Emergency Patients Being Transported by Rescue AmbulanceNCT05939258
Data Acquisition for Connected Network for EMSs Comprehensive Technical-support Using Artificial Intelligence
An observational study of Emergency Patients Being Transported by Rescue Ambulance, run by Yonsei University at 1 site in South Korea.
Trial summary
Currently, the domestic emergency medical system is disconnected from the information flow between hospitals, emergency sites, and control agencies, which are participants in the emergency medical system, and there are limitations in collecting and utilizing integrated data in emergency situations [1]. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Emergency Patients Being Transported by Rescue Ambulance
- How many people
- 15,296 participants
- Where
- 1 siteSouth Korea
- Study dates
- 19 Apr 2021to 31 Dec 2021
- Phase
- Observational
- 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
- Healthy volunteers accepted
- Adult, Older adult
Study population: * One interim analysis 6 months after the start of the study. * Data analysis: Patients who were transported to the emergency department of Severance Hospital by ambulance vehicles from two fire stations for 6 months, and more than 20% of the items required to be filled in the case record were checked * Abnormal management plan: The researchers discuss the reason for the missing value through a data analysis meeting and plan a plan for it.
Who may be able to join
- Patients are transported from Seodaemun Fire Station, Mapo Fire Station to Severance Hospital Emergency Care Center via 119 ambulance
Who cannot join
- Minors under the age of 18
Trial locations and contacts
1 site in 1 country.
About this study
Currently, the domestic emergency medical system is disconnected from the information flow between hospitals, emergency sites, and control agencies, which are participants in the emergency medical system, and there are limitations in collecting and utilizing integrated data in emergency situations [1]. In addition, due to the lack of manpower for emergency services at the site and the lack of a real-time patient information delivery system, sufficient data records are not made to reflect the situation at the emergency site, and emergency patient information at the pre-hospital stage is not delivered to the transfer hospital [1]. Records of pre-hospital patient information that are currently being prepared are often written by hand, relying on the memory of paramedics after completing patient transfer, so the data is highly inaccurate and cannot be guaranteed to be reliable[2]. In particular, in the case of the four major serious emergency diseases, which are called cardiac arrest, severe trauma, cardiovascular emergency, and cerebrovascular emergency, the patient information identified in the emergency stage is very important in determining the severity, so it is very important to collect real-time patient information in the field to evaluate the severity, and based on the results of this evaluation, it is possible to select a medical institution suitable for treatment [3,4]. In addition, in the case of these serious emergency diseases, since targeted treatment is determined to be performed within a certain time, if the medical staff of the medical institution is aware of the patient's information before the patient arrives at the hospital, it is possible to prepare in advance for emergency treatment, thereby increasing the performance rate of emergency treatment within a reasonable time [5,6,7].
Read the detailed description
We develop an AI-based algorithm that can predict the four major serious emergency diseases that require immediate emergency treatment and evaluate their severity through the following input and output models.
- Algorithm input data 1) Video/Image Data
- Video acquisition via a 360-degree camera installed inside the ambulance and a Mobile Hot spot (MHS) device connection (RJ-45 wired connection)
- Video data collection via neckband camera (wearable)
- Number of videos through smart glasses (wearable) devices and first-aid terminals 2) Sound Signal Data
- Collect pre-hospital paramedic voice and patient voice data through bone conduction microphones worn by paramedics 3) Bio-signal data
- Patient monitoring device installed in ambulance Mobile Hot Spot (MHS) device Collecting and transmitting vital signs through TCP/IP connection
- Defibrillators and emergency terminals used by field crews (5G support) Collect and transmit vital signs through TCP/IP connection
- Development technology 1) Development of voice recognition AI technology in emergency environment
- Collect spoken text, emergency-related sentences, and voice data from the site-transport phase
- Speech text collection from on-site transfer service scenario to build voice DB for voice recognition learning
- Paraphrasing, from the collected text, in which paramedics generate sentences with similar meanings that can be uttered 2) Establishment of a natural language processing system for emergency environment voice transcription data - natural language processing such as stemming analysis and entity name recognition Optimize the emergency medical domain of the module
- Collection of language data in emergency environments such as emergency activities, first aid, and first aid
- Processing collected emergency environment language data for domain optimization and learning a machine learning-based natural language processing model 3) Paramedic voice information noise removal and speaker separation model design 4) Development of AI-based image recognition bio-sign information monitoring technology in ambulances
- Development of image-based character recognition algorithm for PMS (Patient monitoring system) equipment output vital signs
- Implementation of automatic recognition technology for PMS equipment (location, type, brand, etc.) through AI learning-based 5G 360°CAM video
- Development of automatic character area recognition and OCR (Optical Character Recognition)-based reading algorithm for each type of vital signal
- Implementation of NLP (Natural language process)-based specific/distorted character correction technology
- Development of image pre-processing technology that minimizes the effects of background, noise, vibration, lighting, etc.
