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.
Visão geral do estudo
Status
Condições
Intervenção / Tratamento
Descrição detalhada
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
Tipo de estudo
Inscrição (Estimado)
Contactos e Locais
Contato de estudo
- Nome: Mary-Anne Hartley
- Número de telefone: +41216930828
- E-mail: mary-anne.hartley@epfl.ch
Estude backup de contato
- Nome: Kristina Keitel
- Número de telefone: +41216932039
- E-mail: Kristina.keitel@epfl.ch
Locais de estudo
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Blantyre
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Blantyre, Blantyre, Malauí, 303
- Recrutamento
- Malawi University of Business and Applied Sciences (MUBAS)
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Contato:
- Chiyembekezo Kachimanga
- Número de telefone: +265 887247777
- E-mail: chembekachimanga@yahoo.co.uk
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Investigador principal:
- Chiyembekezo Kachimanga
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Nairobi County
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Nairobi, Nairobi County, Quênia, 19676
- Ainda não está recrutando
- University of Nairobi College of health sciences
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Contato:
- Paul Macharia
- E-mail: pmachariah@gmail.com
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Investigador principal:
- Paul Macharia
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Dar es Salaam Region
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Dar es Salaam, Dar es Salaam Region, Tanzânia
- Ainda não está recrutando
- Muhimbili University of Health and Allied Sciences (MUHAS)
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Contato:
- Hendry R Sawe
- Número de telefone: +255754885658
- E-mail: hendry_sawe@yahoo.com
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Investigador principal:
- Hendry R Sawe
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Dodoma
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Dodoma, Dodoma, Tanzânia
- Recrutamento
- Ifakara Health Institute (IHI)
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Contato:
- Grace Mhalu
- Número de telefone: +255655888891
- E-mail: gmhalu@ihi.or.tz
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Investigador principal:
- Grace Mhalu
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Critérios de participação
Critérios de elegibilidade
Idades elegíveis para estudo
Aceita Voluntários Saudáveis
Gêneros Elegíveis para o Estudo
Método de amostragem
População do estudo
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.
Descrição
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.
Plano de estudo
Como o estudo é projetado?
Detalhes do projeto
- Modelos de observação: COHORT
- Perspectivas de Tempo: PROSPECTIVE
Coortes e Intervenções
Grupo / Coorte |
Intervenção / Tratamento |
|---|---|
|
Non interventional study
Non interventional study |
Non interventional study |
O que o estudo está medindo?
Medidas de resultados primários
Medida de resultado |
Descrição da medida |
Prazo |
|---|---|---|
|
Performance Scores
Prazo: 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
Prazo: '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'
|
Medidas de resultados secundários
Medida de resultado |
Descrição da medida |
Prazo |
|---|---|---|
|
Implementation Feasibility Research
Prazo: 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
Prazo: week 4 until study completion
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Workflow integration feasibility: This will be evaluated using semi structured interviews and questionnaires |
week 4 until study completion
|
|
Implementation Feasibility Research
Prazo: 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
Prazo: week 4 until study completion
|
Implementation facilitators and barriers: This will be evaluated using semi-structured interviews and questionnaires |
week 4 until study completion
|
Colaboradores e Investigadores
Patrocinador
Colaboradores
Investigadores
- Investigador principal: Mary-Anne Hartley, LIGHT
Publicações e links úteis
Publicações Gerais
- 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.
Datas de registro do estudo
Datas Principais do Estudo
Início do estudo (Real)
Conclusão Primária (Estimado)
Conclusão do estudo (Estimado)
Datas de inscrição no estudo
Enviado pela primeira vez
Enviado pela primeira vez que atendeu aos critérios de CQ
Primeira postagem (Real)
Atualizações de registro de estudo
Última Atualização Postada (Real)
Última atualização enviada que atendeu aos critérios de controle de qualidade
Última verificação
Mais Informações
Termos relacionados a este estudo
Palavras-chave
Outros números de identificação do estudo
- LiGHT-26-02
- INV-076674 (Número de outro subsídio/financiamento: BILL and Melinda gates foundation)
Plano para dados de participantes individuais (IPD)
Planeja compartilhar dados de participantes individuais (IPD)?
Descrição do plano IPD
We will share IPD within 12 months of study completion.
Prazo de Compartilhamento de IPD
IPD information will be shared within 12 months of study completion.
Critérios de acesso de compartilhamento IPD
Will be confirmed within 12 months of study completion
Tipo de informação de suporte de compartilhamento de IPD
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