Patient-Driven Sharing of Health Information: A National Effort to Advance Equitable Interoperability
Bibliographic record
Abstract
The goal of national interoperability is to improve care quality and decrease administrative burden and costs. Patients, providers, and other stakeholders are increasingly concerned that indiscriminate sharing of data may have deleterious, permanent consequences, as well as fail to provide granular control over the sharing of individual health data. Data segmentation and consent standards to date have been limited in scope and implementation, which has hindered efforts to scale data sharing preferences. Shift, an independent expert stakeholder task force, has been convened to mature standards, terminologies, and consensus-driven implementation guidance, which are prerequisites for more robust policy drivers needed to support nationwide sensitive data segmentation and consent capabilities. This paper describes Shift's framework and processes as means to advance equitable interoperability.
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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 | 0.307 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.022 | 0.030 |
| Open science | 0.007 | 0.044 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".