Use of MedSafer electronic decision support for deprescribing in patients on hemodialysis: a qualitative study
Bibliographic record
Abstract
Background Patients on dialysis are commonly prescribed multiple medications (polypharmacy), many of which are potentially inappropriate medications (PIMs). PIMs are associated with an increased risk of falls, fractures, and hospitalization. Deprescribing is a promising intervention to reduce PIMs.Methods We previously conducted a prospective controlled trial whereby we provided deprescribing decision support to nephrologists in one of two tertiary care outpatient hemodialysis units in Montreal, Canada. We aimed to collect information on barriers and facilitators to implementing deprescribing decision support with an electronic tool (MedSafer) by conducting semi-structured interviews among the four nephrologists who participated in the intervention arm of the study, between February and April 2023, following completion of the study. The four nephrologists had conducted medication reviews for a total of 68 patients on the intervention unit during the study. Interviews with participating nephrologists were conducted and transcribed by the study lead. Afterwards, data was coded and analyzed thematically with a focus on their perspective on participating in a quality improvement project during their clinical practice. Two graduate students used a combination of deductive (Theoretical Domains Framework) and inductive coding to analyze each transcribed interview in duplicate. Each coder then created a mind map to visually interpret results and derive themes. A senior qualitative researcher oversaw the development of the final common themes from the interviews.Results Four themes were developed: 1) the importance of deprescribing for patients on hemodialysis, 2) barriers to the success of the deprescribing intervention (e.g., the lack of a clinical pharmacist on the unit), 3) resources that were needed during the intervention (e.g., multidisciplinary team members to facilitate medication reconciliation), and 4) resources that facilitated the intervention (e.g., the provision of deprescribing brochures to patients).Conclusions This was the first study to explore the perspectives of nephrologists participating in a quality improvement project on deprescribing for patients on hemodialysis. This study interviewed a limited number of prescribers, which could limit the contextualized understanding and transferability of the nephrologists’ experiences. As a next step, some of the facilitators identified by the nephrologists should be implemented and studied in a larger clinical trial.Trial registration: NCT05585268
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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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".