Bridging Policy and Practice in Telemedicine Follow-Up Identification: Multicenter Mixed Methods Study in Beijing
Bibliographic record
Abstract
Background: Telemedicine services have been developing rapidly worldwide. Following the 2018 policy enabling telemedicine for follow-up patients, this service model has gradually gained popularity in China. However, little has been done to understand the policy's implementation across different types of medical institutions or to evaluate its effectiveness. Objective: This study aims to (1) investigate the patient eligibility assessment process in various types of institutions for telemedicine services in Beijing, (2) elucidate institutions' rationale for adopting such approaches, (3) analyze discrepancies between policy and practice, and (4) provide references for the development of telemedicine services. Methods: This mixed methods study involved 36 medical institutions in Beijing, determined based on placing in the top 20% for both service volume and service quality in 2023. The study was conducted in 2 phases. First, quantitative analyses were conducted based on the questionnaires collected from each institution's contact to gather information about the assessment process and patient prerequisites. Subsequently, qualitative analyses were conducted through thematic analysis of 36 semistructured interviews with each institution's contact to acquire their considerations of this practice. Results: These 36 institutions contributed 968,786 telemedicine visits, representing 89.5% of the total service volume in Beijing. In practice, each of the visits underwent a 2-stage eligibility assessment before the physician officially accepted the patient's request. In the first stage, for assessment approaches, 86.2% (25/29) of the nonprofit, tertiary hospitals and 14.3% (1/7) of the private, for-profit institutions automatically assessed patient eligibility, while others did it manually. The assessment was based on the scope of previous visit location, diagnosis of previous visits, and visit interval. For the scope of visit location, 22 hospitals required prior visits to the same institution. For diagnosis, 7 hospitals required patients to have an identical diagnosis. For visit intervals, 11 hospitals required it to be within 6 months. The second stage assessment was conducted by physicians. Compared with policy requirements, nonprofit hospitals had stricter requirements for the scope of visit locations. The main reasons for these discrepancies included distrust in the medical outcomes from other institutions (19/26, 73.1%) and difficulties in handling interinstitutional medical disputes (18/26, 69.2%). In addition, 61.1% (22/36) of the institutions indicated that terminations of telemedicine services were primarily due to the patient's conditions. Conclusions: This pioneering multicenter, mixed methods study delineated the patient eligibility assessment process for telemedicine services in Beijing. Discrepancies were identified between real-world practice and regulatory prerequisites. The key factors contributing to these variations included the ambiguity of policies and different priorities across institution types. Our findings suggest greater policy clarity, relaxation of regulations for new patients, and strengthened oversight of telemedicine services to improve the quality and accessibility of telemedicine.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".