Scoping Review of Interventions Associated with Cost Avoidance Able to Be Performed in the Intensive Care Unit and Emergency Department
Bibliographic record
Abstract
A framework for evaluating pharmacists' impact on cost avoidance in the intensive care unit (ICU) and emergency department (ED) has not been established. This scoping review was registered (CRD42018091217) and conducted to identify, aggregate, and qualitatively describe the highest quality evidence for cost avoidance generated by clinical pharmacists on interventions performed in an ICU or ED. Searches were conducted in PubMed, Scopus, CINAHL, Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews from inception until April 2018. The level of evidence (LOE) for each specific category of intervention was evaluated according to the Grading of Recommendations, Assessment, Development and Evaluation evidence-to-decision framework. The risks of bias for articles were evaluated using Newcastle Ottawa and Cochrane Collaboration tools. The values from all interventions were inflated to 2018 U.S. dollars using the consumer price index for medical care. Of the 464 articles initially identified, 371 were excluded and 93 were included. After reviewing references from the articles included, an additional 71 articles were also reviewed. The 38 cost intervention categories were supported by varying LOEs: IA (0 categories), IB (1 category), IIA (4 categories), IIB (0 categories), III (27 categories), and IV (6 categories), and articles mostly displayed low to moderate risks of bias. Pharmacists generate cost avoidance through a variety of interventions in critically and emergently ill patients. The quality of evidence supporting specific cost avoidance values is generally low. Quantification of and factors associated with the cost avoidance generated from pharmacists caring for these patients are of paramount importance.
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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 | 0.044 | 0.191 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.027 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".