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Record W2887562947 · doi:10.1111/ijpp.12481

Integration of a clinical pharmacist into a Canadian, urban emergency department: a prospective observational study

2018· article· en· W2887562947 on OpenAlexaffabout
Lindsay Dryden, Norman Dewhurst

Bibliographic record

VenueInternational Journal of Pharmacy Practice · 2018
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicinePsychological interventionEmergency departmentPharmacistObservational studyClinical pharmacyEmergency medicineIntervention (counseling)Family medicinePharmacyNursingInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the clinical and cost implications generated by a newly integrated ED pharmacist in a Canadian urban, university-affiliated tertiary care hospital. METHODS: A pharmacist documented all interventions that took place over a 5-week period. Interventions were assessed by a review panel for clinical significance and probability of harm had the intervention not occurred. Direct medication cost and cost avoidance as a result of interventions were calculated. KEY FINDINGS: The ED pharmacist made 421 interventions during the study period, 204 (48%) interventions were accepted at the time they were presented to the prescriber. After review, 53.9% of interventions were considered significant, and 52.9% were given a probability of patient harm of ≥50% had the intervention not occurred. Interventions resulted in an increase in direct medication costs of $1270, but generated a cost avoidance of $160 709. The projected direct medication cost estimate for one year was $13 208 with a cost avoidance of over $1.6 million. CONCLUSION: The integration of a pharmacist into a Canadian ED resulted in patient care interventions that were assessed as clinically significant, with a substantial projected cost avoidance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.406
GPT teacher head0.584
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2018
Admission routes2
Has abstractyes

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