Stacking-BERT model for Chinese medical procedure entity normalization.

Medical procedure entity normalization is an important task to realize medical information sharing at the semantic level; it faces main challenges such as variety and similarity in real-world practice. Although deep learning-based methods have been successfully applied to biomedical entity normalization, they often depend on traditional context-independent word embeddings, and there is minimal research on medical entity recognition in Chinese Regarding the entity normalization task as a sentence pair classification task, we applied a three-step framework to normalize Chinese medical procedure terms, and it consists of dataset construction, candidate concept generation and candidate concept ranking. For dataset construction, external knowledge base and easy data augmentation skills were used to increase the diversity of training samples. For candidate concept generation, we implemented the BM25 retrieval method based on integrating synonym knowledge of SNOMED CT and train data. For candidate concept ranking, we designed a stacking-BERT model, including the original BERT-based and Siamese-BERT ranking models, to capture the semantic information and choose the optimal mapping pairs by the stacking mechanism. In the training process, we also added the tricks of adversarial training to improve the learning ability of the model on small-scale training data. Based on the clinical entity normalization task dataset of the 5th China Health Information Processing Conference, our stacking-BERT model achieved an accuracy of 93.1%, which outperformed the single BERT models and other traditional deep learning models. In conclusion, this paper presents an effective method for Chinese medical procedure entity normalization and validation of different BERT-based models. In addition, we found that the tricks of adversarial training and data augmentation can effectively improve the effect of the deep learning model for small samples, which might provide some useful ideas for future research.

Usage and perceptions of telemedicine among health care professionals in China.

OBJECTIVE: To explore health care professionals' usage and perceptions of telemedicine, including their experience, evaluation and attitude towards telemedicine, and the factors affecting their satisfaction, using intention, and willingness to recommend telemedicine. METHODS: A nationwide and web-based survey was conducted. Statistical charts were used to describe the usage and perceptions of telemedicine by health care professionals. And the ordinal logistic regression was applied to analyze the influencing factors. RESULTS: A total of 1349 valid questionnaires were collected. In the survey, 74.0 % of Chinese health care professionals used telemedicine once a week. The average duration of participating in telemedicine services lasted mainly 11-30 min (64.0 %). More than half (52.5 %) of participants' hospitals adopted fee subsidy measures, followed by the award for excellent evaluation (36.4 %). The majority (92.5 %) believed that telemedicine could reduce patient referral rates. More than 95 % were satisfied with telemedicine, were willing to continue, and would recommend telemedicine for patients, respectively. The main problems existing were: incomplete system equipment, insufficient timeliness, high costs, inability to reimburse medical insurance, non-standardized medical records, inadequate publicity, cumbersome processes and long waiting time. Ordinal logistic regression showed that too short service duration (≤10 min) could significantly reduce medical professionals' overall satisfaction, using intention and willingness to recommend (P = 0.026, P = 0.017 and P = 0.040 respectively), while the convenience of the operating system had a significant positive impact (P = 0.005, P = 0.003 and P = 0.001 respectively). And cost subsidy incentive and the promotion of professional titles could significantly enhance their overall satisfaction(P = 0.006, P = 0.030), using intention (P = 0.011, P = 0.001), and willingness to recommend (P = 0.040, P = 0.004). CONCLUSIONS: The usage of telemedicine is relatively insufficient. Most health care professionals have fairly positive opinions toward telemedicine. The most mentioned issues and expectations for telemedicine can be roughly divided into four categories: infrastructure, service process, charge and cost, and popularity. It is possible to improve their evaluation by improving system convenience, enhancing service punctuality, avoiding excessively short service duration and adopting incentive measures.

Telemedicine Is Becoming an Increasingly Popular Way to Resolve the Unequal Distribution of Healthcare Resources: Evidence From China.

Background: Few studies focused on the general situation of telemedicine in China. Objectives: The purpose of this review is to investigate telemedicine in China, from the aspects of necessity, history, scale, and operation procedure, to improve the further development and implementation of telemedicine service. Methods: A literature search for peer-reviewed studies was conducted using the primary electronic databases. Additional documents from the official websites of Chinese government departments involved telemedicine was also collected. We extracted telemedicine related information focused on China from the final retrieved materials, and the general situation of telemedicine was drawn. Results: In China, telemedicine offers a feasible solution to the unequal allocation of healthcare resources, which makes telemedicine increasingly become an important alternative to close the gap between rural and urban in the capability and quality of medical services. China initiated telemedicine in the late 1980s. In 2018, China's telemedicine network has covered more than 3,000 hospitals across the country. As of 2019, almost all of the 31 provinces and municipalities in mainland have established regional telemedicine centers, and the market size of telemedicine reached about USD 2.68 billion. Based on the telemedicine network, remote rural patients can apply for healthcare services of top-tier urban hospitals through local county-level medical institutions. Conclusions: Through improving the capacity, quality, and efficiency of healthcare in underserved areas, and reducing the unequal distribution of medical resources, telemedicine can help solve the problems of the difficulty and high cost to access to medical services in China.

A Study of Mobile Medical App User Satisfaction Incorporating Theme Analysis and Review Sentiment Tendencies.

