Implementation and Application of Telemedicine in China: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Telemedicine has been used widely in China and has benefited a large number of patients, but little is known about the overall development of telemedicine. OBJECTIVE: The aim of this study was to perform a national survey to identify the overall implementation and application of telemedicine in Chinese tertiary hospitals and provide a scientific basis for the successful expansion of telemedicine in the future. METHODS: The method of probability proportionate to size sampling was adopted to collect data from 161 tertiary hospitals in 29 provinces, autonomous regions, and municipalities. Charts and statistical tests were applied to compare the development of telemedicine, including management, network, data storage, software and hardware equipment, and application of telemedicine. Ordinal logistic regression was used to analyze the relationship between these factors and telemedicine service effect. RESULTS: Approximately 93.8% (151/161) of the tertiary hospitals carried out telemedicine services in business-to-business mode. The most widely used type of telemedicine network was the virtual private network with a usage rate of 55.3% (89/161). Only a few tertiary hospitals did not establish data security and cybersecurity measures. Of the 161 hospitals that took part in the survey, 100 (62.1%) conducted remote videoconferencing supported by hardware instead of software. The top 5 telemedicine services implemented in the hospitals were teleconsultation, remote education, telediagnosis of medical images, tele-electrocardiography, and telepathology, with coverage rates of 86.3% (139/161), 57.1% (92/161), 49.7% (80/161), 37.9% (61/161), and 33.5% (54/161), respectively. The average annual service volume of teleconsultation reached 714 cases per hospital. Teleconsultation and telediagnosis were the core charging services. Multivariate analysis indicated that the adoption of direct-to-consumer mode (P=.003), support from scientific research funds (P=.01), charging for services (P<.001), number of medical professionals (P=.04), network type (P=.02), sharing data with other hospitals (P=.04), and expertise level (P=.03) were related to the effect of teleconsultation. Direct-to-consumer mode (P=.01), research funding (P=.01), charging for services (P=.01), establishment of professional management departments (P=.04), and 15 or more instances of remote education every month (P=.01) were found to significantly influence the effect of remote education. CONCLUSIONS: A variety of telemedicine services have been implemented in tertiary hospitals in China with a promising prospect, but the sustainability and further standardization of telemedicine in China are still far from accomplished.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".