History TakingMINERVA-HXNCT07877883
Safety, Feasibility, and Acceptability of the MINERVA Voice Conversational Agent for Emergency Department History-Taking
An observational study of History Taking and Artificial Intelligence (AI), run by Beth Israel Deaconess Medical Center at 1 site in United States.
Trial summary
This study evaluates whether an artificial intelligence (AI) conversational agent called MINERVA (Medical Interface for Navigating the Emergency Room with Voice Assistance) can safely and feasibly gather medical histories from emergency department (ED) patients and produce accurate summaries. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- History Taking, Artificial Intelligence (AI)
- What is being tested?
- MINERVA AI Chatbot
- How many people
- 250 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Oct 2026to 1 Feb 2027
- Phase
- Observational
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
Study population: Adult emergency department patients
Who may be able to join
- Patients aged ≥18 years.
- Patient underwent triage in the ED.
- English-speaking.
Who cannot join
- Critical illness precluding interaction with MINERVA.
- Inability to provide consent.
- Significant hearing/speech/typing impairments precluding interaction by text or voice.
- Prisoners.
- Inability to follow study procedures.
- Patients requiring isolation precautions incompatible with study procedures.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site
Beth Israel Deaconess Medical Center
Boston, Massachusetts, 02215
About this study
Official title: Feasibility and Acceptability of a Voice Conversational Agent in the Emergency Department
This study evaluates whether an artificial intelligence (AI) conversational agent called MINERVA (Medical Interface for Navigating the Emergency Room with Voice Assistance) can safely and feasibly gather medical histories from emergency department (ED) patients and produce accurate summaries. The main questions it aims to answer are:
Can an AI conversational agent gather and summarize patient histories in the emergency department?
Read more
Are the resulting summaries accurate? Do they capture essential clinical information?
Can an AI agent overseeing these conversations independently identify red-flag symptoms that may need time-sensitive clinical interventions?
Adult ED patients interact with MINERVA via either voice chat or typing on a keyboard. MINERVA asks them questions about their symptoms and generates a summary which the patients review for accuracy. MINERVA reviews the conversation for clinical red flags that suggest urgent evaluation is required. Participants fill out a survey providing feedback on their experience. The summary is not saved in the medical record and clinical care is not affected.
Physician reviewers will evaluate the safety, accuracy, and clinical value of the MINERVA interview transcripts and summaries using standardized rubrics, including whether MINERVA was correct in identifying red-flag symptoms that may need time-sensitive clinical interventions.
Read the detailed description
This is a single-arm prospective observational cohort study evaluating safety, feasibility, and acceptability of the MINERVA conversational agent for gathering and summarizing patient histories in the emergency department. ED patients who have already been assessed by a triage nurse will be identified by research assistants (RAs) and verbally consented. MINERVA will ask the patient questions about their symptoms, medical history, and risk factors, as well as clarifying questions to highlight pertinent positives and negatives or red flag symptoms. MINERVA will generate a summary at the end of the conversation which patients may edit. Patients will complete a survey after finishing the interaction.
After the interaction, MINERVA will determine whether there are any red flags (called Safety Diagnoses below) that would require urgent evaluation. These red flags will be compared to the assessments of expert physicians. Additionally, MINERVA will generate a differential diagnosis from the interview for clinician review. Descriptive statistics will be calculated for survey responses. MINERVA transcripts and summaries will be evaluated by physician reviews for accuracy, completeness, clinical utility, and safety according to standardized rubrics
- Conditions
- History Taking, Artificial Intelligence (AI)
- Being tested
- MINERVA AI Chatbot
- Keywords
- history, artificial intelligence, safety, diagnosis, triage
- Medical subject headings
- Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
MINERVA AI ChatbotOther
The MINERVA AI chatbot is AI software that allows patients to provide medical history through text or voice.
| Study group | Role | What they receive |
|---|---|---|
| AI History Arm | All participants are in this arm, in which participants provide history to the MINERVA AI Chatbot Receives: Other: MINERVA AI Chatbot |
What is being measured
The primary outcome is the main question the study is designed to answer.
Safety Diagnoses
Emergency physicians will review each patient-AI interview transcript using a standardized rubric to determine whether evaluation for possible myocardial infarction, acute stroke, or sepsis is warranted. MINERVA's assessments will be compared with these physician assessments.
Measured: 1 Day
Secondary outcomes (7)
Secondary Safety Diagnoses
Emergency physicians will review each interview transcript using a standardized rubric to determine whether evaluation for diagnoses including major traumatic injury, aortic syndromes, ovarian/testicular torsion, anaphylaxis, behavioral emergency, major bleeding, pre-eclampsia, other pregnancy emergencies, spinal cord compression, limb ischemia, toxic ingestion, or sickle cell crisis are warranted. MINERVA's assessments will be compared with these physician assessments.
Measured: 1 Day
Conversation completion
Whether interview was completed, terminated early, or patient declined to participate.
Measured: 1 Day
Conversation length
Length of patient interaction with AI conversational agent, measured in number of turns.
Measured: 1 Day
Turn Length
Length of turns for patient and AI conversational agent, measured in seconds and words.
Measured: 1 Day
Patient Acceptability of AI Conversational Agent
Patients will complete an ED Conversational Agent Patient Impressions (ED-CAPI) questionnaire before and after interacting with the chatbot, including components of the following questionnaires:
- General Medical Counsel Patient Questionnaire
- Practical Assessment of Clinical Examination Skills
- Articulate Medical Intelligence Explorer post-provider survey
- General Attitudes towards Patient-Facing AI Scale
Further questions include:
- Ease of use
- Perceived value and effects on healthcare system
- Willingness to share information with the AI
- Accuracy of the AI summary of the interview
Responses are aggregated with percent of maximum possible (POMP) methodology. All survey questions are rescaled to a 0-100 scale with 0 the worst possible impression and 100 the best. The final outcome is the 0-100 average of all responses.
Measured: 1 Day
Physician Acceptability of AI Conversational Agent
The panel of ED physicians will review interview transcripts and summaries generated by the AI conversational agent. They will complete an ED Conversational Agent MD Acceptability (ED-CAMA) survey to measure perceived utility of the tool in their clinical workflow.[
Measured: 3 Months
Primary Diagnosis
The primary diagnosis managed during the patient's emergency department encounter will be assessed via review of emergency department notes as well as admission history and physical and discharge summary when available. Bond score will measure AI diagnostic accuracy relative to the ground truth diagnosis.
Measured: 1 Day
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Oct 2026
- Study start1 Oct 2026 (estimated)
- Primary completion1 Jan 2027 (estimated)
- Study completion1 Feb 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for History TakingStudies 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
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 250 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Beth Israel Deaconess Medical Center
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Adrian Haimovich, Beth Israel Deaconess Medical Center
- Investigators
- Adrian Haimovich, MD, PhD, Principal investigator, Beth Israel Deaconess Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07877883
- Sponsor's protocol ID
- 2025P000446
- Acronym
- MINERVA-HX
- Status last verified
- Oct 2026
- First submitted
- 30 Sep 2026
- Last updated
- 16 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: The primary data collected by the study is free text interview transcripts which contain private health information.
Publications
Brodeur, Peter, et al. "A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic." arXiv preprint arXiv:2603.08448 (2026).