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.
Highlights
In late December 2019, a cluster of pneumonia cases caused by a new severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) were firstly reported in Wuhan, Hubei Province, China [1, 2]
Through natural language processing (NLP) technology, it dynamically adjusts the follow-up questions that need to be confirmed according to the different options selected by the user and give corresponding response opinions depending on actual situations, which can quickly screen out COVID-19 suspected cases and offer specific suggestions for further actions
Due to the incompleteness of online consultation, the system is not connected to any healthcare systems, once a COVID19 case is suspected, she/he will be recommended to go to the nearest COVID-19 designated hospital for further confirmation and treatment immediately
Summary
In late December 2019, a cluster of pneumonia cases caused by a new severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) were firstly reported in Wuhan, Hubei Province, China [1, 2]. The COVID-19 pandemic has led to a large number of healthy, suspected, or asymptomatic-infected individuals visit medical institutions for diagnosis or treatment, resulting in a shortage of healthcare resources, and lots of people crowd in clinics and emergency departments [6, 7]. The public gatherings may increase the infection risk of healthy people and medical staff. The COVID-19 outbreak has caused a sharp increase in the demand for healthcare consultation services, far exceeding the capacity that medical institutions can bear [8, 9]. 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. Basic Service Layer This layer is in charge of functions, such as user terminal management, outgoing and incoming call tasks management, call center control, API management, and security module, which provides services directly to users and engineers of system operation
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