Key result
Public health data-driven decision support matches questionnaires for detecting obstructive ventilation dysfunction.
Why the study?
Improving the ability to identify patients with COPD in primary medical institutions is important to prevent and treat the disease.
Does a clinical decision support system improve the identification of COPD patients in primary health institutions compared to existing methods?
Observational (n=1,875)
No
Does a clinical decision support system improve the identification of COPD patients in primary health institutions compared to existing methods?
A proposed clinical decision support system using big data and AI may provide a convenient and effective way to identify COPD patients in primary care settings.
Chronic obstructive pulmonary disease (COPD) is a serious chronic respiratory disease. Improving the ability to identify patients with COPD in primary medical institutions is important to prevent and treat the disease. With the continuous development of medical digitization, the application of big data informatization in the medical and health fields has become possible. Recently, applying innovative technologies such as big data analysis, machine learning, and artificial intelligence-assisted decision-making in the medical field has become an interdisciplinary research hotspot. Based on the identification and diagnosis of COPD in the high-risk population, this study proposes a convenient and effective clinical decision support system to help identify patients with COPD in primary health institutions. The results of the preliminary experiments show that the proposed method is convenient and effective compared with the existing methods.
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Lin et al. (2022) conducted an observational in Chronic Obstructive Pulmonary Disease (COPD) (n=1,875). Clinical Decision Support System (CDSS) based on public health data vs. COPD-SQ field investigation screening was evaluated on Area under the receiver operating characteristic curve (AUC) for detecting obstructive ventilation dysfunction (95% CI 0.504-0.725). A clinical decision support system based on public health data achieved an AUC of 0.615 for detecting obstructive ventilation dysfunction, demonstrating comparable performance to traditional questionnaire screening.
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