. In silentMOOVE, patients are prospectively recruited during routine clinical care in participating outpatient health facilities. Clinical and demographic data collected during consultations are used to prompt LLMs retrospectively without influencing patient care. The routine diagnosis and care plan of the treating clinicians (human care plan) are also captured. LLM outputs are subsequently evaluated by a panel of independent clinical experts against the human care plan as a comparator. Patient clinical outcomes are collected on 7 and 28 days after enrolment to establish a baseline and to be used in the evaluation of LLMs.
The rationale for this study is to generate robust, prospective evidence on the safety and performance of LLMs in real-world clinical contexts, while minimizing risk to participants by fully separating model evaluation from clinical decision-making.
Study also provides the opportunity to assess the usability and gather early insights on implementation readiness of the LLM prompting interface. For this reason, besides evaluating the performance and safety of various LLMs, this study includes an implementation feasibility component. These activities are needed to better understand the context and any amelioration needed before future implementation in healthcare settings. Learnings from the implementation feasibility research will be shared among countries, especially applicable within a given use case. Implementation feasibility research will be grounded in artificial intelligence in healthcare implementation frameworks and other Implementation science frameworks Primary objectives
- To evaluate the overall performance profile of LLMs on real-world clinical scenarios using the human care plan as a comparator
- To assess whether each LLM being evaluated is not inferior to the human comparator with respect to safety on real-world clinical scenarios, based on structured retrospective assessment of model- and human-generated care plans.
Implementation feasibility research objectives
- Assess clinician experience with the LLM prompting interface: ease of use, time burden, and alignment with clinical workflow.
- Gather clinician impressions of LLM output disclosed and reviewed post-consultation: clarity, relevance, perceived safety, alignment with local clinical guidelines, and trust in LLM outputs.
- Facilitators and Barriers to Future Implementation: Identify site-level infrastructure, staff capacity, and organizational readiness factors that may support or hinder future implementation of LLM feedback in real-time
연구 유형
연구 유형
등록 (추정된)
등록
연락처 및 위치
연구 연락처
연구 연락처
- 이름: Mary-Anne Hartley
- 전화번호: +41216930828
- 이메일: mary-anne.hartley@epfl.ch
연구 연락처 백업
- 이름: Kristina Keitel
- 전화번호: +41216932039
- 이메일: Kristina.keitel@epfl.ch
연구 장소
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Blantyre
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Blantyre, Blantyre, 말라위, 303
- 모병
- Malawi University of Business and Applied Sciences (MUBAS)
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연락하다:
- Chiyembekezo Kachimanga
- 전화번호: +265 887247777
- 이메일: chembekachimanga@yahoo.co.uk
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수석 연구원:
- Chiyembekezo Kachimanga
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Nairobi County
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Nairobi, Nairobi County, 케냐, 19676
- 아직 모집하지 않음
- University of Nairobi College of health sciences
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연락하다:
- Paul Macharia
- 이메일: pmachariah@gmail.com
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수석 연구원:
- Paul Macharia
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Dar es Salaam Region
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Dar es Salaam, Dar es Salaam Region, 탄자니아
- 아직 모집하지 않음
- Muhimbili University of Health and Allied Sciences (MUHAS)
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연락하다:
- Hendry R Sawe
- 전화번호: +255754885658
- 이메일: hendry_sawe@yahoo.com
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수석 연구원:
- Hendry R Sawe
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Dodoma
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Dodoma, Dodoma, 탄자니아
- 모병
- Ifakara Health Institute (IHI)
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연락하다:
- Grace Mhalu
- 전화번호: +255655888891
- 이메일: gmhalu@ihi.or.tz
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수석 연구원:
- Grace Mhalu
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참여기준
자격 기준
자격 기준
공부할 수 있는 나이
건강한 자원 봉사자를 받아들입니다
연구 대상 성별
샘플링 방법
연구 인구
The study population consists of patients of any age presenting to participating health facilities for a distinct care-seeking episode related to a clinical condition within the defined use case (e.g. primary care, emergency care).
Both adult and pediatric patients are eligible, reflecting the real-world case mix of the participating facilities.
설명
Inclusion Criteria:
- Presentation at a participating facility for a distinct care seeking episode related to a clinical condition.
- Use case specific inclusion criteria
- Willing and able to provide written informed consent.
Exclusion Criteria:
- Prolonged care-seeking episode.
- Previously enrolled in the same study.
- Use case specific exclusion criteria
- Immediate life-threatening condition requiring emergency intervention precluding study procedures.
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
- 관찰 모델: COHORT
- 시간 관점: PROSPECTIVE
그룹/코호트 수
코호트 및 개입
그룹/코호트그룹/코호트 |
개입 / 치료개입 / 치료 |
|---|---|
|
Non interventional study
Non interventional study |
Non interventional study |
연구는 무엇을 측정합니까?
