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

NCT07432893 | Completed

• 1 other identifier: HREC,IILDS/2025-R70
• Study Type: interventional
• Target: 736
• Locations: 1 country
• Sites: 2

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.

Conditions

Hypertension; Diabete Mellitus; Breathlessness; Fever

Keywords

Artificial Intelligence; Delivery of Health Care; Health Personnel; Frontline Workers; Resource-Limited Settings

Outcome Measures

Primary Outcomes (1)

• Clinical Quality of Consultation (Clinical Management and Clinical Reasoning Score)

  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.

  Day 1 (same study visit, immediately after completion of both consultations)

Secondary Outcomes (2)

• Patient Experience on Exit Survey

  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

EXPERIMENTAL

Participants 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.

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

Physician led clinical consultation (standard of care)

ACTIVE COMPARATOR

The 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.

Other: Physician consultation

Interventions

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.

Nurse+Large language model clinical consultation

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.

Physician led clinical consultation (standard of care)

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

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

Sponsors & Collaborators

• Sarah Nabia: lead
• Liver Foundation, West Bengal: collaborator
• Endless Health: collaborator

Study Sites (2)

Liver Foundation

Birbhum, West Bengal, India

Location

Liver Foundation

Puruliya, West Bengal, India

Location

MeSH Terms

Conditions

Hypertension; Diabetes Mellitus; Dyspnea; Fever

Condition Hierarchy (Ancestors)

Vascular Diseases; Cardiovascular Diseases; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases; Respiration Disorders; Respiratory Tract Diseases; Signs and Symptoms, Respiratory; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Body Temperature Changes

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

Model Details: This study uses a randomized, within-participant crossover interventional design. Each enrolled patient participates in two sequential clinical consultations during a single visit: (1) an AI-assisted, nurse-led consultation (intervention) and (2) a standard physician-led consultation (control). The order of consultations is randomized to minimize order effects. Both consultations address the same clinical condition or symptom and result in independent treatment plans. Because each participant serves as their own control, this design reduces between-subject variability and improves statistical efficiency. Clinical interactions are audio recorded and de-identified, and resulting treatment plans are independently scored by blinded physicians using standardized rubrics to assess clinical reasoning and management quality. In addition, patient experience is assessed via a post-consultation exit survey, and nurse experiences are explored through qualitative interviews.

Study Record Dates

• First Submitted: January 12, 2026
• First Posted: February 25, 2026
• Study Start: January 13, 2026
• Primary Completion: August 1, 2026
• Study Completion: August 1, 2026
• Last Updated: August 5, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will not share

Locations

IN (2)