Internal DiseaseMLDNCT05596929
Build-up Computed Assisted History Taking, Physical Examination and Diagnosis System of Emergency Patient Through Machine Learning (II)
A study testing Artificial intelligence in people with Internal Disease, run by National Taiwan University Hospital at 1 site in Taiwan.
Trial summary
In emergency department（ED）, physicians need to complete patient evaluation and management in a short time, which required different history taking, and physical examination skill in healthcare system. Natural language processing（NLP） became easily accessible after the development of machine learning（ML）. Read more about this study
- Who is it for?
- Ages 20 years and older
- Conditions the study is for
- Internal Disease
- What is being tested?
- Artificial intelligence
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 3,000 participants
- Where
- 1 siteTaiwan
- Study dates
- 12 Dec 2022to 30 Mar 2023
- 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 20 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Over twenty years old
- Non-traumatic patient
Who cannot join
- Excluding the patients for administration reasons (issuing a medical certificate)
- Excluding the patients for non-emergency reasons like simply acupuncture, virus screening and prescription for medication.
- Excluding Patients who allocated to critical care station
Trial locations and contacts
1 site in 1 country.
Taiwan1 site, 1 recruiting
- Recruiting
National Taiwan University Hospital
Taipei, 100
Wang, Dr., 886-2-23123456 ext. 265659, ticoer@ntuh.gov.tw
About this study
In emergency department（ED）, physicians need to complete patient evaluation and management in a short time, which required different history taking, and physical examination skill in healthcare system.
Natural language processing（NLP） became easily accessible after the development of machine learning（ML）. Besides, electronic medical record（EMR） had been widely applied in healthcare systems. There are more and more tools try to capture certain information from the EMR help clinical workers handle increasing patient data and improving patient care.
Read more
However, to err is human. Physicians might omit some important signs or symptoms, or forget to write it down in the record especially in a busy emergency room. It will lead to an unfavorable outcome when there were medical legal issue or national health insurance review. The condition could be limited by a EMR supporting system. The quality of care will also improve.
The investigators are planning to analyze EMR of emergency room by NLP and machine learning. To establish the linkage between triage data, chief complaint, past history, present illness and physical examination. The investigators will try to predict the tentative diagnosis and patient disposition after the relationship being found. Thereafter, the investigators could try to predict the key element of history taking and physical examination of the patient and inform the physician when the miss happened. The investigators hope the system may improve the quality of medical recording and patient care.
- Conditions
- Internal Disease
- Being tested
- Artificial intelligence
- Keywords
- Artificial intelligence
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligenceDiagnostic test
After the patients under triage classification to which randomly allocates in two groups. The group with AI intervention and the other without AI intervention.
| Study group | Role | What they receive |
|---|---|---|
| Control | No intervention | Not described |
| Experimental | Experimental | Not described Receives: Diagnostic Test: Artificial intelligence |
What is being measured
The primary outcome is the main question the study is designed to answer.
Senior doctor appraisal
Senior doctor appraisal which measured by an established questionnaire. Senior doctor will fill an expert-verified clinical note quality evaluation questionnaire after junior doctor finished patient interview and clinical note recording. The questionnaire is designed to use 5 points likert scale and higher scores mean a better outcome.
Measured: 24 hours
Secondary outcomes (2)
Accuracy of diagnosis prediction
The percentage of predicted diagnosis match the final diagnosis.
Measured: patient discharge from ED, up to 1 week
Rationality of diagnosis prediction
Senior doctors will assess rationality of predicted diagnosis.
Measured: 24 hours
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Oct 2022
- Study start12 Dec 2022
- Primary completion30 Mar 2023 (estimated)
- Study completion30 Mar 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Internal DiseaseStudies of the same condition, open and under way.
- More trials run by National Taiwan University HospitalThe sponsor's other registered studies.
- 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
- Parallel groups
- Masking
- Triple blind
- Who is masked
- Participant, Care provider, Investigator
- Enrolment
- 3,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National Taiwan University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Huang, Dr., Study chair, National Taiwan University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05596929
- Sponsor's protocol ID
- 202110012RIND
- Acronym
- MLD
- Status last verified
- Dec 2022
- First submitted
- 10 Oct 2022
- Last updated
- 22 Feb 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 22 Feb 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Build-up Computed Assisted History Taking, Physical Examination and Diagnosis System of Emergency Patient Through Machine Learning (II)
- ClinicalTrials.gov ID
- NCT05596929
- Acronym / sponsor ID
- MLD / 202110012RIND
- Conditions
- Internal Disease
- Keywords
- Artificial intelligence
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 3,000 participants (estimated)
- Lead sponsor
- National Taiwan University HospitalUniversity, hospital or other
- Study start
- 12 Dec 2022 (actual)
- Primary completion
- 30 Mar 2023 (estimated)
- Study completion
- 30 Mar 2023 (estimated)
- First posted
- 27 Oct 2022 (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.
