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.

Study Overview

• Status: Recruiting
• Conditions: Acute Conditions
• Intervention / Treatment: Other: Non interventional study

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

1. To evaluate the overall performance profile of LLMs on real-world clinical scenarios using the human care plan as a comparator
2. 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

1. Assess clinician experience with the LLM prompting interface: ease of use, time burden, and alignment with clinical workflow.
2. 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.
3. 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 Type: Observational
• Enrollment (Estimated): 3000

Contacts and Locations

• Study Contact: Name: Mary-Anne Hartley; Phone Number: +41216930828; Email: mary-anne.hartley@epfl.ch
• Study Contact Backup: Name: Kristina Keitel; Phone Number: +41216932039; Email: Kristina.keitel@epfl.ch

Study Locations

• Kenya
  • Nairobi County
    • Nairobi, Nairobi County, Kenya, 19676
      • Not yet recruiting
      • University of Nairobi College of health sciences
      • Contact: Paul Macharia; Email: pmachariah@gmail.com
      • Principal Investigator: Paul Macharia
• Malawi
  • Blantyre
    • Blantyre, Blantyre, Malawi, 303
      • Recruiting
      • Malawi University of Business and Applied Sciences (MUBAS)
      • Contact: Chiyembekezo Kachimanga; Phone Number: +265 887247777; Email: chembekachimanga@yahoo.co.uk
      • Principal Investigator: Chiyembekezo Kachimanga
• Tanzania
  • Dar es Salaam Region
    • Dar es Salaam, Dar es Salaam Region, Tanzania
      • Not yet recruiting
      • Muhimbili University of Health and Allied Sciences (MUHAS)
      • Contact: Hendry R Sawe; Phone Number: +255754885658; Email: hendry_sawe@yahoo.com
      • Principal Investigator: Hendry R Sawe
  • Dodoma
    • Dodoma, Dodoma, Tanzania
      • Recruiting
      • Ifakara Health Institute (IHI)
      • Contact: Grace Mhalu; Phone Number: +255655888891; Email: gmhalu@ihi.or.tz
      • Principal Investigator: Grace Mhalu

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: No older than 1 day (Child)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

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.

Description

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.

Study Plan

How is the study designed?

Design Details

• Observational Models: COHORT
• Time Perspectives: PROSPECTIVE

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Non interventional study; Non interventional study	Other: Non interventional study; Non interventional study

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	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.	'Day 7' and 'Day 28'

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Implementation Feasibility Research	Usability: Quantitative scoring rubric for clinicians; semi-structured interviews with clinicians; evaluation methods will be used	week 4 until study completion
Implementation Feasibility Research	Workflow integration feasibility: This will be evaluated using semi structured interviews and questionnaires	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	week 4 until study completion
Implementation Feasibility Research	Implementation facilitators and barriers: This will be evaluated using semi-structured interviews and questionnaires	week 4 until study completion

Collaborators and Investigators

• Sponsor: Ecole Polytechnique Fédérale de Lausanne
• Collaborators: Ifakara Health Institute; Muhimbili University of Health and Allied Sciences; University of Nairobi, Kenya; Malawi University of Business and Applied Sciences (MUBAS) WASHTED Centre
• Investigators: Principal Investigator: Mary-Anne Hartley, LIGHT

Publications and helpful links

General 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 record dates

Study Major Dates

• Study Start (Actual): August 31, 2026
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2026

Study Registration Dates

• First Submitted: September 9, 2026
• First Submitted That Met QC Criteria: October 29, 2026
• First Posted (Actual): November 3, 2026

Study Record Updates

• Last Update Posted (Actual): November 3, 2026
• Last Update Submitted That Met QC Criteria: October 29, 2026
• Last Verified: October 1, 2026

More Information

Terms related to this study

• Keywords: Acute conditions
• Other Study ID Numbers: LiGHT-26-02; INV-076674 (Other Grant/Funding Number: BILL and Melinda gates foundation)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES

IPD Plan Description

We will share IPD within 12 months of study completion.

IPD Sharing Time Frame

IPD information will be shared within 12 months of study completion.

IPD Sharing Access Criteria

Will be confirmed within 12 months of study completion

• IPD Sharing Supporting Information Type: STUDY_PROTOCOL

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No