The Implementation of Federated Digital Identifiers in Health Care: Rapid Review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".