Status of Digital Health Technology Adoption in 5 Vietnamese Hospitals: Cross-Sectional Assessment
Notice bibliographique
Résumé
BACKGROUND: Digital health technologies (DHTs) have been recognized as a key solution to help countries, especially those in the low- and middle-income group, to achieve the Sustainable Development Goals (SDGs) and the World Health Organization's (WHO) Triple Billion Targets. In hospital settings, DHTs need to be designed and implemented, considering the local context, to achieve usability and sustainability. As projects such as the Vietnam ICU Translational Applications Laboratory are seeking to integrate new digital technologies in the Vietnamese critical care settings, it is important to understand the current status of DHT adoption in Vietnamese hospitals. OBJECTIVE: We aimed to explore the current digital maturity in 5 Vietnamese public hospitals to understand their readiness in implementing new DHTs. METHODS: We assessed the adoption of some key DHTs and infrastructure in 5 top-tier public hospitals in Vietnam using a questionnaire adapted from the Vietnam Health Information Technology (HIT) Maturity Model. The questionnaire was answered by the heads of the hospitals' IT departments, with follow-up for clarifications and verifications on some answers. Descriptive statistics demonstrated on radar plots and tile graphs were used to visualize the data collected. RESULTS: Hospital information systems (HIS), laboratory information systems (LIS), and radiology information systems-picture archiving and communication systems (RIS-PACS) were implemented in all 5 hospitals, albeit at varied digital maturity levels. At least 50% of the criteria for LIS in the Vietnam HIT Maturity Model were satisfied by the hospitals in the assessment. However, this threshold was only met by 80% and 60% of the hospitals with regard to HIS and RIS-PACS, respectively. Two hospitals were not using any electronic medical record (EMR) system or fulfilling any extra digital capability, such as implementing clinical data repositories (CDRs) and clinical decision support systems (CDSS). No hospital reported sharing clinical data with other organizations using Health Level Seven (HL7) standards, such as Continuity of Care Document (CCD) and Clinical Document Architecture (CDA), although 2 (40%) reported their systems adopted these standards. Of the 5 hospitals, 4 (80%) reported their RIS-PACS adopted the Digital Imaging and Communications in Medicine (DICOM) standard. CONCLUSIONS: The 5 major Vietnamese public hospitals in this assessment have widely adopted information systems, such as HIS, LIS, and RIS-PACS, to support administrative and clinical tasks. Although the adoption of EMR systems is less common, their implementation revolves around data collection, management, and access to clinical data. Secondary use of clinical data for decision support through the implementation of CDRs and CDSS is limited, posing a potential barrier to the integration of external DHTs into the existing systems. However, the wide adoption of international standards, such as HL7 and DICOM, is a facilitator for the adoption of new DHTs in these hospitals.
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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 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 ».