The MedSafer Study: A Controlled Trial of an Electronic Decision Support Tool for Deprescribing in Acute Care
Bibliographic record
Abstract
OBJECTIVES: Polypharmacy is common, costly, and harmful for hospitalized older adults. Scalable strategies to reduce the burden of potentially inappropriate medications (PIMs) are needed. We sought to leverage medication reconciliation in hospitalized older adults by pairing with MedSafer, an electronic decision support tool for deprescribing. DESIGN: This was a nonrandomized controlled before-and-after study. SETTING: The study took place on four internal medicine clinical teaching units. PARTICIPANTS: Subjects were aged 65 years and older, had an expected prognosis of 3 or more months, and were taking five or more usual home medications. INTERVENTION: In the baseline phase, patients received usual care that was medication reconciliation. Patients in the intervention arm also had a "deprescribing opportunity report" generated by MedSafer and provided to their in-hospital treating team. MEASUREMENTS: The primary outcome was ascertained at the time of hospital discharge and was the proportion of patients who had one or more PIMs deprescribed. RESULTS: A total of 1066 patients were enrolled, and deprescribing opportunities were present for 873 (82%; 418 during the control and 455 during the intervention phases, respectively). The proportion of patients with one or more PIMs deprescribed at discharge increased from 46.9% in the control period to 54.7% in the intervention period with an adjusted absolute risk difference of 8.3% (2.9%-13.9%). Not all classes of drugs in the intervention arm were associated with an increase in deprescribing, and new PIM starts were equally common in both arms of the study. CONCLUSION: Using an electronic decision support tool for deprescribing, we increased the proportion of patients with one or more PIMs deprescribed at hospital discharge as compared with usual care. Although this type of intervention may help address medication overload in hospitalized patients, it also underscores the importance of powering future trials for a reduction in adverse drug events. TRIAL REGISTRATION: NCT02918058. J Am Geriatr Soc 67:1843-1850, 2019.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".