- Development of emergency activity image information object detection module 6) AI behavior detection video analysis modeling
- Behavior detection using deep learning Image Analysis Modeling- Analysis of General Behavior Detection Techniques
- Class target test similar to emergency medical behavior during general behavior detection
- Class detection similar to the rescue activities of paramedics and the movement of patients
- General behavior detection modeling 7) The input variable obtained based on the obtained multifaceted data extracts the main determinants for the model output variable through the Ji-an Lee deep network method and calculates the predictive power for the final output variable.
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Mapo Fire Station | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area Under Receiver operating characteristics, AUROC
- Algorithm prediction performance evaluation method: Module performance evaluation by measuring the recipient operation characteristic graph area (AUROC) with the diagnosis name of the actual transferred patient and the prediction value output through the algorithm
- Verification of how the accuracy of the trained model is improved by accumulation of prospective data through AUROC comparison
Measured: 6 months after the start of the study, and an average of 1 year until the last analysis
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Jul 2023
- Study start19 Apr 2021
- Primary completion31 Dec 2021
- Study completion31 Dec 2021
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Emergency Patients Being Transported by Rescue AmbulanceStudies of the same condition, open and under way.
- More trials run by Yonsei UniversityThe 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
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 15,296 participants (actual)
Sponsor and investigators
- Lead sponsor
- Yonsei University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Hyuk-Jae Chang, Principal investigator, Severance Cardiovascular Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05939258
- Sponsor's protocol ID
- 4-2019-0739
- Status last verified
- Jul 2023
- First submitted
- 3 Jul 2023
- Last updated
- 11 Jul 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 11 Jul 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Data Acquisition for Connected Network for EMSs Comprehensive Technical-support Using Artificial Intelligence
- ClinicalTrials.gov ID
- NCT05939258
- Acronym / sponsor ID
- 4-2019-0739
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 15,296 participants (actual)
- Lead sponsor
- Yonsei UniversityUniversity, hospital or other
- Study start
- 19 Apr 2021 (actual)
- Primary completion
- 31 Dec 2021 (actual)
- Study completion
- 31 Dec 2021 (actual)
- First posted
- 11 Jul 2023 (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.
Currently, the domestic emergency medical system is disconnected from the information flow between hospitals, emergency sites, and control agencies, which are participants in the emergency medical system, and there are limitations in collecting and utilizing integrated data in emergency situations [1]. In addition, due to the lack of manpower for emergency services at the site and the lack of a real-time patient information delivery system, sufficient data records are not made to reflect the situation at the emergency site, and emergency patient information at the pre-hospital stage is not delivered to the transfer hospital [1]. Records of pre-hospital patient information that are currently being prepared are often written by hand, relying on the memory of paramedics after completing patient transfer, so the data is highly inaccurate and cannot be guaranteed to be reliable[2]. In particular, in the case of the four major serious emergency diseases, which are called cardiac arrest, severe trauma, cardiovascular emergency, and cerebrovascular emergency, the patient information identified in the emergency stage is very important in determining the severity, so it is very important to collect real-time patient information in the field to evaluate the severity, and based on the results of this evaluation, it is possible to select a medical institution suitable for treatment [3,4]. In addition, in the case of these serious emergency diseases, since targeted treatment is determined to be performed within a certain time, if the medical staff of the medical institution is aware of the patient's information before the patient arrives at the hospital, it is possible to prepare in advance for emergency treatment, thereby increasing the performance rate of emergency treatment within a reasonable time [5,6,7].
