Designing a Secure Blue Button Health Information Exchange for a Commercial Laboratory
Bibliographic record
Abstract
Background: There is a growing demand by patients for easy electronic access to laboratory result data for use in personal health record systems (PHR-S) and for transfer to other health providers. The Blue Button Initiative is a public-private partnership offering a framework based on national standards to support patient access to electronic data. Objective: The aim of this project study was to architect an initial pilot implementation of the Blue Button framework for a commercial laboratory to facilitate patient access to electronic results. Methods: The proposed design architecture includes multiple application services, specifically an Encrypted Data Store, Client Access component, and Result Publishing service to accomplish these goals of the pilot project and meet the security and privacy requirements. Results: The resulting application components and programming interfaces accomplish the initial pilot goals and provide a base to expand the platform to offer support for mobile devices and additional interoperability options. Encryption and isolation of data have been used to safeguard the confidentiality, integrity and availability of protected health information (PHI) and allow for the use of standard cloud services to host external facing components. Conclusions: The Blue Button standards and framework provide a solid basis for facilitating electronic access to result data by patients and for meeting the requirements of View, Download, and Transmit (V/D/T) in Meaningful Use Stage 2 (MU-II). The Blue Button Framework can provide the functionality required for a Consumer Mediated Health Information Exchange which gives patients the ability to aggregate and control the use of their health information among providers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".