Bridging Policy and Practice in Telemedicine Follow-Up Identification: Multicenter Mixed Methods Study in Beijing
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».