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

NCT04661488 | Unknown

• 1 other identifier: ETMK Dnro: 68 /1801/2020
• Study Type: observational
• Target: 1,000
• Locations: 0 countries
• Sites: N/A

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.

Conditions

Artificial Intelligence; Triage

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (1)

• Primary diagnosis

  1.12.2020-31.12.2021

Study Arms (1)

Children and adolescence

Device: AI driven triage-system

Interventions

AI driven triage-system DEVICE

AI driven triage-system

Children and adolescence

Eligibility Criteria

• Age: 0 Years - 17 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17)
• Sampling Method: Probability Sample

Study Population

Children and adolescence of all ages and comorbidities

You may qualify if:

• all patients at the emergency department with acute symptoms

You may not qualify if:

• Emergency situation

Sponsors & Collaborators

• Turku University Hospital: lead

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER GOV
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 26, 2020
• First Posted: December 10, 2020
• Study Start: December 1, 2020
• Primary Completion: June 1, 2021
• Study Completion: December 1, 2021
• Last Updated: December 10, 2020

Record last verified: 2020-11

Data Sharing

• IPD Sharing: Will not share