Assessing the Effectiveness of Large Language Model (LLM)-Enabled Nurse Treatment Planning in 2 Indian Districts

• AI-enabled clinical decision support tool (software) used by nurses (Other): A nurse-led primary care consultation supported by a large language model-based clinical decision support tool. The nurse uses the AI tool during the patient encounter to support clinical reasoning, differential diagnosis, and evidence-based treatment and follow-up planning.
• Physician consultation (Other): Participants receive a routine physician-led primary care consultation conducted according to existing clinical practice. The physician independently performs history taking, clinical assessment, diagnosis, and treatment planning without use of the AI tool.

Primary outcomes

• Clinical Quality of Consultation (Clinical Management and Clinical Reasoning Score) [Day 1 (same study visit, immediately after completion of both consultations)]: Clinical quality of the consultation, scored by two blinded physician graders using a domain-based rubric (Annexure 1). Each domain is scored 0 (inadequate), 1 (suboptimal), or 2 (optimal). Disease (clinical management: hypertension, diabetes) cases are scored on four domains - quality of history, accuracy of next steps, safety, and comprehensiveness - for a total of 0-8. Symptom (clinical reasoning: fever, breathlessness, musculoskeletal pain) cases are scored on all six domains, adding quality of differential and accuracy of provisional diagnosis, for a total of 0-12. The primary outcome is the absolute total score; results are also reported normalised to 0-100% for concordance with the original registration. The two study arms (nurse+LLM vs. physician standard of care) are compared within each patient.

Secondary outcomes

• Patient Experience on Exit Survey [Day 1 (immediately after completion of the nurse + LLM consultation during the study visit)]: Patient-reported experience of the nurse+LLM consultation, measured by a brief exit survey covering three domains: communication and understanding, trust and comfort with AI use, and respect and satisfaction (one item per domain; three-point response scale). Responses are summarised descriptively (frequencies/proportions per item and domain).
• Nurse-Reported Acceptability and Feasibility Themes from Semi-Structured Interviews [Through study completion (after nurses complete a minimum of 10 AI-assisted consultations; up to 9 months)]: Qualitative assessment of nurse-reported usability, trust in AI recommendations, workflow impact, barriers, facilitators, and willingness to continue use. Interviews are audio recorded and thematically analyzed. Outcomes will be reported as identified themes with representative quotations and frequency of theme occurrence across participants.

Sex: All, ages: 18 Years to any

Inclusion Criteria:

1. Adults aged ≥18 years
2. Presenting to participating primary care facilities in study sites
3. Meeting criteria for at least one of the following conditions or symptoms:

   * Hypertension: Known diagnosis
   * Diabetes mellitus: Known diagnosis or laboratory evidence (HbA1c ≥6.5%, fasting blood glucose ≥126 mg/dL, or post-prandial glucose ≥200 mg/dL)
   * Fever: Presenting as chief complaint
   * Breathlessness: Presenting as chief complaint, without evidence of fever
   * Musculoskeletal pain: Presenting as chief complaint, without evidence of fever
4. Able and willing to provide written informed consent
5. Willing to participate in two sequential consultations and complete an exit survey

Exclusion Criteria:

1. Inability to provide informed consent due to cognitive impairment (e.g., dementia or intellectual disability)
2. Medical instability or condition requiring immediate emergency referral
3. Prior participation in the study during an earlier visit

• Study Protocol, Statistical Analysis Plan, and Informed Consent Form (PDF, 2026-07-29)