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Clinical Trials/NCT04661488
NCT04661488
Unknown
Not Applicable

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

Turku University Hospital0 sites1,000 target enrollmentDecember 1, 2020

Overview

Phase
Not Applicable
Intervention
Not specified
Conditions
Artificial Intelligence
Sponsor
Turku University Hospital
Enrollment
1000
Primary Endpoint
Emergency severity index (ESI)
Last Updated
5 years ago

Overview

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.

Registry
clinicaltrials.gov
Start Date
December 1, 2020
End Date
December 1, 2021
Last Updated
5 years ago
Study Type
Observational
Sex
All

Investigators

Responsible Party
Sponsor

Eligibility Criteria

Inclusion Criteria

  • all patients at the emergency department with acute symptoms

Exclusion Criteria

  • Emergency situation

Outcomes

Primary Outcomes

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

Secondary Outcomes

  • Primary diagnosis(1.12.2020-31.12.2021)

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