The Usability, Acceptability, and Functionality of Smart Oral Multidose Dispensing Systems for Medication Adherence: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Medication non-adherence is a leading cause of non-optimal disease management, resulting in poor health outcomes, poor quality of life, and increased healthcare costs. Smart oral multidose dispensing systems (SOMDS) are being developed to address non-adherence; however, little is known about their integration into daily use by patients. METHODS: Using Arksey and O'Malley's scoping review framework, relevant literature was searched for in electronic databases (PubMed, EMBASE, International Pharmaceutical Abstracts, and Scopus). Observational and interventional studies reporting the integration and impact on adherence from SOMDS in adults ≥18 years and published after 1960 were included. RESULTS: Thirteen articles including one case study, 8 cohort studies, and 4 randomized trials were eligible. SOMDS included smart blister packaging, automated dispensers, and electronic medication trays. The number of medications dispensed per SOMDS was one (n = 3), >1 (n = 2), placebo (n = 1) and not reported (n = 7). Reported outcomes included impact on medication adherence (n = 3), integration (n = 2) and both parameters (n = 8). CONCLUSION: Although most studies reported that SOMDS appear usable, there was significant variability in the SOMDS types, patient populations, medication adherence definitions, and measurements; impacting the interpretation of results. Future studies should be designed to address effectiveness of SOMDS on medication adherence in patients with multi-drug therapy and the utilization of real-time adherence data for informing clinical decision making.
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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 | 0.023 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".