Assessing the Effectiveness of Large Language Model (LLM)-Enabled Nurse Treatment Planning in 2 Indian Districts: A Pilot Study
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 736
- 试验地点
- 3
- 主要终点
- Clinical Quality of Consultation (Clinical Management and Clinical Reasoning Score)
研究概览
简要总结
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Crossover
- 主要目的
- Treatment
- 盲法
- Single (Outcomes Assessor)
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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
排除标准
- •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
研究组 & 干预措施
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.
干预措施: AI-enabled clinical decision support tool (software) used by nurses (Other)
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.
干预措施: Physician consultation (Other)
结局指标
主要结局
Clinical Quality of Consultation (Clinical Management and Clinical Reasoning Score)
时间窗: Day 1 (same study visit, immediately after completion of both consultations)
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.
次要结局
- Patient Experience on Exit Survey(Day 1 (immediately after completion of the nurse + LLM consultation during the study visit))
- 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))
研究者
Sarah Nabia
Research Consultant
HEAL India