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Clinical Trial Details — Status: Not yet recruiting

Administrative data

NCT number NCT04661488
Other study ID # ETMK Dnro: 68 /1801/2020
Secondary ID
Status Not yet recruiting
Phase
First received
Last updated
Start date December 1, 2020
Est. completion date December 1, 2021

Study information

Verified date November 2020
Source Turku University Hospital
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical 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. 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.


Recruitment information / eligibility

Status Not yet recruiting
Enrollment 1000
Est. completion date December 1, 2021
Est. primary completion date June 1, 2021
Accepts healthy volunteers Accepts Healthy Volunteers
Gender All
Age group N/A to 17 Years
Eligibility Inclusion Criteria: - all patients at the emergency department with acute symptoms Exclusion Criteria: - Emergency situation

Study Design


Related Conditions & MeSH terms


Intervention

Device:
AI driven triage-system
AI driven triage-system

Locations

Country Name City State
n/a

Sponsors (1)

Lead Sponsor Collaborator
Turku University Hospital

Outcome

Type Measure Description Time frame Safety issue
Primary Emergency severity index (ESI) AI-driven automated analysis of triage urgency (ESI index) will be compared with the index estimated by healthcare professionals 1.12.2020-31.12.2021
Secondary Primary diagnosis AI-driven automated analysis of diagnosis will be compared with the diagnosis estimated by healthcare professionals 1.12.2020-31.12.2021
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