Acute ConditionsSilentMOOVENCT07914140
Massive Online Open Validation and Evaluation of Large Language Models for Resource Constrained Settings - a Study of Contextual Awareness, Safety, and Accuracy
An observational study of Acute Conditions, run by Ecole Polytechnique Fédérale de Lausanne at 4 sites in 3 countries.
Trial summary
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. Read more about this study
- Who is it for?
- Ages Up to 1 day
- Conditions the study is for
- Acute Conditions
- What is being tested?
- Non interventional study
- How many people
- 3,000 participants
- Where
- 4 sitesin 3 countries
- Study dates
- 31 Aug 2026to 31 Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Up to 1 day
- All sexes
- No healthy volunteers
- Child
Study population: 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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
4 sites in 3 countries. Contact the study team or a site near you to ask about taking part.
4 sites in 3 countries
Tanzania2 sites, 2 recruiting
- Not yet recruiting
Muhimbili University of Health and Allied Sciences (MUHAS)
Dar es Salaam, Dar es Salaam Region
Hendry R Sawe, +255754885658, hendry_sawe@yahoo.com
Hendry R Sawe
- Recruiting
Ifakara Health Institute (IHI)
Dodoma, Dodoma
Grace Mhalu, +255655888891, gmhalu@ihi.or.tz
Grace Mhalu
Kenya1 site, 1 recruiting
- Not yet recruiting
University of Nairobi College of health sciences
Nairobi, Nairobi County, 19676
Paul Macharia, pmachariah@gmail.com
Paul Macharia
Malawi1 site, 1 recruiting
- Recruiting
Malawi University of Business and Applied Sciences (MUBAS)
Blantyre, Blantyre, 303
Chiyembekezo Kachimanga, +265 887247777, chembekachimanga@yahoo.co.uk
Chiyembekezo Kachimanga
About this study
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.
Read the detailed description
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
- Conditions
- Acute Conditions
- Being tested
- Non interventional study
- Keywords
- Acute conditions
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Non interventional studyOther
Non interventional study
| Study group | Role | What they receive |
|---|---|---|
| Non interventional study | Non interventional study Receives: Other: Non interventional study |
What is being measured
The primary outcome is the main question the study is designed to answer.
Performance Scores
Scoring is between -2 to +2 where '-2' means minimum, and '-2' means maximum. Score to be >0. Higher score means better outcome.
Measured: Day 0
Safety performance threshold achievement
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.
Measured: 'Day 7' and 'Day 28'
Secondary outcomes (4)
Implementation Feasibility Research
Usability: Quantitative scoring rubric for clinicians; semi-structured interviews with clinicians; evaluation methods will be used
Measured: week 4 until study completion
Implementation Feasibility Research
Workflow integration feasibility: This will be evaluated using semi structured interviews and questionnaires
Measured: week 4 until study completion
Implementation Feasibility Research
Acceptability and perceived trust: This will be evaluated with Quantitative scoring rubric for clinicians; semi-structured interviews with clinicians
Measured: week 4 until study completion
Implementation Feasibility Research
Implementation facilitators and barriers: This will be evaluated using semi-structured interviews and questionnaires
Measured: week 4 until study completion
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Nov 2026
- Study start31 Aug 2026
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Acute ConditionsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 3,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Ecole Polytechnique Fédérale de Lausanne
- Sponsor type
- University, hospital or other
- Collaborators
- Ifakara Health Institute, Muhimbili University of Health and Allied Sciences, Malawi University of Business and Applied Sciences (MUBAS) WASHTED Centre, University of Nairobi, Kenya
- Responsible party
- Principal investigator, Rita Szekely, Ecole Polytechnique Fédérale de Lausanne
- Investigators
- Mary-Anne Hartley, Principal investigator, LIGHT
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07914140
- Sponsor's protocol ID
- LiGHT-26-02
- Other IDs
- INV-076674
- Acronym
- SilentMOOVE
- Status last verified
- Oct 2026
- First submitted
- 9 Sep 2026
- Last updated
- 3 Nov 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: We will share IPD within 12 months of study completion.
Publications
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.
Study at a glance
From the registry record, last updated by the sponsor on 3 Nov 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Massive Online Open Validation and Evaluation of Large Language Models for Resource Constrained Settings - a Study of Contextual Awareness, Safety, and Accuracy
- ClinicalTrials.gov ID
- NCT07914140
- Acronym / sponsor ID
- SilentMOOVE / LiGHT-26-02
- Conditions
- Acute Conditions
- Keywords
- Acute conditions
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 3,000 participants (estimated)
- Lead sponsor
- Ecole Polytechnique Fédérale de LausanneUniversity, hospital or other
- Collaborators
- Ifakara Health Institute, Muhimbili University of Health and Allied Sciences, Malawi University of Business and Applied Sciences (MUBAS) WASHTED Centre, University of Nairobi, Kenya
- Study start
- 31 Aug 2026 (actual)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 3 Nov 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Non interventional study | – | Other: Non interventional studyNon interventional study |
Who may qualify
- Ages
- Up to 1 day
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child
- Study population
- 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.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Non interventional studyNon interventional study | Other | Non interventional study |
Primary outcome
Performance Scores
Time frame: Day 0
Scoring is between -2 to +2 where '-2' means minimum, and '-2' means maximum. Score to be >0. Higher score means better outcome.
Safety performance threshold achievement
Time frame: 'Day 7' and 'Day 28'
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.
4 secondary outcomes
Implementation Feasibility Research
Time frame: week 4 until study completion
Implementation Feasibility Research
Time frame: week 4 until study completion
Implementation Feasibility Research
Time frame: week 4 until study completion
Implementation Feasibility Research
Time frame: week 4 until study completion
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Nov 2026.
- Background1. 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.
- Individual participant dataSharing planned: Yes. We will share IPD within 12 months of study completion.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Mary-Anne Hartley principal investigator
LIGHT
4 sites in 3 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Acute Conditions, run by Ecole Polytechnique Fédérale de Lausanne at 4 sites in 3 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 3 Nov 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 4 sites in Tanzania, Kenya, Malawi. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Mary-Anne Hartley (+41216930828, mary-anne.hartley@epfl.ch), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Performance Scores (time frame: Day 0). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07914140, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 3 Nov 2026.