Implementation Status and Usability of Digital Health Interventions Among Health Care Workers and End Users at the Primary Health Care Level in Chandigarh, North India: Cross-Sectional Study
Notice bibliographique
Résumé
Background: Digital health interventions (DHIs) refer to the use of information and communication technologies to support or facilitate the achievement of health objectives. The Government of India has launched various DHIs at the primary health care level to improve health services and health-seeking behaviors. However, there is a paucity of evidence on the effectiveness of implementing these interventions and the user response from target end-users within the government health system setting. Objective: This study aimed to assess the implementation status of DHIs and the user response of target end users, that is, the general population and health care workers (HCWs), in health and wellness centers (HWCs) in Chandigarh, India. Methods: A cross-sectional study was conducted to assess the implementation status of 9 DHIs: the Electronic Vaccine Intelligence Network (eVIN), Reproductive and Child Health (RCH), Health Management Information System (HMIS), HWC portal, Comprehensive Primary Health Care-Noncommunicable Disease (CPHC-NCD), Family Planning-Logistics Management Information System (FP-LMIS), eSanjeevani, Integrated Disease Surveillance Program-Integrated Health Information Program (IDSP-IHIP) portal, Aarogya Setu, and the COVID-19 Vaccine Intelligence Network (CoWIN) app. Data were collected from 4 purposively selected HWCs using a pretested data extraction form and observation checklist from June to September 2022. The implementation status of these DHIs was evaluated by categorizing indicators into input, process, and output components and estimating cumulative percentage scores using a score-based logic model framework. Pretested interview schedules were used to assess awareness and user response of DHIs among 120 target end users (clients visiting HWCs) and 120 HCWs (auxiliary nurse midwives, data entry operators, and medical officers). The prevalence of user response was then estimated. Results: The implementation status scores of the eVIN and RCH portals ranged from 70% to 90%. The HMIS portal, HWC portal, CPHC-NCD portal, and FP-LMIS scored between 25% and 50%, while eSanjeevani and the IDSP-IHIP portal scored between 51% and 70%. Community awareness of DHIs was poor, ranging from 1% to 18.3%, except for Aarogya Setu (94/120, 78.3%) and the CoWIN app (43/120, 35.8%), despite 86.7% (104/120) of participants having access to a mobile phone. Low awareness of DHIs was significantly associated with lower socioeconomic status (P=.02) and lower education levels (P=.04). In total, 66% (80/120) of HCWs reported that working with DHIs was easy; however, 89.2% (107/120) stated that dual data entry increased their workload. Frequent technical glitches were most commonly reported for the Auxiliary Nurse Midwife OnLine app (78/80, 97%) by HCWs. Help desk or feedback options in DHIs were rarely used by auxiliary nurse midwives/multipurpose workers (0%-3.8%). Conclusions: The RCH and eVIN portals were effectively implemented, eSanjeevani was moderately implemented, while the HMIS, HWC portal, CPHC, and FP-LMIS were poorly implemented. Community awareness of DHIs was low, except for the Aarogya Setu and CoWIN apps. Although HCWs found DHIs easy to use, increased workload due to dual data entry and frequent technical issues was a key concern.
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 | 0,004 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| 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 ».