Mobile medicine plays a significant role in optimizing medical resource allocation, improving medical efficiency, etc. Identifying and analyzing user concern elements from active online reviews can help to improve service quality and enhance product competitiveness in a targeted manner. Based on the latent Dirichlet allocation (LDA) topic model, this study carries out a topic-clustering analysis of users' online comments and builds an evaluation index system of mobile medical users' satisfaction by using grounded theory. After that, the evaluation information of users is obtained through an emotional analysis of online comments. Then, in order to fully consider the uncertainty of decision makers' evaluations, rough number theory and the fuzzy comprehensive evaluation method are used to confirm the conclusions of experts and indicators and to evaluate the satisfaction of mobile medical users. The empirical results show that users are satisfied with the service quality and content quality of mobile medical apps, and less satisfied with the management and technology qualities. Therefore, this paper puts forward corresponding countermeasures from the aspects of strengthening safety supervision, strengthening scientific research, strengthening information audit, attaching importance to service quality management and strengthening doctors' sense of gain. This study uses text mining for index extraction and satisfaction analysis of online reviews to quantitatively evaluate user satisfaction with mobile medical apps, providing a reference for the improvement of mobile medical apps. However, there are still certain shortcomings in the current study, and subsequent studies can screen false reviews for a deeper study of online review information.

Self-Adaptive Telemedicine Specialist Recommendation Considering Specialist Activity and Patient Feedback.

Purpose: With the rapid development of medical informatization, information overload and asymmetry have become major obstacles that limit patients' ability to find appropriate telemedicine specialists. Although doctor recommendation methods have been proposed, they fail to address data sparsity and cold-start issues, and electronic medical records (EMRs), patient preferences, potential interest of service providers and the changes over time are largely under-explored. Therefore, this study develops a self-adaptive telemedicine specialist recommendation method that incorporates specialist activity and patient utility feedback from the perspective of privacy protection to fill the research gaps. Methods: First, text vectorization, view similarity and probabilistic topic model are used to construct the patient and specialist feature models based on patients' EMRs and specialists' long- and short-term knowledge backgrounds, respectively. Second, the recommended specialist candidate set and recommendation index are obtained based on the similarity between patient features. Then, the specialist long-term knowledge feature model is used to update the newly registered specialist recommendation index and the recommended specialist candidate set to overcome the data sparsity and cold-start issues, and the specialist short-term knowledge feature model is adopted to extend the recommended specialist candidate set at the semantic level. Finally, we introduce the specialists' activity and patients' perceived utility feedback mechanism to construct a closed-loop adjusted and optimized specialist recommendation method. Results: An empirical study was conducted integrating EMRs of telemedicine patients from the National Telemedicine Center of China and specialists' profiles and ratings from an online healthcare platform. The proposed method successfully recommended relevant and active telemedicine specialists to the target patient, and increased the recommended opportunities for newly registered specialists to some extent. Conclusions: The proposed method emphasizes the adaptability and acceptability of the recommended results while ensuring their accuracy and relevance. Specialists' activity and patients' perceived utility jointly contribute to the acceptability of recommended results, and the recommendation strategy achieves the organic fusion of the two. Several comparative experiments demonstrate the effectiveness and operability of the hybrid recommendation strategy under the premise of data sparsity and privacy protection, enabling effective matching of patients' demand and service providers' capabilities, and providing beneficial insights for data-driven telemedicine services.

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.

Spatio-temporal distribution characteristics of COVID-19 in China: a city-level modeling study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) has become a pandemic. Few studies have been conducted to investigate the spatio-temporal distribution of COVID-19 on nationwide city-level in China. OBJECTIVE: To analyze and visualize the spatiotemporal distribution characteristics and clustering pattern of COVID-19 cases from 362 cities of 31 provinces, municipalities and autonomous regions in mainland China. METHODS: A spatiotemporal statistical analysis of COVID-19 cases was carried out by collecting the confirmed COVID-19 cases in mainland China from January 10, 2020 to October 5, 2020. Methods including statistical charts, hotspot analysis, spatial autocorrelation, and Poisson space-time scan statistic were conducted. RESULTS: The high incidence stage of China's COVID-19 epidemic was from January 17 to February 9, 2020 with daily increase rate greater than 7.5%. The hot spot analysis suggested that the cities including Wuhan, Huangshi, Ezhou, Xiaogan, Jingzhou, Huanggang, Xianning, and Xiantao, were the hot spots with statistical significance. Spatial autocorrelation analysis indicated a moderately correlated pattern of spatial clustering of COVID-19 cases across China in the early phase, with Moran's I statistic reaching maximum value on January 31, at 0.235 (Z = 12.344, P = 0.001), but the spatial correlation gradually decreased later and showed a discrete trend to a random distribution. Considering both space and time, 19 statistically significant clusters were identified. 63.16% of the clusters occurred from January to February. Larger clusters were located in central and southern China. The most likely cluster (RR = 845.01, P < 0.01) included 6 cities in Hubei province with Wuhan as the centre. Overall, the clusters with larger coverage were in the early stage of the epidemic, while it changed to only gather in a specific city in the later period. The pattern and scope of clusters changed and reduced over time in China. CONCLUSIONS: Spatio-temporal cluster detection plays a vital role in the exploration of epidemic evolution and early warning of disease outbreaks and recurrences. This study can provide scientific reference for the allocation of medical resources and monitoring potential rebound of the COVID-19 epidemic in China.

