Clinical benefits and economic impact of post-surgical care provided by pharmacists in a Canadian hospital
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it