Clinical benefits and economic impact of post-surgical care provided by pharmacists in a Canadian hospital
Bibliographic record
Abstract
OBJECTIVE: Clinical pharmacists improve the quality of patient care by reducing adverse drug events (ADEs), length of stay and mortality. This impact is currently not well described in surgery. The objective was to evaluate clinical and economic outcomes after clinical pharmacist services were added to two general surgical wards in an adult hospital. METHODS: This was a prospective, observational study. All clinical interventions to resolve drug therapy problems were documented and assessed for severity, value and the probability of preventing an ADE. Cost avoidance was calculated using two methods: by avoiding additional days in hospital (CA$3593/ADE) or additional hospital costs ($7215/ADE). Two clinical pharmacy specialists and the surgical care pharmacist independently categorized the interventions; disagreements were resolved by consensus. KEY FINDINGS: The pharmacists made 1097 interventions in 6 months with a 98% acceptance rate by surgical staff. Half of the interventions were rated significant for severity (561, 51.1%) and value (559, 51.0%). One-quarter of the interventions had a 40% or greater probability of preventing an ADE (270, 24.6%). Cost avoidance was estimated to be $0.68-1.36 million or $617-1239 per intervention. Pharmacists avoided an additional 867 days in the hospital for surgical patients. CONCLUSION: The pharmacist's role in the management of the drug therapy needs of the post-surgical patient has the potential to improve clinical and patient outcomes and avoid healthcare costs. The inclusion of clinical pharmacists in surgical wards may result in $7 in savings for every $1 invested.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".