Patient-Centric Mobile Medical Services Accessed Through Smartphones in the Top 100 Chinese Public Hospitals: Cross-Sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: Smartphone-based technology has been used to enhance the delivery of health care services to the public in numerous countries. OBJECTIVE: This study aims to investigate the application of patient-centric mobile medical services accessed through smartphones in the top 100 Chinese public hospitals. METHODS: Data on 124 tertiary public hospitals, ranked among the top 100 by the China Hospital Science and Technology Evaluation Metrics of the Chinese Academy of Medical Sciences (2019) and China's Hospital Rankings of the Hospital Management Institute of Fudan University (2019), were collected from the WeChat platform (Tencent Inc), mobile phone apps, and official websites until February 10, 2021. RESULTS: A total of 124 tertiary public hospitals, all of which were among the top 100 hospitals according to the 2 ranking lists, were selected for this study. Almost all (122/124, 98.39%) of the hospitals offered basic services such as appointment scheduling, registration, and health education. The majority also provided online access to test reports (95/124, 76.61%), consultations (72/124, 58.06%), and prescriptions (61/124, 49.19%). Among the hospitals offering online prescriptions, the majority (54/61, 88.52%) supported home delivery through third-party carriers. Slightly less than half (57/124, 45.97%) used artificial intelligence for medical guidance. Only a small fraction (8/124, 6.45%) managed chronic diseases through online monitoring and supervision by experienced doctors. Approximately half (60/124, 48.39%) of the included hospitals were officially licensed as internet hospitals approved to provide full online services. Hospitals with official internet hospital licenses provided more extensive digital health offerings. A significantly higher proportion of approved hospitals offered online consultations (29.69% vs 88.33%, r=43.741; P<.001), test reports (62.5% vs 91.67%, r=14.703; P<.001), and chronic disease management (1.56% vs 11.67%, r=5.238; P<.05). These officially approved hospitals tended to provide over 6 mobile medical services, mainly in the regions of Shanghai and Guangdong. This geographic distribution aligned with the overall layout of hospitals included in the study. CONCLUSIONS: Patient-centric mobile medical services offered by the top 100 Chinese public hospitals accessed through smartphones primarily focus on online appointment scheduling, registration, health education, and accessing test reports. The most popular features include online consultations, prescriptions, medication delivery, medical guidance, and early-stage chronic disease management. Approved internet hospitals offer a significantly greater variety of patient-centric mobile medical services compared with unapproved ones.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".