Using ActionADE to create information continuity to reduce re-exposures to harmful medications: study protocol for a randomized controlled trial
Bibliographic record
Abstract
Background: Repeat exposures to culprit medications are a common cause of preventable adverse drug events. Health information technologies have the potential to reduce repeat adverse drug events by improving information continuity. However, they rarely interoperate to ensure providers can view adverse drug events documented in other systems. We designed ActionADE to enable rapid documentation of adverse drug events and communication of standardized information across health sectors by integrating with legacy systems. We will leverage ActionADE’s implementation to conduct two parallel, randomized trials: patients with adverse drug reactions in the main trial and those diagnosed with non-adherence in a secondary trial. Primary objective of the main trial is to evaluate the effects of providing information continuity about adverse drug reactions on culprit medication re-dispensations over 12 months. Primary objective of the secondary trial is to evaluate the effect of providing information continuity on adherence over 12 months. Methods: We will conduct two parallel group, triple-blind randomized controlled trials in participating hospitals in British Columbia, Canada. We will enroll adults presenting to hospital with an adverse drug event to prescribed outpatient medication. Clinicians will document the adverse drug event in ActionADE. The software will use an algorithm to determine patient eligibility and allocate eligible patients to experimental or control. In the experimental arm, ActionADE will transmit information to PharmaNet, where adverse drug event information will be displayed in community pharmacies when re-dispensations are attempted. In the control arm, ActionADE will retain information in the local record. We will enroll 3600 adults with an adverse drug reaction into the main trial. The main trial’s primary outcome is re-dispensation of a culprit or same-class medication within 12 months; the secondary trial’s primary outcome will be adherence to culprit medication. Secondary outcomes include health services utilization and mortality. Discussion: These studies have the potential to guide policy decisions and investments needed to drive health information technology integrations to prevent repeat adverse drug events. We present an example of how a health information technology implementation can be leveraged to conduct pragmatic randomized controlled trials. Trial registration ClinicalTrials.gov NCT04568668 , NCT04574648 . Registered on 1 October 2020.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".