Detailed description
We develop an AI-based algorithm that can predict the four major serious emergency diseases that require immediate emergency treatment and evaluate their severity through the following input and output models.
- Algorithm input data 1) Video/Image Data
- Video acquisition via a 360-degree camera installed inside the ambulance and a Mobile Hot spot (MHS) device connection (RJ-45 wired connection)
- Video data collection via neckband camera (wearable)
- Number of videos through smart glasses (wearable) devices and first-aid terminals 2) Sound Signal Data
- Collect pre-hospital paramedic voice and patient voice data through bone conduction microphones worn by paramedics 3) Bio-signal data
- Patient monitoring device installed in ambulance Mobile Hot Spot (MHS) device Collecting and transmitting vital signs through TCP/IP connection
- Defibrillators and emergency terminals used by field crews (5G support) Collect and transmit vital signs through TCP/IP connection
- Development technology 1) Development of voice recognition AI technology in emergency environment
- Collect spoken text, emergency-related sentences, and voice data from the site-transport phase
- Speech text collection from on-site transfer service scenario to build voice DB for voice recognition learning
- Paraphrasing, from the collected text, in which paramedics generate sentences with similar meanings that can be uttered 2) Establishment of a natural language processing system for emergency environment voice transcription data - natural language processing such as stemming analysis and entity name recognition Optimize the emergency medical domain of the module
- Collection of language data in emergency environments such as emergency activities, first aid, and first aid
- Processing collected emergency environment language data for domain optimization and learning a machine learning-based natural language processing model 3) Paramedic voice information noise removal and speaker separation model design 4) Development of AI-based image recognition bio-sign information monitoring technology in ambulances
- Development of image-based character recognition algorithm for PMS (Patient monitoring system) equipment output vital signs
- Implementation of automatic recognition technology for PMS equipment (location, type, brand, etc.) through AI learning-based 5G 360°CAM video
- Development of automatic character area recognition and OCR (Optical Character Recognition)-based reading algorithm for each type of vital signal
- Implementation of NLP (Natural language process)-based specific/distorted character correction technology
- Development of image pre-processing technology that minimizes the effects of background, noise, vibration, lighting, etc.
- Development of emergency activity image information object detection module 6) AI behavior detection video analysis modeling
- Behavior detection using deep learning Image Analysis Modeling- Analysis of General Behavior Detection Techniques
- Class target test similar to emergency medical behavior during general behavior detection
- Class detection similar to the rescue activities of paramedics and the movement of patients
- General behavior detection modeling 7) The input variable obtained based on the obtained multifaceted data extracts the main determinants for the model output variable through the Ji-an Lee deep network method and calculates the predictive power for the final output variable.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Mapo Fire Station | – | – |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- * One interim analysis 6 months after the start of the study. * Data analysis: Patients who were transported to the emergency department of Severance Hospital by ambulance vehicles from two fire stations for 6 months, and more than 20% of the items required to be filled in the case record were checked * Abnormal management plan: The researchers discuss the reason for the missing value through a data analysis meeting and plan a plan for it.
Key inclusion criteria (1 listed)
- Patients are transported from Seodaemun Fire Station, Mapo Fire Station to Severance Hospital Emergency Care Center via 119 ambulance
Key exclusion criteria (1 listed)
- Minors under the age of 18
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Area Under Receiver operating characteristics, AUROC
Time frame: 6 months after the start of the study, and an average of 1 year until the last analysis
* Algorithm prediction performance evaluation method: Module performance evaluation by measuring the recipient operation characteristic graph area (AUROC) with the diagnosis name of the actual transferred patient and the prediction value output through the algorithm * Verification of how the accuracy of the trained model is improved by accumulation of prospective data through AUROC comparison
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 11 Jul 2023.
- Individual participant dataSharing planned: No
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.
Hyuk-Jae Chang principal investigator
Severance Cardiovascular Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Emergency Patients Being Transported by Rescue Ambulance, run by Yonsei University at 1 site in South Korea.
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 1 inclusion and 1 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 11 Jul 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 South Korea. 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?
Area Under Receiver operating characteristics, AUROC (time frame: 6 months after the start of the study, and an average of 1 year until the last analysis). 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 11 Jul 2023.