Opioid stewardship: implementing a proactive, pharmacist-led intervention for patients coprescribed opioids and benzodiazepines at an urban academic primary care centre
Bibliographic record
Abstract
In 2017, almost 4000 Canadians died from opioid-related causes. Coadministration of opioids and benzodiazepines is a risk factor for overdose. Few studies have evaluated leveraging pharmacists to address opioid-benzodiazepine coprescribing. Our aim was to develop and test a role for pharmacists as opioid stewards, to reduce opioid and benzodiazepine doses in coprescribed patients. We conducted Plan-Do-Study-Act cycles between November 2017 and May 2018 across two primary care centre clinics. A third clinic acted as a control. Our intervention included a pharmacist: (1) identifying patients through medical record queries; (2) developing care plans; (3) discussing recommendations with physicians and (4) discussing implementing recommendations. We refined the intervention according to patient and physician feedback. At the intervention clinics, the number of patients with pharmacist developed care plans increased from less than 20% at baseline to over 60% postintervention. There was also a fourfold increase in the number of patients with an active opioid taper. At the control clinic, the number of patients with pharmacist developed care plans remained relatively stable at less than 20%. The number of patients with active opioid tapers remained zero. At the intervention clinics, mean daily opioid dose decreased 11% from 50.5 milligrams morphine equivalent (MME) to 44.7 MME. At the control clinic, it increased 15% from 62.3 MME to 71.4 MME. The number of patients with a benzodiazepine taper remained relatively stable at both the intervention and control clinics at less than 20%. At the intervention clinics, mean daily benzodiazepine dose decreased 8% from 9.9 milligrams diazepam equivalent (MDE) to 9.3 MDE. At the control clinic, it decreased 4% from 10.8 MDE to 10.4 MDE. A proactive, pharmacist-led intervention for coprescribed patients increased opioid tapers and decreased opioid and benzodiazepine doses. Future work will help us understand whether sustaining the intervention ultimately reduces rates of opioid-benzodiazepine coprescribing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".