HypertensionNCT07432893
Assessing the Effectiveness of Large Language Model (LLM)-Enabled Nurse Treatment Planning in 2 Indian Districts
A study testing AI-enabled clinical decision support tool (software) used by nurses and Physician consultation in people with Hypertension and Diabete Mellitus, run by Sarah Nabia at 2 sites in India.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Hypertension, Diabete Mellitus, Breathlessness
- What is being tested?
- AI-enabled clinical decision support tool (software) used by nurses and Physician consultation
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 736 participants
- Where
- 2 sitesIndia
- Study dates
- 13 Jan 2026to 1 Aug 2026
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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
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- 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
Who cannot join
- 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 locations and contacts
2 sites in 1 country.
India2 sites
Liver Foundation
Birbhum, West Bengal
Liver Foundation
Puruliya, West Bengal
About this study
Official title: 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:
Read more
- 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.
- Conditions
- Hypertension, Diabete Mellitus, Breathlessness, Fever
- Being tested
- AI-enabled clinical decision support tool (software) used by nurses, Physician consultation
- Keywords
- Artificial Intelligence, Delivery of Health Care, Health Personnel, Frontline Workers, Resource-Limited Settings
- Medical subject headings
- Hypertension, Diabetes Mellitus, Dyspnea, Fever
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: AI-enabled clinical decision support tool (software) used by nurses |
| 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. Receives: Other: Physician consultation |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Day 1 (same study visit, immediately after completion of both consultations)
Secondary outcomes (2)
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).
Measured: 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.
Measured: Through study completion (after nurses complete a minimum of 10 AI-assisted consultations; up to 9 months)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Feb 2026
- Study start13 Jan 2026
- Primary completion1 Aug 2026
- Study completion1 Aug 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for HypertensionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Crossover
- Model description
- This study uses a randomized, within-participant crossover interventional design. Each enrolled patient participates in two sequential clinical consultations during a single visit: (1) an AI-assisted, nurse-led consultation (intervention) and (2) a standard physician-led consultation (control). The order of consultations is randomized to minimize order effects. Both consultations address the same clinical condition or symptom and result in independent treatment plans. Because each participant serves as their own control, this design reduces between-subject variability and improves statistical efficiency. Clinical interactions are audio recorded and de-identified, and resulting treatment plans are independently scored by blinded physicians using standardized rubrics to assess clinical reasoning and management quality. In addition, patient experience is assessed via a post-consultation exit survey, and nurse experiences are explored through qualitative interviews.
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 736 participants (actual)
Sponsor and investigators
- Lead sponsor
- Sarah Nabia
- Sponsor type
- University, hospital or other
- Collaborators
- Liver Foundation, West Bengal, Endless Health
- Responsible party
- Sponsor-investigator, Sarah Nabia, HEAL India
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07432893
- Sponsor's protocol ID
- HREC,IILDS/2025-R70
- Status last verified
- Jan 2026
- First submitted
- 12 Jan 2026
- Last updated
- 5 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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.
Study at a glance
From the registry record, last updated by the sponsor on 5 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Assessing the Effectiveness of Large Language Model (LLM)-Enabled Nurse Treatment Planning in 2 Indian Districts: A Pilot Study
- ClinicalTrials.gov ID
- NCT07432893
- Acronym / sponsor ID
- HREC,IILDS/2025-R70
- Conditions
- Hypertension, Diabete Mellitus, Breathlessness, Fever
- Keywords
- Artificial Intelligence, Delivery of Health Care, Health Personnel, Frontline Workers, Resource-Limited Settings
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 736 participants (actual)
- Lead sponsor
- Sarah NabiaUniversity, hospital or other
- Collaborators
- Liver Foundation, West Bengal, Endless Health
- Study start
- 13 Jan 2026 (actual)
- Primary completion
- 1 Aug 2026 (actual)
- Study completion
- 1 Aug 2026 (actual)
- First posted
- 25 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
- Model description
- This study uses a randomized, within-participant crossover interventional design. Each enrolled patient participates in two sequential clinical consultations during a single visit: (1) an AI-assisted, nurse-led consultation (intervention) and (2) a standard physician-led consultation (control). The order of consultations is randomized to minimize order effects. Both consultations address the same clinical condition or symptom and result in independent treatment plans. Because each participant serves as their own control, this design reduces between-subject variability and improves statistical efficiency. Clinical interactions are audio recorded and de-identified, and resulting treatment plans are independently scored by blinded physicians using standardized rubrics to assess clinical reasoning and management quality. In addition, patient experience is assessed via a post-consultation exit survey, and nurse experiences are explored through qualitative interviews.
| Arm | Type | Receives |
|---|---|---|
| Nurse+Large language model clinical consultation | Experimental | Other: AI-enabled clinical decision support tool (software) used by nursesParticipants 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. |
| Physician led clinical consultation (standard of care) | Active comparator | Other: Physician consultationThe 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (10 listed)
- 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:
- Able and willing to provide written informed consent
- Willing to participate in two sequential consultations and complete an exit survey
Key exclusion criteria (3 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-enabled clinical decision support tool (software) used by nursesA 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. | Other | Nurse+Large language model clinical consultation |
| Physician consultationParticipants 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. | Other | Physician led clinical consultation (standard of care) |
Primary outcome
Clinical Quality of Consultation (Clinical Management and Clinical Reasoning Score)
Time frame: 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.
2 secondary outcomes
Patient Experience on Exit Survey
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
Time frame: Through study completion (after nurses complete a minimum of 10 AI-assisted consultations; up to 9 months)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Aug 2026.
- Individual participant dataSharing planned: No. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI-enabled clinical decision support tool (software) used by nurses and Physician consultation in people with Hypertension and Diabete Mellitus, run by Sarah Nabia at 2 sites in India.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 10 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 5 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in India. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Clinical Quality of Consultation (Clinical Management and Clinical Reasoning Score) (time frame: Day 1 (same study visit, immediately after completion of both consultations)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 5 Aug 2026.