Assessing the acceptability and usability of MedSafer, a patient-centered electronic deprescribing tool
Bibliographic record
Abstract
Background: Deprescribing is the clinically supervised process of stopping or reducing medications that are no longer beneficial. MedSafer is an electronic decision support tool that guides healthcare providers (HCPs) through the deprescribing process. We recently developed a novel patient-facing version of the software, allowing patients and caregivers to generate a personalized deprescribing report to bring to their prescriber. Objective: The study aimed to evaluate the usability and acceptability of MedSafer among older adults, caregivers, and community HCPs (physicians, nurse practitioners and pharmacists). Method: A mixed-methods feasibility study was conducted with a convenience sample of 100 older adults/caregivers, and 25 healthcare practitioners. Participants were invited to test MedSafer and answer telephone or electronic surveys via RedCap. The Extended Technology Acceptance Model (TAM2) and System Usability Scale (SUS) were used for evaluation. A semi-structured interview was also conducted with a subset of participants (5 per group) who were selected on a volunteer basis, and thematic analysis was used following Braun & Clarke's approach. Results: Healthcare providers scored more favorably on TAM2 constructs such as perceived usefulness (PU) (median: 4.25 for HCPs; 3.75 for caregivers; 3.00 for patients), and SUS compared to patients and caregivers (mean: 79.50 for HCPs; 52.95 for caregivers; 55.75 for patients). Thematic analysis revealed that participants recognized MedSafer as an empowering tool but noted the need for some usability improvements. Conclusion: MedSafer is a promising tool to support deprescribing conversations. Enhancing usability, accessibility, and patient education may improve adoption.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".