The Impact of a Clinical Pharmacist on Patient and Economic Outcomes in a Child and Adolescent Mental Health Unit
Bibliographic record
Abstract
ABSTRACT Background and Objective: It has been shown that clinical pharmacists positively influence quality of care and decrease drug expenditures in hospital settings. This 2-part study was undertaken to evaluate the impact of a clinical pharmacist on patient and economic outcomes in a pediatric mental health setting. Methods: In the first part of the study, a 4-week prospective evaluation period, pharmacist-initiated interventions were documented. This information was distributed to a panel of assessors, who determined the impact of each intervention on patient care. In the second part of the study, a retrospective cost analysis was used to compare drug costs for 2 consecutive years, the 12-month period before and the 12-month period immediately after institution of the clinical pharmacy position. A matched-pair t-test and regression analysis were conducted on the cost data. Results: The pharmacist initiated 48 interventions during the 4-week period, 47 (98%) of which were accepted by the treating physician. Eighty-six percent (38/44) of the interventions were assessed as having a positive effect on patient care. Drug cost per patient-day was 14% lower in the year after implementation of the pharmacy position, and the difference was statistically significant in the last 8 months of that year (p = 0.0019). Total drug costs decreased by 21%, a cost saving of $5485.80. Conclusions: The clinical pharmacist had a positive impact on both clinical and economic outcomes in this pediatric mental health population. RESUME Historique et objectif : On a demontre que les pharmaciens cliniciens influaient positivement sur la qualite des soins et faisaient baisser le cout des medicaments en milieu hospitalier. Cette etude de deux phases a ete menee pour evaluer l’incidence des interventions du pharmacien clinicien en termes de resultats therapeutiques et de repercussions economiques dans un etablissement de soins de sante mentale pour enfants. Methodes : Dans la premiere phase de l’etude, soit une periode d’evaluation prospective de quatre semaines, les interventions entreprises par le pharmacien ont ete documentees. L’information ainsi recueillie a ete distribuee a un comite qui a evalue l’incidence de chaque intervention sur les soins au patient. Dans la seconde phase de l’etude, une analyse retrospective des couts — au moyen d’un test de Student apparie et d’une analyse de regression — a ete menee pour comparer le cout des medicaments sur une periode de deux annees consecutives, soit la periode de douze mois avant et celle immediatement apres la mise en place du poste de pharmacien clinicien. Resultats : Des 48 interventions entreprises par le pharmacien durant la periode de quatre semaines, 47 (98 %) ont ete acceptees par le medecin traitant et 86 % (38/44) se sont revelees avoir un effet positif sur les soins aux patients. Le cout des medicaments par jour-patient etait 14 % plus bas dans l’annee suivant la mise en place du poste de pharmacien clinicien, et on a observe une difference statistiquement significative dans les huit derniers mois de cette annee (p = 0,0019). Le cout total des medicaments a diminue de 21 %, ce qui s’est traduit par des economies de 5 485,80 $. Conclusion : Le pharmacien clinicien a eu une incidence positive a la fois sur les resultats therapeutiques et sur les couts au sein de cette population d’enfants en milieu de soins de sante mentale.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".