Assessing the Effectiveness of Large Language Model (LLM)-Enabled Nurse Treatment Planning in 2 Indian Districts
1 other identifier
interventional
672
1 country
1
Brief 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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for not_applicable hypertension
Started Jan 2026
Shorter than P25 for not_applicable hypertension
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
January 12, 2026
CompletedStudy Start
First participant enrolled
January 13, 2026
CompletedFirst Posted
Study publicly available on registry
February 25, 2026
CompletedPrimary Completion
Last participant's last visit for primary outcome
July 15, 2026
CompletedStudy Completion
Last participant's last visit for all outcomes
July 31, 2026
CompletedFebruary 25, 2026
January 1, 2026
6 months
January 12, 2026
February 19, 2026
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Clinical Quality of Consultation (Clinical Management and Clinical Reasoning Score)
This outcome assesses the quality of clinical care by comparing AI-assisted, nurse-led consultations with standard physician-led consultations. For patients with hypertension or diabetes mellitus, clinical quality is measured using a clinical management rubric with a raw score range of -2 to 7, assessing data review, complication screening, medication adherence, counseling, and treatment planning, with penalties for inappropriate counseling or treatment. For patients presenting with fever, breathlessness, or musculoskeletal pain, clinical quality is measured using a clinical reasoning rubric with a raw score range of -5 to 10, assessing differential diagnoses, final diagnosis, and next steps, with negative scores for harmful recommendations. Consultations are audio recorded, de-identified, and scored by blinded physicians. Higher scores indicate better alignment with evidence-based, context-appropriate care.
Day 1 (same study visit, immediately after completion of both consultations)
Secondary Outcomes (2)
Patient Experience Score on Exit Survey (Likert Scale Composite Score)
Day 1 (immediately after completion of the nurse + LLM consultation during the study visit)
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)
Study Arms (2)
Nurse+Large language model clinical consultation
EXPERIMENTALParticipants in this arm receive a nurse-led primary care consultation supported by a large language model (LLM)-based clinical decision support tool. During the consultation, a trained nurse conducts routine history taking and clinical assessment and engages in a multi-turn interaction with the LLM via a digital interface to support differential diagnosis, clinical reasoning, and evidence-based treatment and follow-up planning. The nurse may ask additional questions of the patient based on LLM prompts. The final clinical recommendations are generated collaboratively by the nurse using the LLM outputs and documented as a treatment plan. This arm evaluates whether AI-assisted nurse-led care can deliver clinical quality comparable to standard physician-led care in primary health settings.
Physician led clinical consultation (standard of care)
ACTIVE COMPARATORThe doctor consultation represents standard-of-care clinical management that is already known and accepted to be effective for diagnosing and treating the study conditions. It is an active clinical intervention, not a placebo, sham, or no-intervention arm, and it serves as the comparator against the experimental nurse + LLM intervention.
Interventions
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.
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.
Eligibility Criteria
You may qualify if:
- 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
You may not qualify if:
- Inability to provide informed consent due to cognitive impairment (e.g., dementia or intellectual disability)
- Medical instability or condition requiring immediate emergency referral
- Prior participation in the study during an earlier visit
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Sarah Nabialead
- Liver Foundation, West Bengalcollaborator
- Endless Healthcollaborator
Study Sites (1)
Liver Foundation
Kolkata, West Bengal, India
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Central Study Contacts
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- RANDOMIZED
- Masking
- SINGLE
- Who Masked
- OUTCOMES ASSESSOR
- Purpose
- TREATMENT
- Intervention Model
- CROSSOVER
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR INVESTIGATOR
- PI Title
- Research Consultant
Study Record Dates
First Submitted
January 12, 2026
First Posted
February 25, 2026
Study Start
January 13, 2026
Primary Completion
July 15, 2026
Study Completion
July 31, 2026
Last Updated
February 25, 2026
Record last verified: 2026-01
Data Sharing
- IPD Sharing
- Will not share
This study involves audio-recorded clinical consultations, detailed transcripts, and qualitative interviews collected in small, identifiable clinic populations. Even after de-identification, there is a meaningful risk of re-identification, particularly from narrative data and voice-derived content. In addition, participant consent forms and ethics approvals are designed for aggregate reporting only, not public IPD sharing.