In emergency department（ED）, physicians need to complete patient evaluation and management in a short time, which required different history taking, and physical examination skill in healthcare system.
Natural language processing（NLP） became easily accessible after the development of machine learning（ML）. Besides, electronic medical record（EMR） had been widely applied in healthcare systems. There are more and more tools try to capture certain information from the EMR help clinical workers handle increasing patient data and improving patient care.
However, to err is human. Physicians might omit some important signs or symptoms, or forget to write it down in the record especially in a busy emergency room. It will lead to an unfavorable outcome when there were medical legal issue or national health insurance review. The condition could be limited by a EMR supporting system. The quality of care will also improve.
The investigators are planning to analyze EMR of emergency room by NLP and machine learning. To establish the linkage between triage data, chief complaint, past history, present illness and physical examination. The investigators will try to predict the tentative diagnosis and patient disposition after the relationship being found. Thereafter, the investigators could try to predict the key element of history taking and physical examination of the patient and inform the physician when the miss happened. The investigators hope the system may improve the quality of medical recording and patient care.
Study design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Triple blind (participant, care provider, investigator)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Control | No intervention | – |
| Experimental | Experimental | Diagnostic Test: Artificial intelligence |
Who may qualify
- Ages
- 20 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- Over twenty years old
- Non-traumatic patient
Key exclusion criteria (3 listed)
- Excluding the patients for administration reasons (issuing a medical certificate)
- Excluding the patients for non-emergency reasons like simply acupuncture, virus screening and prescription for medication.
- Excluding Patients who allocated to critical care station
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 |
|---|---|---|
| Artificial intelligenceAfter the patients under triage classification to which randomly allocates in two groups. The group with AI intervention and the other without AI intervention. | Diagnostic test | Experimental |
Primary outcome
Senior doctor appraisal
Time frame: 24 hours
Senior doctor appraisal which measured by an established questionnaire. Senior doctor will fill an expert-verified clinical note quality evaluation questionnaire after junior doctor finished patient interview and clinical note recording. The questionnaire is designed to use 5 points likert scale and higher scores mean a better outcome.
2 secondary outcomes
Accuracy of diagnosis prediction
Time frame: patient discharge from ED, up to 1 week
Rationality of diagnosis prediction
Time frame: 24 hours
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Feb 2023.
- Individual participant dataSharing planned: No
Related trials for Internal Disease
Recruiting
Addressing Nocturia in Older Women Utilizing Behavioral Sleep Intervention and Brief Mindfulness
Nocturia, Insomnia
RecruitingObservational study
Locus Coeruleus and CCHS (Congenital Central Hypoventilation Syndrome)
Ondine Syndrome (Congenital Central Hypoventilation Syndrome)
RecruitingPhase 1
A Study Evaluating the Safety and Efficacy of Targeted Therapies in Subpopulations of Patients With Metastatic Colorectal Cancer (INTRINSIC)
Metastatic Colorectal Cancer
Not yet recruiting
A Transdiagnostic Sleep Health Intervention for Veterans With PTSD
Stress Disorders, Post-Traumatic, Sleep Initiation and Maintenance Disorders
RecruitingObservational study
Development and Validation of a Multimodal Deep Learning Model Using Peripheral Oxygen Saturation and Ballistocardiography Signals for Predicting Apnea-Hypopnea Index in Obstructive Sleep Apnea
Obstructive Sleep Apnea
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.
Huang, Dr. study chair
National Taiwan University Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Artificial intelligence in people with Internal Disease, run by National Taiwan University Hospital at 1 site in Taiwan.
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 2 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: status not confirmed, as of the sponsor's last update on 22 Feb 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Hui-Chih Wang, Dr. (+88623123456, ticoer@ntuh.gov.tw), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Senior doctor appraisal (time frame: 24 hours). 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 22 Feb 2023.