Evaluating the effectiveness of the Smart About Meds (SAM) mobile application among patients discharged from hospital: protocol of a randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: Almost half of patients discharged from hospital are readmitted or return to the emergency department (ED) within 90 days. Non-adherence to medication changes made during hospitalisation and the use of potentially inappropriate medications (PIMs) both contribute to postdischarge adverse events. We developed Smart About Meds (SAM), a patient-centred mobile application that targets medication non-adherence and PIMs use. This protocol describes a randomised controlled trial (RCT) to evaluate SAM. METHODS AND ANALYSIS: A pragmatic, stratified RCT will evaluate SAM among 3250 adult patients discharged from hospital. At discharge, consenting participants will be randomised 1:1 to usual care or SAM. SAM integrates novel patient-centred features with pharmacist monitoring to manage non-adherence to new medication regimens. SAM also notifies patients of PIMs in their regimen, with advice to discuss with their physician.Following discharge, patients will be followed for 90 days to measure the primary composite outcome of ED visits, hospital readmissions and death. Secondary outcomes will include primary adherence to medication changes, secondary adherence to disease-modifying medications, patient empowerment and health-related quality of life.The primary outcome will be analysed according to intention-to-treat. Multivariable logistic regression will estimate differences between treatment groups in the proportion of patients experiencing the primary outcome and will assess modification of intervention effects by hospital, unit, age, sex and comorbidity burden. With a sample size of 3250, the study will have 80% power to detect a 5% absolute reduction in the primary outcome. Binary and continuous secondary outcomes will be assessed using multivariable logistic and linear regression, respectively. ETHICS AND DISSEMINATION: The Research Ethics Board of the McGill University Health Centre in Montréal, Canada has approved this study. Results will be submitted for publication in a peer-reviewed journal and presented at scientific conferences. If effective, SAM will be made available in app stores. TRIAL REGISTRATION NUMBER: NCT05371548.
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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 | 0.048 | 0.050 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.008 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.061 | 0.011 |
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".