Using an Internet-Based Hospital to Address Maldistribution of Health Care Resources in Rural Areas of Guangdong Province, China: Retrospective and Descriptive Study
Bibliographic record
Abstract
BACKGROUND: Health care maldistribution is a long-term problem in China. Telemedicine is an efficient way to deliver medical resources to remote areas; however, there are few studies on the effectiveness and challenges in providing health care from rural to urban areas in China. OBJECTIVE: The objective was to describe the effectiveness and challenges of telemedicine for providing health care from Guangzhou to rural areas in Guangdong Province. METHODS: We designed surveys and conducted them immediately after the consultation and 2-4 weeks later. Data were collected from June 2015 to May 2016 including patients' demographic features, patient satisfaction, medicine effect, patient compliance, acceptability of prescription expenses, patients' desire to revisit the department, the top 10 diseases, and self-reported difficulties in telemedicine experience. The monthly average prescription expense was described using a line chart. The monthly consultation and prescription, as well as monthly prescriptions of Western medicines and herbs, were described using a bar chart. RESULTS: Women comprised majority (45,386/67,740, 67.00%) of participants and men comprised the minority (22,354/67,740, 33.00%). The top 3 diseases were upper respiratory diseases (12,371/36,311, 34.07%), laryngopharyngitis (4938/36,311, 13.60%), and menstrual disorders (4669/36,311, 12.86%). The monthly prescription for Western medicine was much more than that for Chinese herbs. The annual average medicine expense per prescription was 62.9 ¥. The participants' perception of expense was acceptable (8775/12,450, 70.48%), mostly acceptable (2497/12,450, 20.01%), accepted but somewhat expensive (980/12,450, 7.9%), and unacceptable because of high cost (198/12,450, 1.6%). The surveys on patient satisfaction demonstrated very satisfied (55,687/67,740, 82.21%), satisfied (5891/67,740, 8.70%), basic satisfaction (3561/67,740, 5.26%), dissatisfaction (1454/67,740, 2.15%), and no comment (1147/67,740, 1.69%). Participants reported their treatment outcome as follows: full recovery (5008/12,450, 40.22%), recovering (4873/12,450, 39.14%), no effect (2153/12,450, 17.29%), or worsening (416/12,450, 3.3%). Approximately 89.01% (20,240/22,740) of participants will revisit the department, whereas 10.99% (2500/22,740) will not. Most patients complied with the doctors' advice completely (5430/10,290, 52.77%), whereas the rest reported partial compliance (3684/10,290, 35.80%) or no compliance at all (1176/10,290, 11.43%). The participants reported poor computer skills (4980/22,740, 21.90%), transportation inconvenience (4670/22,740, 20.50%), unstable internet connection (3820/22,740, 16.80%), language barriers (3708/22,740, 16.30%), medication and medical hardware shortage (2459/22,740, 10.82%), tiring commute (2068/22,740, 9.08%), family care burdens (679/22,740, 3.0%), and other unclassified difficulties (356/22,740, 1.6%) as difficulties in using telemedicine. CONCLUSIONS: Telemedicine has a wide disease spectrum, similar to ordinary medicine in China. It saves costs, has high patient satisfaction and price acceptability, and can relieve disease and syndromes. However, certain problems need to be resolved. Telemedicine could be a feasible approach to address the health care maldistribution in rural China. This study may provide useful information for policy making and guidance for further telemedicine practice in China and other developing countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".