Increasing the Detection and Response to Adherence Problems with Cardiovascular Medication in Primary Care through Computerized Drug Management Systems: A Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Adherence with antihypertensive and lipid-lowering therapy is poor, resulting in an almost 2-fold increase in hospitalization. Treatment side effects, cost, and complexity are common reasons for nonadherence, and physicians are often unaware of these potentially modifiable problems. OBJECTIVE: To determine if a cardiovascular medication tracking and nonadherence alert system, incorporated into a computerized health record system, would increase drug profile review by primary care physicians, increase the likelihood of therapy change, and improve adherence with antihypertensive and lipid-lowering drugs. METHODS: There were 2293 primary care patients prescribed lipid-lowering or antihypertensive drugs who were randomized to the adherence tracking and alert system or active medication list alone to determine if the intervention increased drug profile review, changes in cardiovascular drug treatment, and refill adherence in the first 6 months. An intention to treat analysis was conducted using generalized estimating equations to account for clustering within physician. RESULTS: Overall, medication adherence was below 80% for 36.3% of patients using lipid-lowering drugs and 40.8% of patients using antihypertensives at the start of the trial. There was a significant increase in drug profile review in the intervention compared to the control group (44.5% v. 35.5%; P < 0.001), a nonsignificant increase in drug discontinuations due to side effects (2.3% v. 2.0%; P = 0.61), and a reduction in therapy increases (28.5% v. 29.1%; P = 0.86). There was no significant change in refill adherence after 6 months of follow-up. CONCLUSION: An adherence tracking and alert system increases drug review but not therapy changes or adherence in prevalent users of cardiovascular drug treatment. Targeting incident users where adverse treatment effects are more common and combining adherence tracking and alert tools with motivational interventions provided by multidisciplinary primary care teams may improve the effectiveness of the intervention.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".