Scoping review of interventions to de-implement potentially harmful non-steroidal anti-inflammatory drugs (NSAIDs) in healthcare settings
Bibliographic record
Abstract
OBJECTIVES: Potentially harmful non-steroidal anti-inflammatory drugs (NSAIDs) utilisation persists at undesirable rates worldwide. The purpose of this paper is to review the literature on interventions to de-implement potentially harmful NSAIDs in healthcare settings and to suggest directions for future research. DESIGN: Scoping review. DATA SOURCES: PubMed, CINAHL, Embase, Cochrane Central and Google Scholar (1 January 2000 to 31 May 2022). STUDY SELECTION: Studies reporting on the effectiveness of interventions to systematically reduce potentially harmful NSAID utilisation in healthcare settings. DATA EXTRACTION: Using Covidence systematic review software, we extracted study and intervention characteristics, including the effectiveness of interventions in reducing NSAID utilisation. RESULTS: From 7818 articles initially identified, 68 were included in the review. Most studies took place in European countries (45.6%) or the USA (35.3%), with randomised controlled trial as the most common design (55.9%). Interventions were largely clinician-facing (76.2%) and delivered in primary care (60.2%) but were rarely (14.9%) guided by an implementation model, framework or theory. Academic detailing, clinical decision support or electronic medical record interventions, performance reports and pharmacist review were frequent approaches employed. NSAID use was most commonly classified as potentially harmful based on patients' age (55.8%), history of gastrointestinal disorders (47.1%), or history of kidney disease (38.2%). Only 7.4% of interventions focused on over-the-counter (OTC) NSAIDs in addition to prescription. The majority of studies (76.2%) reported a reduction in the utilisation of potentially harmful NSAIDs. Few studies (5.9%) evaluated pain or quality of life following NSAIDs discontinuation. CONCLUSION: Many varied interventions to de-implement potentially harmful NSAIDs have been applied in healthcare settings worldwide. Based on these findings and identified knowledge gaps, further efforts to comprehensively evaluate the effectiveness of interventions and the combination of intervention characteristics associated with effective de-implementation are needed. In addition, future work should be guided by de-implementation theory, focus on OTC NSAIDs and incorporate patient-focused strategies and outcomes, including the evaluation of unintended consequences of the intervention.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".