Assessing the Effectiveness of Large Language Model (LLM)-Enabled Nurse Treatment Planning in 2 Indian Districts: A Pilot Study
The goal of this clinical trial is to learn whether AI-enabled, nurse-led treatment planning can improve the quality of clinical reasoning and management compared with standard physician-led care in adult primary care patients (≥18 years) presenting with hypertension, diabetes mellitus, fever, breathlessness, or musculoskeletal pain in rural and semi-urban India. The main questions it aims to answer are: * Does a nurse + large language model (LLM) consultation achieve non-inferior clinical quality scores compared with a standard doctor consultation? * Is AI-assisted nurse-led care acceptable and satisfactory to patients in primary healthcare settings? Researchers will compare nurse + LLM-led consultations with physician-led standard-of-care consultations within the same participant to see if the AI-enabled nurse model delivers comparable or improved clinical reasoning and treatment planning. Participants will: * Receive two sequential consultations for the same visit (one with a nurse using an AI tool and one with a physician, order randomized). * Have both consultations audio recorded for blinded clinical quality assessment. * Complete a brief exit survey on communication, trust, and satisfaction after the AI-assisted nurse consultation.
• Adults aged ≥18 years
• Presenting to participating primary care facilities in study sites
• 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
• Able and willing to provide written informed consent
• Willing to participate in two sequential consultations and complete an exit survey