[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100626227":3},{"organization":4,"armGroups":7,"interventions":20,"overallOfficials":25,"centralContacts":30,"locations":40,"responsibleParty":58,"collaborators":62,"id":68,"slug":25,"hasResults":69,"nctId":70,"briefTitle":71,"officialTitle":72,"acronym":25,"eligibilityCriteria":73,"healthyVolunteers":69,"sex":74,"minAge":75,"maxAge":25,"enrollmentInfo":76,"targetDuration":25,"studyType":79,"phases":80,"briefSummary":82,"conditions":83,"keywords":88,"overallStatus":43,"whyStopped":25,"lastUpdateSubmitDate":94,"lastUpdatePostDateStruct":95,"startDateStruct":98,"completionDateStruct":100,"leadSponsor":102,"locationsCount":103},{"fullName":5,"class":6},"HEAL India","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"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.",[13],"Other: AI-enabled clinical decision support tool (software) used by nurses",{"label":15,"type":16,"description":17,"interventionNames":18},"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.",[19],"Other: Physician consultation",[21,26],{"type":6,"name":22,"description":23,"armGroupLabels":24,"otherNames":25},"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.",[9],null,{"type":6,"name":27,"description":28,"armGroupLabels":29,"otherNames":25},"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.",[15],[31,36],{"name":32,"role":33,"phone":34,"phoneExt":25,"email":35},"Sarah Nabia, MA, MPH, MBA","CONTACT","4438503359","snabia24@gmail.com",{"name":37,"role":33,"phone":38,"phoneExt":25,"email":39},"Anup Agarwal, MBBS","5056207815","mailanupagarwal@gmail.com",[41],{"facility":42,"status":43,"city":44,"state":45,"zip":25,"country":46,"cosmosGeoPoint":47,"geoPoint":52,"contacts":53},"Liver Foundation","RECRUITING","Kolkata","West Bengal","India",{"type":48,"coordinates":49},"Point",[50,51],88.36304,22.56263,{"lat":51,"lon":50},[54],{"name":55,"role":33,"phone":56,"phoneExt":25,"email":57},"Parthasarathi Mukherjee","+919830356780","spartham@gmail.com",{"type":59,"investigatorFullName":60,"investigatorTitle":61,"investigatorAffiliation":5,"oldNameTitle":25,"oldOrganization":25},"SPONSOR_INVESTIGATOR","Sarah Nabia","Research Consultant",[63,66],{"name":64,"class":65},"Liver Foundation, West Bengal","UNKNOWN",{"name":67,"class":65},"Endless Health","100626227",false,"NCT07432893","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","Inclusion Criteria:\n\n1. Adults aged ≥18 years\n2. Presenting to participating primary care facilities in study sites\n3. Meeting criteria for at least one of the following conditions or symptoms:\n\n   * Hypertension: Known diagnosis\n   * Diabetes mellitus: Known diagnosis or laboratory evidence (HbA1c ≥6.5%, fasting blood glucose ≥126 mg\u002FdL, or post-prandial glucose ≥200 mg\u002FdL)\n   * Fever: Presenting as chief complaint\n   * Breathlessness: Presenting as chief complaint, without evidence of fever\n   * Musculoskeletal pain: Presenting as chief complaint, without evidence of fever\n4. Able and willing to provide written informed consent\n5. Willing to participate in two sequential consultations and complete an exit survey\n\nExclusion Criteria:\n\n1. Inability to provide informed consent due to cognitive impairment (e.g., dementia or intellectual disability)\n2. Medical instability or condition requiring immediate emergency referral\n3. Prior participation in the study during an earlier visit","ALL","18 Years",{"count":77,"type":78},672,"ESTIMATED","INTERVENTIONAL",[81],"NA","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.\n\nThe main questions it aims to answer are:\n\n* Does a nurse + large language model (LLM) consultation achieve non-inferior clinical quality scores compared with a standard doctor consultation?\n* 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.\n\nParticipants will:\n\n* Receive two sequential consultations for the same visit (one with a nurse using an AI tool and one with a physician, order randomized).\n* Have both consultations audio recorded for blinded clinical quality assessment.\n* Complete a brief exit survey on communication, trust, and satisfaction after the AI-assisted nurse consultation.",[84,85,86,87],"Hypertension","Diabete Mellitus","Breathlessness","Fever",[89,90,91,92,93],"Artificial Intelligence","Delivery of Health Care","Health Personnel","Frontline Workers","Resource-Limited Settings","2026-02-19",{"date":96,"type":97},"2026-02-25","ACTUAL",{"date":99,"type":97},"2026-01-13",{"date":101,"type":78},"2026-07-31",{"name":60,"class":6},1]