Stakeholders’ Perceptions Regarding Digital Therapeutics Reimbursement in South Korea: Qualitative Study
Notice bibliographique
Résumé
Background: Digital therapeutics (DTx) are therapeutic interventions driven by software and directly provided to patients, allowing them to manage their health with ease in any setting. A growing interest in DTx has spurred a discussion concerning their reimbursement pathways. However, DTx are still at a premature stage, with insufficient evidence on effectiveness, efficiency, and safety. Currently, although industries desire to quickly enter the market, especially by getting their products reimbursed by the National Health Insurance (NHI) fund, the NHI is cautious about DTx due to their uncertainties. Thus, public discussion and social consensus are crucial in deciding whether to reimburse DTx by the NHI fund. Objective: This study examined multiple stakeholders' awareness and attitudes toward DTx and perceptions of regulatory pathways for adopting DTx. Methods: In-depth interviews were conducted with 11 stakeholders in South Korea (industry: n=4, health care: n=3, academia: n=2, and consumer: n=2) using semistructured guidelines. They were purposively sampled to identify individuals with expertise in DTx and NHI policies. The interviews were conducted either in person or via a videoconference for 45-70 minutes. Qualitative data were analyzed using directed content analysis, which uses interview guidelines as an analytical framework. Results: Findings were divided into three categories: (1) awareness and attitude toward DTx, (2) perception of whether DTx are worth entering the market and being reimbursed by the NHI fund, and (3) perception of how to enter the market and how to reimburse DTx by the NHI fund if they are worth it. Although consumer stakeholders were not familiar with the basic concept of DTx, the other stakeholders understood it thoroughly. However, all participants showed positive attitudes and acceptance of DTx. Most of them responded that DTx are worth entering the market, but they could not reach an agreement on the pathways for DTx to enter the market. Although participants were in favor of the reimbursement of DTx in principle, they responded that a conservative approach is required due to insufficient clinical evidence for DTx. Conclusions: We found that stakeholders in South Korea had positive attitudes toward DTx, perceived them as worth using, and agreed to allow them to enter the market. The main issue was not the problem of the technology itself but the difference in opinion as to the pathways for reimbursement. Therefore, this study concluded that the NHI fund, which is operated very conservatively, is insufficient to quickly adopt and implement DTx. Various reimbursement methods, including tax-based financing, raising innovation funds for new technologies, and pilot studies using the NHI fund, should be used to rapidly generate clinical evidence and reduce the uncertainties of DTx to secure a stable market.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,005 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».