The Implementation of Federated Digital Identifiers in Health Care: Rapid Review
Notice bibliographique
Résumé
BACKGROUND: Federated digital identifiers (FDIs) have been cited to improve the interoperability of data and information management while enhancing the privacy of individuals verifying their identity on the web. Many countries around the world have implemented FDIs in various sectors, such as banking and government. Similarly, FDIs could improve the experience for those wanting to access their health care information; however, they have only been introduced in a few jurisdictions around the world, and their impact remains unclear. OBJECTIVE: The main objective of this environmental scan was to describe how FDIs have been established and implemented to enable patients' access to health care. METHODS: We conducted this study in 2 stages, with the primary stage being a rapid review, which was supplemented by a targeted gray literature search. Specifically, the rapid review was conducted through a database search of MEDLINE and Embase, which generated a list of countries and their services that use FDIs in health care. This list was then used to conduct a targeted gray literature search using the Google search engine. RESULTS: A total of 93 references from the database and targeted Google searches were included in this rapid review. FDIs were implemented in health care in 11 countries (Australia, Belgium, Canada, Denmark, Estonia, Finland, Iceland, Norway, Singapore, Sweden, and Taiwan) and exclusively used with a patient-accessible electronic health record system through a single sign-on interface. The most common FDIs were implemented nationally or provincially, and establishing them usually required individuals to visit a bank or government office in person. In contrast, some countries, such as Australia, allow individuals to verify their identities entirely on the web. We found that despite the potential of FDIs for use in health care to facilitate the amalgamation of health information from different data sources into one platform, the adoption of most health care services that use FDIs remained below 30%. The exception to this was Australia, which had an adoption rate of 90%, which could be correlated with the fact that it leveraged an opt-out consent model. CONCLUSIONS: This rapid review highlights key features of FDIs across regions and elements associated with higher adoption of the patient-accessible electronic health record systems that use them, like opt-out registration. Although FDIs have been reported to facilitate the collation of data from multiple sources through a single sign-on interface, there is little information on their impact on care or patient experience. If FDIs are used to their fullest potential and implemented across sectors, adoption rates within health care may also improve.
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 | 0,018 | 0,060 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,016 | 0,018 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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; 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 ».