Patient-Centric Approach to Personalized Electronic Medical Records via QR Code in Japan
Notice bibliographique
Résumé
Government policies in the United States and the European Union promote standardization and value creation in the use of FAIR (findability, accessibility, interoperability, and reusability) data, which can enhance trust in digital health systems and is crucial for their success. Trust is built through elements such as FAIR data access, interoperability, and improved communication, which are essential for fostering innovation in digital health technologies. This Viewpoint aims to report on exploratory research demonstrating the feasibility of testing a patient-centric data flow model facilitating semantic interoperability on precision medical information. In this global trend, the interoperable interface called Sync for Science-J (S4S-J) for linking electronic medical records (EMRs) and personal health records was launched as part of the Basic Policy for Economic and Fiscal Management and Reform in Japan. S4S-J controls data distribution consisting of EMR and patient-generated health data and converts this information into QR codes that can be scanned by mobile apps. This system facilitates data sharing based on personal information beliefs and unlocks siloed Internet of Things systems with a privacy preference manager. In line with Japanese information handling practices, the development of a mobile cloud network will lower barriers to entry and enable accelerated data sharing. To ensure cross-compatibility and compliance with future international data standardization, S4S-J conforms to the Health Level 7 Fast Health Care Interoperability Resources standard and uses the international standardized logical observation identifiers names and codes (LOINC) to redefine medical terms used in different terminology standards in different medical fields. It is developed as an applied standard in medical information intended for industry, health care services, and research through secondary use of data. A multicenter collaborative study was initiated to investigate the effectiveness of this system; this was a registered, multicenter, randomized controlled clinical trial, the EMBRACE study of the mobile health app M♡Link for hyperglycemic disorders in pregnancy, which implements an EMR-personal health record interoperable interface via S4S-J. Nevertheless, the aforementioned new challenges, the pivotal Health Level 7 Fast Health Care Interoperability Resources system, and LOINC data mapping were successfully implemented. Moreover, the preliminary input of EMR-integrated patient-generated health data was successfully shared between authorized medical facilities and health care providers in accordance with the patients' preferences. The patient-centric data flow of the S4S-J in Japan is expected to guarantee the right to data portability, which promotes the maximum benefit of use by patients themselves, which in turn contributes to the promotion of open science.
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 | 0,040 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,019 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».