A Longitudinal Analysis of Institutional Adoption, Use, and Dissemination of an EHR Vendor-Based Data Sharing Program
Bibliographic record
Abstract
While health systems increasingly participate in real-world data sharing collaboratives, little is known about their empiric use. To examine one proprietary research collaborative in detail, administrative data from Cosmos research collaborative (Epic Corporation) was analyzed over 27 months through the end of 2023. An increasing number of organizations participated in Cosmos across geographic regions and organization types between Quarter 4 2021 and end of 2023 (152 to 229 total organizations "live" on Cosmos). While distinct users increased 3-fold over this time period, user engagement remained low, with between 0.25 to 0.21 projects on average per user per quarter. There was a trend toward increased number of logins and time using the platform over the study period. Through 2024 there have been 54 total publications referencing Cosmos. Although adoption is increasing for Cosmos, opportunities remain to improve cross-organizational data collaborative engagement for this and similar platforms.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".