Fast Healthcare Interoperability Resources (FHIR)–Based Interoperability Design in Indonesia: Content Analysis of Developer Hub’s Social Networking Service
Notice bibliographique
Résumé
Background: Interoperability in health care is a critical aspect for the exchange of health information. The Fast Healthcare Interoperability Resources (FHIR) framework has become widely adopted to provide interoperable data exchange in the health care industry. The COVID-19 pandemic has demonstrated the significance of interoperable data in tracking patients who have contracted the virus and keeping track of the vaccinated population. Indonesia is one of the many countries that have implemented interoperable data systems to track patients with COVID-19, and it has aspirations to expand the system to other use cases, particularly in the primary health care setting. The primary health care providers in Indonesia include Puskesmas (community health centers) and private clinics. Objective: To promote interoperable health data exchange in the primary health care sector, the Indonesian government has launched the Satusehat project. The goal of the Satusehat platform is to make health data in Indonesia interoperable and exchangeable between health care organizations, particularly Puskesmas and private clinics. Methods: For a successful implementation of the Satusehat platform in Puskesmas and private clinics, it is crucial to understand the challenges that may arise. This study analyzed the pain points of the Satusehat platform based on a content analysis of the Satusehat Social Networking Service Telegram group messages. The study revealed the pain points and suggested existing approaches to address them, which can be used as a proposed design of interoperability for Puskesmas and private clinics, making it easier for these organizations to adopt the Satusehat platform. Results: The pain points identified in this study include issues with the FHIR server, problems with FHIR profile selection, and the mapping of electronic medical record data into standardized data, such as mapping into the Systematized Nomenclature of Medicine Clinical Terminology. The results show that the value of the mapping issue is 37, profile issue 9, and server issue 61. Among the 3 categories, server issues had the highest population, followed by mapping issues and then profile issues. To address these issues, the study proposed practical approaches, including a federated architecture for the FHIR server instead of a centralized architecture, an FHIR writer and FHIR viewer system inspired by the Standardized Structured Medical Record Information eXchange system in Japan, and an FHIR conversion framework that integrates with our FHIR writer and FHIR viewer system. Conclusions: These proposed solutions can help resolve the pain points identified in the study and help the advancement of the Satusehat platform implementation in Puskesmas and private clinics in Indonesia. We believed that the proposed solutions have potential to be adopted to other countries with similar issues when conducting nationwide project in health care interoperability design.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,009 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».