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

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.

Study Overview

• Status: Recruiting
• Conditions: Hypertension; Fever; Diabete Mellitus; Breathlessness
• Intervention / Treatment: Other: AI-enabled clinical decision support tool (software) used by nurses; Other: Physician consultation

• Study Type: Interventional
• Enrollment (Estimated): 672
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Sarah Nabia, MA, MPH, MBA; Phone Number: 4438503359; Email: snabia24@gmail.com
• Study Contact Backup: Name: Anup Agarwal, MBBS; Phone Number: 5056207815; Email: mailanupagarwal@gmail.com

Study Locations

• India
  • West Bengal
    • Kolkata, West Bengal, India
      • Recruiting
      • Liver Foundation
      • Contact: Parthasarathi Mukherjee; Phone Number: +919830356780; Email: spartham@gmail.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Treatment
• Allocation: Randomized
• Interventional Model: Crossover Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Nurse+Large language model clinical consultation; 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; 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.
Active Comparator: Physician led clinical consultation (standard of care); 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; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Patient Experience Score on Exit Survey (Likert Scale Composite Score)	Composite patient experience score derived from an 9-item Likert-scale exit survey adapted from the WHO Health System Responsiveness framework and PSQ-18. Items assess communication, understanding, dignity/respect, trust in AI use, and overall satisfaction. Responses are scored 1-5 per item and averaged to generate a composite score (range 1-5), with higher scores indicating more positive experience.	Day 1 (immediately after completion of the nurse + LLM consultation during the study visit)
Nurse-Reported Acceptability and Feasibility Themes from Semi-Structured Interviews	Qualitative assessment of nurse-reported usability, trust in AI recommendations, workflow impact, barriers, facilitators, and willingness to continue use. Interviews are audio recorded and thematically analyzed. Outcomes will be reported as identified themes with representative quotations and frequency of theme occurrence across participants.	Through study completion (after nurses complete a minimum of 10 AI-assisted consultations; up to 9 months)

Collaborators and Investigators

• Sponsor: Sarah Nabia
• Collaborators: Liver Foundation, West Bengal; Endless Health

Study record dates

Study Major Dates

• Study Start (Actual): January 13, 2026
• Primary Completion (Estimated): July 15, 2026
• Study Completion (Estimated): July 31, 2026

Study Registration Dates

• First Submitted: January 12, 2026
• First Submitted That Met QC Criteria: February 19, 2026
• First Posted (Actual): February 25, 2026

Study Record Updates

• Last Update Posted (Actual): February 25, 2026
• Last Update Submitted That Met QC Criteria: February 19, 2026
• Last Verified: January 1, 2026

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Delivery of Health Care; Health Personnel; Frontline Workers; Resource-Limited Settings
• Additional Relevant MeSH Terms: Endocrine System Diseases; Vascular Diseases; Cardiovascular Diseases; Metabolic Diseases; Respiratory Tract Diseases; Respiration Disorders; Glucose Metabolism Disorders; Signs and Symptoms, Respiratory; Body Temperature Changes; Pathological Conditions, Signs and Symptoms; Nutritional and Metabolic Diseases; Signs and Symptoms; Hypertension; Diabetes Mellitus; Dyspnea; Fever
• Other Study ID Numbers: HREC,IILDS/2025-R70

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: 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.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No