Artificial IntelligenceNCT04661488
Safety and Reliability of Artificial Intelligence Driven Symptom Assessment in Children and Adolescentes
An observational study of Artificial Intelligence and Triage, run by Turku University Hospital.
Trial summary
Digital health technologies (DHT) are increasingly developed to support healthcare systems around the world. However, they are frequently lacking evidence-based medicine and medical validation. Read more about this study
- Who is it for?
- Ages 0 to 17 yearsHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelligence, Triage
- What is being tested?
- AI driven triage-system
- Type of treatment
- Medical device
- How many people
- 1,000 participants
- Where
- No sites listed yet
- Study dates
- 1 Dec 2020to 1 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 0 to 17 years
- All sexes
- Healthy volunteers accepted
- Child
Study population: Children and adolescence of all ages and comorbidities
Who may be able to join
- all patients at the emergency department with acute symptoms
Who cannot join
- Emergency situation
About this study
Digital health technologies (DHT) are increasingly developed to support healthcare systems around the world. However, they are frequently lacking evidence-based medicine and medical validation. There is considerable need in the western countries to allocate healthcare resources accurately and give the population detailed and reliable health information enabling to take greater responsibility for their health. Intelligent patient flow management system (IPFM, product name Klinik Frontline) is developed to meet these needs. In practice, IPFM is used for decision support in the triaging and diagnostic processes as well as automatizing the management of inflow of the patients. The core of the IPFM is a clinical artificial intelligence (AI), which utilizes a comprehensive medical database of clinical correlations generated by medical doctors.
The study population of this research consists of patients from the Paediatric Emergency Clinic of Turku University Hospital (TUH). Data will be gathered during 6 months of piloting, after which the results will be analysed. Anticipated number of patients to the study is minimum of 500 patients, with objective to be 1 000. When attending to the hospital, patients or their guardians will report their demographics, background information and symptoms using structured IPFM online form. Results obtained from IPFM are blinded from the healthcare professional and IPFM does not affect professional's clinical decision making. The data obtained from IPFM online form and clinical data from the emergency department and TUH will be analysed after the data collection. The main aim of the research is to validate the use of IPFM by evaluating the association of IPFM output with 1) urgency and severity of the conditions; and 2) actual diagnoses diagnosed by medical doctors. The main hypotheses of the research are that 1) IPFM is safe and sensitive in evaluating the urgency of the conditions of arriving patients at the emergency department and that 2) IPFM has sufficient correlation of differential diagnosis with actual diagnosis made by medical doctor.
- Conditions
- Artificial Intelligence, Triage
- Being tested
- AI driven triage-system
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI driven triage-systemDevice
AI driven triage-system
| Study group | Role | What they receive |
|---|---|---|
| Children and adolescence | Not described Receives: Device: AI driven triage-system |
What is being measured
The primary outcome is the main question the study is designed to answer.
Emergency severity index (ESI)
AI-driven automated analysis of triage urgency (ESI index) will be compared with the index estimated by healthcare professionals
Measured: 1.12.2020-31.12.2021
Secondary outcomes (1)
Primary diagnosis
AI-driven automated analysis of diagnosis will be compared with the diagnosis estimated by healthcare professionals
Measured: 1.12.2020-31.12.2021
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Dec 2020
- Study start1 Dec 2020 (estimated)
- Primary completion1 Jun 2021 (estimated)
- Study completion1 Dec 2021 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Artificial IntelligenceStudies of the same condition, open and under way.
- More trials run by Turku 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
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Turku University Hospital
- Sponsor type
- Other government
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04661488
- Sponsor's protocol ID
- ETMK Dnro: 68 /1801/2020
- Status last verified
- Nov 2020
- First submitted
- 26 Nov 2020
- Last updated
- 10 Dec 2020
- 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 10 Dec 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Safety and Reliability of Artificial Intelligence Driven Symptom Assessment in Children and Adolescentes
- ClinicalTrials.gov ID
- NCT04661488
- Acronym / sponsor ID
- ETMK Dnro: 68 /1801/2020
- Conditions
- Artificial Intelligence, Triage
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Turku University HospitalOther government
- Study start
- 1 Dec 2020 (estimated)
- Primary completion
- 1 Jun 2021 (estimated)
- Study completion
- 1 Dec 2021 (estimated)
- First posted
- 10 Dec 2020 (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.
Digital health technologies (DHT) are increasingly developed to support healthcare systems around the world. However, they are frequently lacking evidence-based medicine and medical validation. There is considerable need in the western countries to allocate healthcare resources accurately and give the population detailed and reliable health information enabling to take greater responsibility for their health. Intelligent patient flow management system (IPFM, product name Klinik Frontline) is developed to meet these needs. In practice, IPFM is used for decision support in the triaging and diagnostic processes as well as automatizing the management of inflow of the patients. The core of the IPFM is a clinical artificial intelligence (AI), which utilizes a comprehensive medical database of clinical correlations generated by medical doctors.
The study population of this research consists of patients from the Paediatric Emergency Clinic of Turku University Hospital (TUH). Data will be gathered during 6 months of piloting, after which the results will be analysed. Anticipated number of patients to the study is minimum of 500 patients, with objective to be 1 000. When attending to the hospital, patients or their guardians will report their demographics, background information and symptoms using structured IPFM online form. Results obtained from IPFM are blinded from the healthcare professional and IPFM does not affect professional's clinical decision making. The data obtained from IPFM online form and clinical data from the emergency department and TUH will be analysed after the data collection. The main aim of the research is to validate the use of IPFM by evaluating the association of IPFM output with 1) urgency and severity of the conditions; and 2) actual diagnoses diagnosed by medical doctors. The main hypotheses of the research are that 1) IPFM is safe and sensitive in evaluating the urgency of the conditions of arriving patients at the emergency department and that 2) IPFM has sufficient correlation of differential diagnosis with actual diagnosis made by medical doctor.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Children and adolescence | – | Device: AI driven triage-system |
Who may qualify
- Ages
- 0 to 17 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child
- Study population
- Children and adolescence of all ages and comorbidities
Key inclusion criteria (1 listed)
- all patients at the emergency department with acute symptoms
Key exclusion criteria (1 listed)
- Emergency situation
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 driven triage-systemAI driven triage-system | Device | Children and adolescence |
Primary outcome
Emergency severity index (ESI)
Time frame: 1.12.2020-31.12.2021
AI-driven automated analysis of triage urgency (ESI index) will be compared with the index estimated by healthcare professionals
1 secondary outcome
Primary diagnosis
Time frame: 1.12.2020-31.12.2021
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Dec 2020.
- Individual participant dataSharing planned: No
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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.
Questions about this trial
What is this clinical trial studying?
An observational study of Artificial Intelligence and Triage, run by Turku University Hospital.
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: status not confirmed, as of the sponsor's last update on 10 Dec 2020. Status can change between updates, so confirm with the study contact before making plans.
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?
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?
Emergency severity index (ESI) (time frame: 1.12.2020-31.12.2021). 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 10 Dec 2020.