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

Sarah Nabia736 enrolled

Overview

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 Type

INTERVENTIONAL

Allocation

RANDOMIZED

Purpose

TREATMENT

Masking

SINGLE

Enrollment

736

Conditions

Hypertension Diabete Mellitus

Interventions

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

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.

Physician consultationOTHER

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

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

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

Outcomes

Primary Outcomes

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.

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

Secondary Outcomes

Patient Experience on Exit Survey

Patient-reported experience of the nurse+LLM consultation, measured by a brief exit survey covering three domains: communication and understanding, trust and comfort with AI use, and respect and satisfaction (one item per domain; three-point response scale). Responses are summarised descriptively (frequencies/proportions per item and domain).

Time frame: 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.

Time frame: Through study completion (after nurses complete a minimum of 10 AI-assisted consultations; up to 9 months)