Improving Transplant Medication Safety Through a Technology and Pharmacist Intervention (ISTEP): Protocol for a Cluster Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Medication errors, adverse drug events, and nonadherence lead to increased health care utilization and increased risk of adverse clinical outcomes, including graft loss, in solid organ transplant recipients. Veterans living with organ transplants represent a population that is at substantial risk for medication safety events and fragmented care coordination issues. To improve medication safety and long-term clinical outcomes in veteran transplant patients, interventions should address interorganizational system failures and provider-level and patient-level factors. OBJECTIVE: This study aims to measure the clinical and economic effectiveness of a pharmacist-led, technology-enabled intervention, compared with usual care, in veteran organ transplant recipients. METHODS: This is a 24-month prospective, parallel-arm, cluster-randomized, controlled multicenter study. The pharmacist-led intervention uses an innovative dashboard system to improve medication safety and health outcomes, compared with usual posttransplant care. Pharmacists at 10 study sites will be consented into this study before undergoing randomization, and 5 sites will then be randomized to each study arm. Approximately, 1600 veteran transplant patients will be included in the assessment of the primary outcome across the 10 sites. RESULTS: This study is ongoing. Institutional review board approval was received in October 2018 and the study opened in March 2019. To date there are no findings from this study, as the delivery of the intervention is scheduled to occur over a 24-month period. The first results are expected to be submitted for publication in August 2021. CONCLUSIONS: With this report, we describe the study design, methods, and outcome measures that will be used in this ongoing clinical trial. Successful completion of the Improving Transplant Medication Safety through a Technology and Pharmacist Intervention study will provide empirical evidence of the effectiveness of a feasible and scalable technology-enabled intervention on improving medication safety and costs. CLINICAL TRIAL: ClinicalTrials.gov NCT03860818; https://clinicaltrials.gov/ct2/show/NCT03860818. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/13821.
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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 | 0.038 | 0.036 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.090 | 0.013 |
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".