Organizational implications of implementing a new adverse drug event reporting system for care providers and integrating it with provincial health information systems
Bibliographic record
Abstract
Cross-sector collaborations between academia, government, and private industry, known as Triple Helix configurations, are increasingly common. In the health Information Technology (IT) sector, such configurations often also include health delivery organizations where technology is implemented and used. The complexity of collaborating within and between multiple organizations can present hurdles for innovators that are seldom discussed in the literature. We outline challenges we encountered in cross-sector collaboration and offer some guiding principles for decision-makers, academics, industry partners, and health delivery organizations to successfully negotiate divergent approaches to innovation and implementation. We discuss an innovative project that aims to implement a researcher-designed adverse drug event reporting system into clinical care and integrate it with provincial and health authority IT systems. Based on our experience, implementing an interoperable health IT system must extend beyond technical integration to encompass meaningful stakeholder engagement to ensure utility for end-users and beneficial impact for participating organizations.
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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 | 0.081 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".