Health Service Early-Stage Digital Adaptation of Traditional Chinese Medicine Internet Hospitals: Qualitative Exploratory Study
Bibliographic record
Abstract
Background: Traditional Chinese medicine (TCM) hospitals in China are experimenting to develop internet hospitals to provide health services. To date, little is known about the characteristics of health services delivered by TCM internet hospitals. Objective: This study aimed to investigate the health service early-stage digital adaptation of TCM internet hospitals from the aspects of target patients, value offering, and service provision. Methods: Qualitative research combined qualitative interview and documentary research in this study. Interviews were completed with clinicians from sample TCM internet hospitals to investigate the target patients and value offerings. Documentary research was conducted to investigate the service provision. Thematic analysis was used to interpret all the materials collected. Results: A total of 7 TCM internet hospitals and 14 participants were included. The target patients of TCM internet hospitals were patients with subsequent visits and patients who sought consultations on health management. TCM internet hospitals were improving patients' adherence to subsequent medical care and TCM promotion. These hospitals provided functional service (including telemedicine, telepharmacy, telenursing, web-based health consultations, and convenient service), and TCM specialty service (including "Tianzhi" [crude herb moxibustion], "Zhiweibing" [preventive treatment of disease], and poststroke rehabilitation). Conclusions: TCM internet hospitals are in an early-stage digital adaptation, offering primarily basic online-offline services. While not yet fully innovative, they represent a transitional model with the potential to reshape TCM delivery. Our findings contribute high-level insights into this emerging integration and inform future development toward more structured, patient-centered digital TCM services.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".