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

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 Overview

• Status: Unknown
• Conditions: Artificial Intelligence; Triage
• Intervention / Treatment: Device: AI driven triage-system

• Study Type: Observational
• Enrollment (Anticipated): 1000

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 1 second to 17 years (CHILD)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

• Sampling Method: Probability Sample

Study Population

Children and adolescence of all ages and comorbidities

Description

Inclusion Criteria:

• all patients at the emergency department with acute symptoms

Exclusion Criteria:

• Emergency situation

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Children and adolescence	Device: AI driven triage-system; AI driven triage-system

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Primary diagnosis	AI-driven automated analysis of diagnosis will be compared with the diagnosis estimated by healthcare professionals	1.12.2020-31.12.2021

Collaborators and Investigators

• Sponsor: Turku University Hospital

Study record dates

Study Major Dates

• Study Start (ANTICIPATED): December 1, 2020
• Primary Completion (ANTICIPATED): June 1, 2021
• Study Completion (ANTICIPATED): December 1, 2021

Study Registration Dates

• First Submitted: November 26, 2020
• First Submitted That Met QC Criteria: December 3, 2020
• First Posted (ACTUAL): December 10, 2020

Study Record Updates

• Last Update Posted (ACTUAL): December 10, 2020
• Last Update Submitted That Met QC Criteria: December 3, 2020
• Last Verified: November 1, 2020

More Information

Terms related to this study

• Other Study ID Numbers: ETMK Dnro: 68 /1801/2020

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No