{
  "abstract": "Introduction Community health workers (CHWs) are critical to healthcare delivery in low-resource settings but often lack formal clinical training, limiting their decision-making. Large language models (LLMs) could provide real-time, context-specific support to improve referrals and management plans. This study aims to evaluate the potential utility of LLMs in assisting CHW decision-making in Rwanda.Methods and analysis This is a prospective, observational study conducted in Nyabihu and Musanze districts, Rwanda. Audio recordings of CHW-patient consultations will be transcribed and analysed by an LLM to generate referral decisions, differential diagnoses and management plans. These outputs, alongside CHW decisions, will be evaluated against a clinical expert panel’s consensus. The primary outcome is the appropriateness of referral decisions. Secondary outcomes include diagnostic accuracy, management plan quality, and patient and user perceptions to ambient recording of consultations. Sample size is set at 800 consultations (400 per district), powered to detect a 15–20 percentage point improvement in referral appropriateness.Ethics and dissemination Ethical approval has been obtained from the Rwandan National Ethics Committee (RNEC) (Ref number: RNEC 853/2025) in June 2025, recruitment started in July 2025 and results are expected in late 2025. Results will be disseminated via stakeholder meetings, academic conferences and peer-reviewed publication.Trial registration number PACTR202504601308784.",
  "authors": [
    {
      "affiliations": [
        "University of Birmingham, Birmingham, UK"
      ],
      "name": "Vaishnavi Menon"
    },
    {
      "affiliations": [
        "University of Global Health Equity, Kigali, Rwanda"
      ],
      "name": "Natnael Shimelash"
    },
    {
      "affiliations": [
        "Digital Umuganda, Kigali, Rwanda"
      ],
      "name": "Samuel Rutunda"
    },
    {
      "affiliations": [
        "Centre for the Fourth Industrial Revolution, Kigali, Rwanda"
      ],
      "name": "Cyprien Nshimiyimana"
    },
    {
      "affiliations": [
        "University of Birmingham, Birmingham, UK"
      ],
      "name": "Lucinda Archer"
    },
    {
      "affiliations": [
        "PATH, Seattle, Washington, USA"
      ],
      "name": "Mira Emmanuel-Fabula"
    },
    {
      "affiliations": [
        "University of Global Health Equity, Kigali, Rwanda"
      ],
      "name": "Derbew Fikadu Berhe"
    },
    {
      "affiliations": [
        "University of Birmingham, Birmingham, UK"
      ],
      "name": "Jaspret Gill"
    },
    {
      "affiliations": [
        "Rwanda Biomedical Center, Kigali, Rwanda"
      ],
      "name": "Emery Hezagira"
    },
    {
      "affiliations": [
        "Rwanda Biomedical Center, Kigali, Rwanda"
      ],
      "name": "Eric Remera"
    },
    {
      "affiliations": [
        "University of Birmingham, Birmingham, UK"
      ],
      "name": "Richard Riley"
    },
    {
      "affiliations": [
        "University of Global Health Equity, Kigali, Rwanda"
      ],
      "name": "Rex Wong"
    },
    {
      "affiliations": [
        "University of Birmingham, Birmingham, UK"
      ],
      "name": "Alastair K Denniston"
    },
    {
      "affiliations": [
        "University of Birmingham, Birmingham, UK",
        "PATH, Seattle, Washington, USA"
      ],
      "name": "Bilal Akhter Mateen"
    },
    {
      "affiliations": [
        "University of Birmingham, Birmingham, UK"
      ],
      "name": "Xiaoxuan Liu"
    }
  ],
  "title": "Assessing the potential utility of large language models for assisting community health workers: protocol for a prospective, observational study in Rwanda",
  "uid": "0fbab70a-a949-5bd5-97d5-694eb36476f9"
}