Impact of pharmacists' interventions on the pharmacotherapy of patients with complex needs monitored in multidisciplinary primary care teams
Bibliographic record
Abstract
OBJECTIVES: Recently, pharmacists have joined multidisciplinary healthcare teams within family medicine groups (FMG) in Quebec Province, Canada. This study assessed the impact of their interventions on the pharmacotherapy of patients with complex needs monitored in FMGs. METHODS: We performed a pre/post real-life intervention study among patients with complex needs referred to the FMG pharmacist in four FMGs in Quebec City. Pharmacists collected data at baseline, during follow-up and up to 6 months after the first encounter. They recorded all drug-related problems (DRPs) identified, interventions made and recommendations that were accepted by physicians. The researchers used the data collected to compare the medication regimen complexity index (MRCI) and medication adherence (using the proportion of days covered (PDC)) before and after the pharmacist's interventions. Descriptive statistics and paired sample t-tests were computed. KEY FINDINGS: Sixty-four patients (median age: 74.5 years) were included; four patients were lost to follow-up. Pharmacists detected 300 DRPs (mean: 7.2 per patient) during the study period for which they made an intervention. The most common DRP was 'drug use without indication' (27%). The physicians accepted 263 (87.7%) of those interventions. The mean number of prescribed drugs per patient decreased from 13.8 (95% confidence interval (CI): 12.24 to 15.29) to 12.4 (95% CI: 10.92 to 13.90). The mean MRCI decreased from 47.18 to 41.74 (-5.44; 95% CI: 1.71 to 9.17), while the mean PDC increased from 84.4% to 90.0% (+5.6%; 95% CI: 2.7% to 8.4%). CONCLUSION: Family medicine groups pharmacists can detect and resolve DRPs and can reduce medication regimen complexity and non-adherence to treatment in patients with complex needs monitored in FMGs.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".