Capturing Outcomes of Clinical Activities Performed by a Rounding Pharmacist Practicing in a Team Environment
Bibliographic record
Abstract
BACKGROUND: Medical inpatients are at risk for suboptimal health outcomes from adverse drug events and under-use of evidence-based therapies. We sought to determine whether collaborative care including a team-based clinical pharmacist improves the quality of prescribed drug therapy and reduces hospital readmission. METHODS: Multicenter, quasi-randomized, controlled clinical trial. Consecutive patients admitted to 2 internal and 2 family medicine teams in 3 teaching hospitals between January 30, 2006 and February 2, 2007 were included. Team care patients received proactive clinical pharmacist services (medication history, patient-care round participation, resolution of drug-related issues, and discharge counseling). Usual care patients received traditional reactive clinical pharmacist services. The primary outcome was the overall quality score measured retrospectively by a blinded chart reviewer using 20 indicators targeting 5 conditions. Secondary outcomes included 3- and 6-month readmission. RESULTS: A total of 452 patients (220 team care, 231 usual care, mean age: 74 years, 46% male) met eligibility criteria. Team care patients were more likely than usual care patients to receive care specified by the indicators overall (56.4% vs. 45.3%; adjusted mean difference: 10.4%; 95% confidence interval [CI]: 4.9%, 15.7%) and for each targeted disease state except for heart failure. Team care patients experienced fewer readmissions at 3 months (36.2% vs. 45.5%; adjusted OR: 0.63; 95% CI: 0.42, 0.94) but not at 6 months (50.7% vs. 56.3%; adjusted OR; 0.78; 95% CI: 0.53, 1.15). CONCLUSIONS: In patients admitted to internal and family medicine teams, team-based care including a clinical pharmacist, improved the overall quality of medication use and reduced rates of readmission.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".