Effect of nurse practitioner and pharmacist counseling on inappropriate medication use in family practice.
Bibliographic record
Abstract
OBJECTIVE: To measure the effect of nurse practitioner and pharmacist consultations on the appropriate use of medications by patients. DESIGN: We studied patients in the intervention arm of a randomized controlled trial. The main trial intervention was provision of multidisciplinary team care and the main outcome was quality and processes of care for chronic disease management. SETTING: Patients were recruited from a single publicly funded family health network practice of 8 family physicians and associated staff serving 10 000 patients in a rural area near Ottawa, Ont. PARTICIPANTS: A total of 120 patients 50 years of age or older who were on the practice roster and who were considered by their family physicians to be at risk of experiencing adverse health outcomes. INTERVENTION: A pharmacist and 1 of 3 nurse practitioners visited each patient at his or her home, conducted a comprehensive medication review, and developed a tailored plan to optimize medication use. The plan was developed in consultation with the patient and the patient's doctor. We assessed medication appropriateness at the study baseline and again 12 to 18 months later. MAIN OUTCOME MEASURES: We used the medication appropriateness index to assess medication use. We examined associations between personal characteristics and inappropriate use at baseline and with improvements in medication use at the follow-up assessment. We recorded all drug problems encountered during the trial. RESULTS: At baseline, 27.2% of medications were inappropriate in some way and 77.7% of patients were receiving at least 1 medication that was inappropriate in some way. At the follow-up assessments these percentages had dropped to 8.9% and 38.6%, respectively (P < .001). Patient characteristics that were associated with receiving inappropriate medication at baseline were being older than 80 years of age (odds ratio [OR] = 5.00, 95% CI 1.19 to 20.50), receiving more than 4 medications (OR = 6.64, 95% CI 2.54 to 17.4), and not having a university-level education (OR = 4.55, 95% CI 1.69 to 12.50). CONCLUSION: We observed large improvements in the appropriate use of medications during this trial. This might provide a mechanism to explain some of the reductions in mortality and morbidity observed in other trials of counseling and advice provided by pharmacists and nurses. TRIAL REGISTRATION NUMBER: NCT00238836 (ClinicalTrials.gov).
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".