A study on the AI-based online triage model for hospitals in sustainable smart city

An in-depth analysis of the artificial intelligence online triage of hospitals in sustainable smart city is conducted, and a model of factors influencing patients' choice of medical institutions is constructed based on the theory of planned behavior and Anderson's health service utilization model. Based on the questionnaire and the model, a structural equation model is constructed to explore and analyze patients' intention to seek medical treatment at the primary level and that at large hospitals respectively, revealing the mechanism of different influencing factors on patients' intention to seek medical treatment at the primary level and that at large hospitals, and providing a reference for guiding patients to seek medical treatment at the primary level and choosing medical institutions nationally. (1) This paper reveals the influencing mechanism of patients' medical treatment behavior, proposes a patient-centered medical institution selection method, and explores the strategic choices affecting patients' primary care, which provides an important reference for establishing a scientific and reasonable hierarchical diagnosis and treatment model. (2) Meanwhile, how to provide precise medical services to patients by acquiring and fusing multi-source and multi-modal mass data will become one of the principles guarantees of a harmonious society in the future. (3) Compared with other studies, the accuracy and efficiency of the research results of the online hierarchical diagnosis and treatment model in this paper are greatly improved. The current results are also fully applicable to practical applications. In the future, we will conduct in-depth research on the general testability of the model. analysis. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study reveals the influencing mechanism of patients' medical treatment behavior, proposes a patient-centered medical institution selection method, and explores strategic choices affecting patients' primary care, providing an important reference for establishing a scientific and reasonable hierarchical diagnosis and treatment model. Moreover, how to provide precise medical services to patients by acquiring and fusing multi-source and multi-modal mass data will be crucial for a harmonious society in the future. The research results of the online hierarchical diagnosis and treatment model in this paper show significantly improved accuracy and efficiency compared to other studies, and they are fully applicable to practical applications, with plans for further in-depth research on general testability of the model in the future.