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Safety and Reliability of Artificial Intelligence Driven Symptom Assessment in Children and Adolescentes

Safety and Reliability of Artificial Intelligence Driven Symptom Assessment in Children and Adolescentes

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT04661488
Enrollment
1000
Registered
2020-12-10
Start date
2020-12-01
Completion date
2021-12-01
Last updated
2020-12-10

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Artificial Intelligence, Triage

Brief 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. 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.

Interventions

DEVICEAI driven triage-system

AI driven triage-system

Sponsors

Turku University Hospital
Lead SponsorOTHER_GOV

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
0 Years to 17 Years
Healthy volunteers
Yes

Inclusion criteria

* all patients at the emergency department with acute symptoms

Exclusion criteria

* Emergency situation

Design outcomes

Primary

MeasureTime frameDescription
Emergency severity index (ESI)1.12.2020-31.12.2021AI-driven automated analysis of triage urgency (ESI index) will be compared with the index estimated by healthcare professionals

Secondary

MeasureTime frameDescription
Primary diagnosis1.12.2020-31.12.2021AI-driven automated analysis of diagnosis will be compared with the diagnosis estimated by healthcare professionals

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026