Lessons Learned From Over 20 Years of Telemedicine Services in India: Scoping Review of Telemedicine Services Initiated From 2000 to 2023
Notice bibliographique
Résumé
BACKGROUND: India is home to some of the world's earliest and largest telemedicine services. Since the first telemedicine services emerged in the 1990s, the growing digitization of health care services has highlighted the potential for telemedicine services to increase access to timely and appropriate care seeking, corresponding to improved health outcomes and cost savings to the individual and health system. Despite this potential, little is known about the varied typologies of telemedicine services providing in India, their design and model characteristics, scale of implementation, and the available evidence on their impact. OBJECTIVE: This scoping review aims to identify the characteristics of telemedicine services in India, including the type of telemedicine model, details on the timing of delivery, health services provided, and service delivery channel. Additional details are extracted on the scale of implementation, software used, and evidence gathered, including impact on care seeking, health outcomes, and cost. METHODS: Telemedicine services in India were identified through searches of Google, the Google Play Store, 3 major scientific databases (Embase, PubMed, and Scopus), and a reference review of identified peer-reviewed articles. Included services were restricted to those implemented in India between January 1, 2010, and July 4, 2023, which included humans, and were published in the English language. Once identified, articles were imported to Covidence, and the process of abstract screening was initiated using 2 independent reviewers and a third person to resolve conflicts. Full-text articles were screened, and data were extracted into Microsoft Excel. RESULTS: A total of 2368 articles were identified, 151 of which were included for the full-text review and data extraction. From the 151 studies, a total of 115 unique services were identified and further classified based on a scale-moderate to large (n=89) and small (n=26). Among moderate- to large-scale services (n=89), 75 used specialized software and 14 used nonspecialized software, such as WhatsApp. On average, 3 new telemedicine services were initiated annually from 2000 to 2019, and the growth of new services occurred predominantly in the private sector. Evidence was available for 43% (32/75) of the telemedicine services. While 21 services reported on some facet of the quality of care, no studies systematically assessed quality of care. Where structured surveys were reported, questions were often leading, used longer Likert scale response options, and asked respondents about broad constructs subject to varied interpretations (eg, quality of care or satisfaction). Additional details on model characteristics, reach, and impact are presented. CONCLUSIONS: The widespread proliferation of telemedicine services in India has much potential to improve access to and continuity of timely and appropriate care seeking for health. However, improved evidence demonstrating the impact of telemedicine services on care seeking, quality of care, cost, and health outcomes is needed.
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 | 0,035 | 0,100 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,019 | 0,031 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,007 | 0,008 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,003 |
| 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 ».