Application of Telemedicine Services Based on a Regional Telemedicine Platform in China From 2014 to 2020: Longitudinal Trend Analysis.

BACKGROUND: Telemedicine that combines information technology and health care augments the operational model of traditional medical services and brings new opportunities to the medical field. China promotes telemedicine with great efforts, and its practices in the deployment of telemedicine platforms and delivery of services have become important references for the research and development in this field. OBJECTIVE: Our work described in this paper focuses on a regional telemedicine platform that was built in 2014. We analyzed the system design scheme and remote consultations that were conducted via the system to understand the deployment and service delivery processes of a representative telemedicine platform in China. METHODS: We collected information on remote consultations conducted from 2015 to 2020 via the regional telemedicine platform that employs a centralized architectural system model. We used graphs and statistical methods to describe the changing trends of service volume of remote consultation, geographical and demographic distribution of patients, and waiting time and duration of consultations. The factors that affect consultation duration and patient referral were analyzed by multivariable linear regression models and binary logistic regression models, respectively. The attitudes toward telemedicine of 225 medical practitioners and 225 patients were collected using the snowball sampling method. RESULTS: The regional telemedicine platform covers all levels of medical institutions and hospitals in all 18 cities of Henan Province as well as some interprovince hospitals. From 2015 to 2020, 103,957 remote medical consultations were conducted via the platform with an annual increasing rate of 0.64%. A total of 86.64% (90,069/103,957) of medical institutions (as clients) that applied for remote consultations were tier 1 or 2 and from less-developed regions; 65.65% (68,243/103,945) of patients who applied for remote consultations were aged over 50 years. The numbers of consultations were high for departments focusing in the treatment of chronic diseases such as neurology, respiratory medicine, and oncology. The invited experts were mainly experienced doctors with senior professional titles. Year of consultation, tier of hospital, consultation department, and necessity of patient referral were the main factors affecting the duration of consultations. In surveys, we found that 60.4% (136/225) of medical practitioners and 53.8% (121/225) of patients had high satisfaction and believed that telemedicine is of vital importance for the treatment of illness. CONCLUSIONS: The development of telemedicine in China shows a growing trend and provides great benefits especially to medical institutions located in less developed regions and senior citizens who have less mobility. Cases of remote consultations are mainly for chronic diseases. At present, the importance and necessity of telemedicine are well recognized by both patients and medical practitioners. However, the waiting time needs to be further reduced to improve the efficiency of remote medical services.

Effectiveness of a Cloud-Based Telepathology System in China: Large-Sample Observational Study.

BACKGROUND: Whole-slide imaging allows the entire slide to be viewed in a manner that simulates microscopy; therefore, it is widely used in telepathology. However, managing the large digital files needed for whole-slide imaging is difficult. To solve this problem, we set up the Chinese National Cloud-Based Telepathology System (CNCTPS). CNCTPS has been running for more than 4 years and has accumulated a large amount of data. OBJECTIVE: The main purpose of this study was to comprehensively evaluate the effectiveness of the CNCTPS based on a large sample. The evaluation indicators included service volume, turnaround time, diagnosis accuracy, and economic benefits. METHODS: Details of 23,167 cases submitted to the CNCTPS from January 2016 to December 2019 were collected to analyze the service volume, turnaround time, and economic benefits. A total of 564 patients who visited the First Affiliated Hospital of Zhengzhou University and obtained final diagnoses were followed up to analyze the diagnostic accuracy of the CNCTPS. RESULTS: From 2016 to 2019, the service volume of the CNCTPS increased from 2335 to 9240, and the number of participating hospitals increased from 60 to 74. Consultation requests from county-level hospitals accounted for 86.57% (20,287/23,167). A total of 17,495 of 23,167 cases (75.52%) were confirmed, including 12,088 benign lesions, 5217 malignant lesions, and 190 borderline lesions. Of the cases, 3.85% (893/23,167) failed to be diagnosed for reasons such as poor slice quality and incomplete sampling. The median turnaround time was 16.93 hours and was shortened yearly (between 2018 and 2019: adjusted P=.01; other groups: adjusted P<.001); 82.88% cases were diagnosed in 48 hours. There was a discrepancy between the diagnosis and final diagnosis for 11 cases, including 4 false-positive cases and 7 false-negative cases. The sensitivity and specificity were 97.66% and 98.49%, respectively. The diagnostic accuracy of the system was 98.05%, with no statistical difference from the final diagnosis in the hospital (P=.55). By using this system, a total of US $300,000 was saved for patients every year. CONCLUSIONS: The novel cloud-based telepathology system has the potential to relieve the shortage of pathologists in primary hospitals. It can also simultaneously reduce medical costs for patients in China. It should, therefore, be further promoted to enhance the efficiency, quantity, and quality of telepathology diagnoses.