Healthcare provider interventions to support medication adherence: state-of-the-science overview
Bibliographic record
Abstract
Medication adherence remains a global health issue and healthcare providers (HCPs) play an important role in supporting patients to adhere to treatment. This article provides a state-of-the-science overview of the evidence for: i) the effectiveness of HCP-delivered interventions on medication adherence outcomes; and ii) the types of implementation approaches targeting evidence-to-practice gaps among HCPs supporting medication adherence. Hundreds of randomized controlled trials and dozens of systematic reviews on the effectiveness of HCP-delivered interventions have been conducted to date. HCP-delivered interventions typically produce small-to-medium effect sizes on adherence outcomes, however, there is considerable heterogeneity in effects and few interventions that show promise are implemented into routine practice. Some key features of potentially effective HCP-delivered interventions include: moving beyond education-only, using multiple behaviour change strategies, tailoring interventions to different determinants of non-adherence, incorporating pharmacists and nurses to deliver interventions, providing ongoing support to patients, and addressing health system-level barriers and inequities. To improve the uptake of evidence into adherence-related clinical practice, it is likely that health systems must adapt to enable HCPs to better support adherence over time and in a patient-centered way. Such approaches include, improving routine screening of adherence issues, making adherence-related clinical guidelines more actionable, using routinely collected data to identify patients with adherence challenges, enhancing HCP incentivization models, and establishing quality indicators for adherence monitoring and support. Concepts and evidence from implementation science should be leveraged to support these types of system-level approaches to address evidence-to-practice gaps. In conclusion, despite an extensive evidence base for the effectiveness of HCP-delivered interventions - and a growing body of evidence for approaches targeting practice change among HCPs - we have identified several areas that could help advance the field. These include optimizing the content and delivery of adherence interventions, understanding how to implement effective strategies, and reaffirming the need for health system-level solutions.
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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 | 0.036 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".