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

NCT07432893 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 672

Last updated 2026-02-25

No results posted yet for this study

Summary

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.

Conditions

Interventions

OTHER

AI-enabled clinical decision support tool (software) used by nurses

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.

OTHER

Physician consultation

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.

Sponsors & Collaborators

  • Liver Foundation, West Bengal

    collaborator UNKNOWN

  • Endless Health

    collaborator UNKNOWN

  • Sarah Nabia

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
SINGLE
Model
CROSSOVER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-01-13
Primary Completion
2026-07-15
Completion
2026-07-31

Countries

  • India

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07432893 on ClinicalTrials.gov