Artificial Intelligence Promotes Shared Decision-making Through Recommending Tests to Febrile Pediatric Outpatients

Overview

Journal World J Emerg Med

Specialty Emergency Medicine

Date 2023 Mar 13

PMID 36911055

Authors

Wei-Hua Li

Bin Dong

Han-Song Wang

Jia-Jun Yuan

Han Qian

Ling-Ling Zheng

Xu-Lin Lin

Zhao Wang

Shi-Jian Liu

Bo-Tao Ning

DanTian

Lie-Bin Zhao

Affiliations

Soon will be listed here.

Abstract

Background: To promote the shared decision-making (SDM) between patients and doctors in pediatric outpatient departments, this study was designed to validate artificial intelligence (AI) -initiated medical tests for children with fever.

Methods: We designed an AI model, named Xiaoyi, to suggest necessary tests for a febrile child before visiting a pediatric outpatient clinic. We calculated the sensitivity, specificity, and F1 score to evaluate the efficacy of Xiaoyi's recommendations. The patients were divided into the rejection and acceptance groups. Then we analyzed the rejected examination items in order to obtain the corresponding reasons.

Results: We recruited a total of 11,867 children with fever who had used Xiaoyi in outpatient clinics. The recommended examinations given by Xiaoyi for 10,636 (89.6%) patients were qualified. The average F1 score reached 0.94. A total of 58.4% of the patients accepted Xiaoyi's suggestions (acceptance group), and 41.6% refused (rejection group). Imaging examinations were rejected by most patients (46.7%). The tests being time-consuming were rejected by 2,133 patients (43.2%), including rejecting pathogen studies in 1,347 patients (68.5%) and image studies in 732 patients (31.8%). The difficulty of sampling was the main reason for rejecting routine tests (41.9%).

Conclusion: Our model has high accuracy and acceptability in recommending medical tests to febrile pediatric patients, and is worth promoting in facilitating SDM.

Citing Articles

Constructing and implementing a performance evaluation indicator set for artificial intelligence decision support systems in pediatric outpatient clinics: an observational study.

Wang Y, Fu W, Zhang Y, Wang D, Gu Y, Wang W Sci Rep. 2024; 14(1):14482.

PMID: 38914707 PMC: 11196575. DOI: 10.1038/s41598-024-64893-w.

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