Assessing the Effectiveness of Large Language Model (LLM)-Enabled Nurse Treatment Planning in 2 Indian Districts
Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18+
SponsorSarah Nabia
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Adults aged ≥18 years
Presenting to participating primary care facilities in study sites
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)
Disqualifiers
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
Trial design
Design model
Crossover
Treatments tested in this trial
AI-enabled clinical decision support tool (software) used by nurses
Other interventionA 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 consultation
Other interventionParticipants 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.
Treatment groups
672 Participants
are divided into 2 treatment groupsGroup A: Nurse+Large language model clinical consultationExperimental treatment 1 intervention
Group B: Physician led clinical consultation (standard of care)Active comparator 1 intervention
Trial outcomes
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.
Time frame
Day 1 (same study visit, immediately after completion of both consultations)Secondary outcomes
1
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.
Time frame
Day 1 (immediately after completion of the nurse + LLM consultation during the study visit)2
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)Sponsors and contacts
Click on the lead sponsor to view all of their trials.
Sarah Nabia
Lead sponsor
HEAL India
Sponsor institution
Liver Foundation, West Bengal
Collaborator
Endless Health
Collaborator
Source ClinicalTrials.gov