MedSafer to Support Deprescribing for Residents of Long-Term Care: a Mixed-Methods Study
Bibliographic record
Abstract
BackgroundPolypharmacy is prevalent in long-term care homes (LTCH) and increases the risk of adverse drug events. Feasible and effective deprescribing interventions applicable in the LTCH environment are needed. MethodsWe performed a mixed methods study to evaluate the feasi-bility, applicability, and effectiveness of an electronic depre-scribing tool, MedSafer, to facilitate quarterly medication reviews (QMRs) on two pilot units in an academic long-term care home (LTCH). Chart reviews collected resident health data. The prevalence of deprescribing at a standard QMR was compared with a QMR conducted three months later with MedSafer. Feedback from physicians on their experience with MedSafer was obtained through semi-structured interviews. ResultsPhysicians found MedSafer helpful in guiding deprescribing decisions and suggested software improvements to increase the feasibility in LTCH. The average number of medications deprescribed per resident was significantly higher at the Med-Safer QMR (mean reduction = 1.1 medications, SD = 1.3) compared to the standard QMR (mean reduction = 0.5, SD = 0.9) (absolute difference of 0.5; SD 1.1; p = .02). ConclusionMedSafer has the potential to increase deprescribing in LTCHs by flagging potentially inappropriate medications. Integration in the electronic medical record might increase uptake in LTCHs. Further research should investigate the generalizability of MedSafer in a larger population and in non-academic LTCHs.
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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 | 0.040 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".