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Construction and Application of an Intelligent Response System for COVID-19 Voice Consultation in China: A
Jinming Shi1,2, Jinghong Gao1,2, Yunkai Zhai3
1The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Insights
An intelligent voice system for COVID-19 consultations was developed to reduce cross-infection risks. This system successfully screened suspected cases and optimized healthcare resource allocation during the pandemic.
Area of Science:
- Public Health
- Artificial Intelligence
- Health Informatics
Background:
- The COVID-19 pandemic necessitated novel approaches to healthcare delivery to minimize transmission risks in clinical settings.
- Public gatherings and close contact in healthcare facilities posed significant risks for COVID-19 exposure and cross-infection.
Purpose of the Study:
- To develop and deploy an intelligent voice response system for COVID-19 consultations.
- To provide users with response measure suggestions based on their information.
- To screen for suspected COVID-19 cases.
Main Methods:
- System architecture and core algorithms were designed based on business, user, and functional requirements.
- Operational processes were aligned with national health guidelines and expert experience in COVID-19 prevention and treatment.
- Retrospective analysis of qualitative and quantitative data from the system's real-world application was performed.
Main Results:
- The system demonstrated remote deployment, operational flexibility, and multi-dimensional reporting capabilities.
- The machine-learning model achieved a low Character Error Rate (CER) of 8.13%.
- By September 24, 2020, the system handled 12,264 calls, providing 11,788 consultations, with most users from Henan Province.
Conclusions:
- The intelligent response system proved effective in practical implementation.
- Voice consultation systems can optimize healthcare resource allocation and improve service efficiency.
- Such systems contribute to cost savings and the protection of vulnerable populations.
Abstract:
Background: The outbreak of novel coronavirus disease 2019 (COVID-19) has led to tremendous individuals visit medical institutions for healthcare services. Public gatherings and close contact in clinics and emergency departments may increase the exposure and cross-infection of COVID-19. Objectives: The purpose of this study was to develop and deploy an intelligent response system for COVID-19 voice consultation, to provide suggestions of response measures based on actual information of users, and screen COVID-19 suspected cases. Methods: Based on the requirements analysis of business, user, and function, the physical architecture, system architecture, and core algorithms are designed and implemented. The system operation process is designed according to guidance documents of the National Health Commission and the actual experience of prevention, diagnosis and treatment of COVID-19. Both qualitative (system construction) and quantitative (system application) data from the real-world healthcare service of the system were retrospectively collected and analyzed. Results: The system realizes the functions, such as remote deployment and operations, fast operation procedure adjustment, and multi-dimensional statistical report capability. The performance of the machine-learning model used to develop the system is better than others, with the lowest Character Error Rate (CER) 8.13%. As of September 24, 2020, the system has received 12,264 times incoming calls and provided a total of 11,788 COVID-19-related consultation services for the public. Approximately 85.2% of the users are from Henan Province and followed by Beijing (2.5%). Of all the incoming calls, China Mobile contributes the largest proportion (66%), while China Unicom and China Telecom are accounted for 23% and 11%. For the time that users access the system, there is a peak period in the morning (08:00-10:00) and afternoon (14:00-16:00), respectively. Conclusions: The intelligent response system has achieved appreciable practical implementation effects. Our findings reveal that the provision of inquiry services through an intelligent voice consultation system may play a role in optimizing the allocation of healthcare resources, improving the efficiency of medical services, saving medical expenses, and protecting vulnerable groups.
