跳至主要内容
临床试验/NCT04661488
NCT04661488Unknown不适用

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

Turku University Hospital0 个研究点目标入组 1,000 人开始时间: 2020年12月1日最近更新:
适应症

试验速览

阶段
不适用
入组人数
1,000
主要终点
Emergency severity index (ESI)

研究概览

简要总结

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.

研究设计

研究类型
Observational
观察模型
Cohort
时间视角
Prospective

入排标准

年龄范围
0 Years 至 17 Years(Child)
性别
All
接受健康志愿者

入选标准

  • all patients at the emergency department with acute symptoms

排除标准

  • Emergency situation

结局指标

主要结局

Emergency severity index (ESI)

时间窗: 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

次要结局

  • Primary diagnosis(1.12.2020-31.12.2021)

研究者

申办方类型
Other Gov
责任方
Sponsor

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