Implementation of a Clinician-led Medication Adherence Intervention Among Patients With Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Medication nonadherence in systemic lupus erythematosus (SLE) leads to poor clinical outcomes. We developed a clinician-led adherence intervention that involves reviewing real-time pharmacy refill data and using effective communication to address nonadherence. Prior pilot testing showed promising effects on medication adherence. Here, we describe further evaluation of how clinicians implemented the intervention and identify areas for improvement. METHODS: We audio recorded encounters of clinicians with patients who were nonadherent (90-day proportion of days covered [PDC] < 80% for SLE medications). We coded recordings for intervention components performed, communication quality, and time spent discussing adherence. We also conducted semistructured interviews with patients and clinicians on their experiences and suggestions for improving the intervention. We assessed change in 90-day PDC post intervention. RESULTS: We included 25 encounters with patients (median age 39, 100% female, 72% Black) delivered by 6 clinicians. Clinicians performed most intervention components consistently and exhibited excellent communication, as coded by objective coders. Adherence discussions took an average of 3.8 minutes, and 44% of patients had ≥ 20% increase in PDC post intervention. In structured interviews, many patients felt heard and valued and described being more honest about nonadherence and more motivated to take SLE medications. Patients emphasized patient-clinician communication and financial and logistical assistance as areas for improvement. Some clinicians wanted additional resources and training to improve adherence conversations. CONCLUSION: We provide further evidence to support the feasibility, acceptability, and fidelity of the adherence intervention. Future work will optimize clinician training and evaluate the intervention's effectiveness in a large, randomized trial.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".