Enhancing efficiency and capacity of telehealth services with intelligent triage: a bidirectional LSTM neural network model employing character embedding.

BACKGROUND: The widespread adoption of telehealth services necessitates accurate online department selection based on patient medical records, a task requiring significant medical knowledge. Incorrect triage results in considerable time wastage for both patients and medical professionals. To address this, we propose an intelligent triage model based on a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network with character embedding to enhance the efficiency and capacity of telehealth services. METHODS: We gathered a 1.3 GB medical dataset comprising 200,000 records, each including medical history, physical examination data, and other pertinent information found on the electronic medical record homepage. Following data preprocessing, a clinical corpus was established to train character embeddings with a medical context. These character embeddings were then utilized to extract features from patient chief  complaints, and a 2-layer Bi-LSTM neural network was trained to categorize these complaints, enabling intelligent triage for telehealth services. RESULTS: 60,000 chief complaint-department data pairs were extracted from clinical corpus and divided into the training, validation, and test sets of 42,000, 9,000, and 9,000, respectively. The character embedding based Bi-LSTM neural network achieved a macro-precision of 85.50% and an F1 score of 85.45%. CONCLUSION: The telehealth triage model developed in this study demonstrates strong implementation outcomes and significantly improves the efficiency and capacity of telehealth services. Character embedding outperforms word embedding, and future work will incorporate additional features such as patient age and gender into the chief complaint feature to future enhance model performance.

Implementation, service effectiveness and satisfaction with teleconsultation services in China during the COVID-19 pandemic: from the Perspective of Primary Health Care Professionals.

OBJECTIVE: This study aims to evaluate satisfaction and service effectiveness of primary hospital physicians participating in the National Telemedicine Center of China during the COVID-19 period, and to identify potential improvement suggestions. METHODS: An online questionnaire was developed to assess the impact and satisfaction of teleconsultation services. A teleconsultation manager from each of the 98 hospitals randomly invited the medical staff involved in teleconsultation to complete the online questionnaire. RESULTS: A total of 379 health care professionals responded to the online questionnaire, with a mean age of 36.74 years. Out of these respondents, 95.5% had a positive attitude towards teleconsultation during the epidemic. Only 6.6% believed that teleconsultation systems were not useful in preventing and controlling the COVID-19 pandemic. Those respondents who were very satisfied with teleconsultation participated in it 1.81 times per week averagely. Factors related to satisfaction included weekly participation frequency（P=.003）, patient data quality(P=.023), equipment operation proficiency（P=.006）, audio and video clarity and smoothness（P=.004, P=.020）, environmental satisfaction（P=.032）, and incentive measures of title promotion（P=.003）. The main challenges in teleconsultation were the lack of understanding of medical staff and the public, insufficiently advanced software and hardware equipment, and the lack of optimization of service processes. CONCLUSIONS: Primary hospital doctors demonstrate high satisfaction levels, suggesting that teleconsultation could be an effective tool for patients seeking medical care in areas under lockdown during the COVID-19 pandemic. The primary barriers to teleconsultation include lack of public understanding and unadvanced equipment. These findings should inform future efforts to establish regional telemedicine programs in the post-COVID-19 era.

Construction and Application of an Intelligent Response System for COVID-19 Voice Consultation in China: A Retrospective Study.

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.