Deprescribing in a family health team: a study of chronic proton pump inhibitor use
Bibliographic record
Abstract
BACKGROUND Proton pump inhibitors (PPIs) are often used inappropriately, without an indication, or for longer durations than recommended. Few tools exist to guide reassessment of their continued use and deprescribing if required. We aimed to reduce inappropriate drug use by developing and implementing a PPI deprescribing tool and process in a family medicine unit. ASSESSMENT OF PROBLEM Primary care providers of adults taking a PPI for 8 weeks with an upcoming periodic health examination were reminded to reassess therapy via electronic medical record (EMR) messaging. A PPI Deprescribing Tool was uploaded into the EMR as a second reminder and to guide reassessment and deprescribing where indicated. Ten weeks after the examination a chart review assessed changes to PPI use. A follow up survey of providers assessed the utility and barriers to implementing the Deprescribing Tool. RESULTS Forty-three of 46 patients on PPIs (93%) had their PPI reassessed, resulting in 11 patients (26%) having their PPI deprescribed. Strategies for Improvement Routine reassessment of long-term medications is often overlooked because of extensive demands on primary care providers' time. Deprescribing likely improved because potentially eligible patients were identified to the provider and a tool was provided at the time of the encounter to guide the deprescribing process. LESSONS Reassessment and deprescribing of PPIs can be supported by implementing a standardised process and use of guidance tools for clinicians. Providers found the timely and selective reminder message to deprescribe the most useful component of the intervention. KEYWORDS proton pump inhibitor; deprescribing; reassessment; primary care; medication therapy management; gastroesophageal reflux disease.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".