주요 결과 측정
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
|
Performance Scores
기간: Day 0
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Scoring is between -2 to +2 where '-2' means minimum, and '-2' means maximum. Score to be >0. Higher score means better outcome. |
Day 0
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Safety performance threshold achievement
기간: 'Day 7' and 'Day 28'
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The mean safety score (minimum is -2 and maximum is +2) is formally compared between each LLM and the human comparator to assess non-inferiority within a UCA (use case appendix) dependent pre-specified margin. |
'Day 7' and 'Day 28'
|
2차 결과 측정
2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
|
Implementation Feasibility Research
기간: week 4 until study completion
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Usability: Quantitative scoring rubric for clinicians; semi-structured interviews with clinicians; evaluation methods will be used |
week 4 until study completion
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Implementation Feasibility Research
기간: week 4 until study completion
|
Workflow integration feasibility: This will be evaluated using semi structured interviews and questionnaires |
week 4 until study completion
|
|
Implementation Feasibility Research
기간: week 4 until study completion
|
Acceptability and perceived trust: This will be evaluated with Quantitative scoring rubric for clinicians; semi-structured interviews with clinicians |
week 4 until study completion
|
|
Implementation Feasibility Research
기간: week 4 until study completion
|
Implementation facilitators and barriers: This will be evaluated using semi-structured interviews and questionnaires |
week 4 until study completion
|
공동 작업자 및 조사자
협력자
협력자
수사관
수사관
- 수석 연구원: Mary-Anne Hartley, LIGHT
간행물 및 유용한 링크
일반 간행물
- 1. Arora RK, Wei J, Hicks RS, Bowman P, et al. HealthBench: Evaluating Large Language Models Towards Improved Human Health. Preprint [Under Review]. 2025. 2. Burnell R, Schellaert W, Burden J, Ullman TD, Martinez-Plumed F, Tenenbaum JB, et al. Rethink reporting of evaluation results in AI. Science. 2023;380: 136-138. doi:10.1126/SCIENCE.ADF6369. 3. Hager P, Jungmann F, Holland R, Bhagat K, Hubrecht I, Knauer M, et al. Evaluation and mitigation of the limitations of large language models in clinical decision-making. Nature Medicine 2024 30:9. 2024;30: 2613-2622. doi:10.1038/s41591-024-03097-1. 4. Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., et al. (2023). Large language models encode clinical knowledge. Nature, 620(7972), 172-180. https://doi.org/10.1038/s41586-023-06291-2 5. WHO. Towards the Development of guidance on ethics and governance of artificial intelligence for health. 2019. 6. Azad TD, Krumholz HM, Saria S. Principles to guide clinical AI readiness and move from benchmarks to real-world evaluation. Nature Medicine. 2026;32:802-804. doi:10.1038/s41591-025-04198-1 7. Declaration of Helsinki https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects 8. European Parliament and Council of the European Union, 2016. Regulation (EU) 2016/679 (General Data Protection Regulation). Official Journal of the European Union, L119, pp.1-88. 9. International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH), 2016. Integrated Addendum to ICH E6(R1): Guideline for Good Clinical Practice E6(R2). Step 4, 9 November 2016. 10. U.S. Food and Drug Administration. 21 CFR Part 11-Electronic Records; Electronic Signatures. Code of Federal Regulations. Title 21, Part 11. Revised 2024.
연구 기록 날짜
연구 주요 날짜
연구 시작 (실제)
연구 시작
기본 완료 (추정된)
기본 완료
연구 완료 (추정된)
연구 완료
연구 등록 날짜
최초 제출
최초 제출
QC 기준을 충족하는 최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
처음 게시됨
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
마지막 업데이트 게시됨
QC 기준을 충족하는 마지막 업데이트 제출
QC 기준을 충족하는 마지막 업데이트 제출
마지막으로 확인됨
마지막으로 확인됨
추가 정보
이 연구와 관련된 용어
키워드
기타 연구 ID 번호
기타 연구 ID 번호
- LiGHT-26-02
- INV-076674 (기타 보조금/기금 번호: BILL and Melinda gates foundation)
개별 참가자 데이터(IPD) 계획
개별 참가자 데이터(IPD)를 공유할 계획입니까?
IPD 계획 설명
We will share IPD within 12 months of study completion.
IPD 공유 기간
IPD information will be shared within 12 months of study completion.
IPD 공유 액세스 기준
Will be confirmed within 12 months of study completion
IPD 공유 지원 정보 유형
- 연구_프로토콜
약물 및 장치 정보, 연구 문서
미국 FDA 규제 의약품 연구
미국 FDA 규제 기기 제품 연구
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