AI-supported nurse treatment planning for adults with high blood pressure, diabetes, fever, breathlessness, or musculoskeletal pain in two Indian districts
Assessing the Effectiveness of Large Language Model (LLM)-Enabled Nurse Treatment Planning in 2 Indian Districts: A Pilot Study
This project will test whether nurses using an AI large language model tool can create treatment plans as good as standard doctor consultations for adults in rural and semi-urban India with hypertension, diabetes, fever, breathlessness, or musculoskeletal pain.
Quick facts
| Phase | Not applicable |
|---|---|
| Study type | Interventional |
| Enrollment | 672 (estimated) |
| Ages | 18 Years and up |
| Sex | All |
| Sponsor | HEAL India Academic / other |
| Locations | 1 site (Kolkata, West Bengal) |
| Trial ID | NCT07432893 on ClinicalTrials.gov |
What this trial studies
This interventional study compares nurse-led consultations aided by an AI large language model to standard physician consultations in adults (≥18) presenting to primary care clinics in two districts of West Bengal, India. Each participant receives two sequential consultations during the same visit—one with a nurse using the LLM-enabled decision support tool and one with a physician—and researchers compare clinical reasoning and treatment plans using predetermined clinical quality scores and patient satisfaction measures. The primary question is whether nurse+LLM care achieves non-inferior clinical quality scores compared with physician care, and secondary outcomes include patient acceptability and satisfaction. The trial focuses on common primary-care conditions including known hypertension or diabetes and chief complaints of fever, breathlessness, or musculoskeletal pain.
Who should consider this trial
Good fit: Adults (≥18) who present to participating primary care facilities with known hypertension or diabetes, or with fever, breathlessness, or musculoskeletal pain as a chief complaint, who can provide written consent and are willing to have two sequential consultations and complete an exit survey.
Not a fit: People with medical emergencies requiring immediate referral, those unable to give informed consent due to cognitive impairment, prior study participants, and children under 18 are unlikely to receive benefit from this intervention.
Why it matters
Potential benefit: If successful, nurses supported by AI could expand access to timely, guideline-informed primary care in resource-limited areas where physicians are scarce.
How similar studies have performed: LLM-enabled nurse-led clinical decision support is relatively novel with limited prior evidence, although other types of AI decision-support tools have shown promising but mixed results in related settings.
Eligibility criteria
Show full inclusion / exclusion criteria
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
Where this trial is running
Kolkata, West Bengal
- Liver Foundation — Kolkata, West Bengal, India (Recruiting)
Study contacts
- Study coordinator: Sarah Nabia, MA, MPH, MBA
- Email: snabia24@gmail.com
- Phone: 4438503359
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.