Smart about medications (SAM): a digital solution to enhance medication management following hospital discharge
Bibliographic record
Abstract
OBJECTIVE: To outline the development of a software solution to improve medication management after hospital discharge, including its design, data sources, intrinsic features, and to evaluate the usability and the perception of use by end-users. MATERIALS AND METHODS: Patients were directly involved in the development using a User Center Design (UCD) approach. We conducted usability interviews prior to hospital discharge, before a user started using the application. A technology acceptance questionnaire was administered to evaluate user self-perception after 2 weeks of use. RESULTS: The following features were developed; pill identification, patient-friendly drug information leaflet, side effect checker, and interaction checker, adherence monitoring and alerts, weekly medication schedule, daily pill reminders, messaging service, and patient medication reviews. The usability interviews show a 98.3% total success rate for all features, severity (on a scale of 1-4) 1.4 (SD 0.79). Regarding the self-perception of use (1-7 agreement scale) the 3 highest-rated domains were: (1) perceived ease of use 5.65 (SD 2.02), (2) output quality 5.44 (SD 1.65), and (3) perceived usefulness 5.29 (SD 2.11). DISCUSSION: Many medication management apps solutions have been created and most of them have not been properly evaluated. SAM (Smart About Medications) includes the user perspective, integration between a province drug database and the pharmacist workflow in real time. Its features are not limited to maintaining a medication list through manual entry. CONCLUSION: We can conclude after evaluation that the application is usable and has been self-perceived as easy to use by end-users. Future studies are required to assess the health benefits associated with its use.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".