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Cost-benefit analysis of pharmacist interventions over 36 months in a university hospital

2020· article· en· W3091147512 on OpenAlexaff
Maurílio de Souza Cazarim, João Paulo Vilela Rodrigues, Priscila Santos Calcini, Thomas R. Einarson, Leonardo Régis Leira Pereira

Bibliographic record

VenueRevista de Saúde Pública · 2020
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsOntario Drug Policy Research NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicinePsychological interventionPharmacyMarginal costCost analysisPharmacistTotal costEmergency medicinePublic healthAverage costClinical pharmacyProspective cohort studyPediatricsInternal medicineFamily medicineNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: To perform a cost-benefits analysis of a clinical pharmacy (CP) service implemented in a Neurology ward of a tertiary teaching hospital. METHODS: This is a cost-benefit analysis of a single arm, prospective cohort study performed at the adult Neurology Unit over 36 months, which has evaluated the results of a CP service from a hospital and Public Health System (PHS) perspective. The interventions were classified into 14 categories and the costs identified as direct medical costs. The results were analyzed by the total and marginal cost, the benefit-cost ratio (BCR) and the net benefit (NB). RESULTS: The total 334 patients were followed-up and the highest occurrence in 506 interventions was drug introduction (29.0%). The marginal cost for the hospital and avoided cost for PHS was US$182±32 and US$25,536±4,923 per year; and US$0.55 and US$76.4 per patient/year. The BCR and NB were 0.0, -US$26,105 (95%CI -31,850 - -10,610), -US$27,112 (95%CI -33,160-11,720) for the hospital and; 3.0 (95%CI 1.97-4.94), US$51,048 (95%CI 27,645-75,716) and, 4.6 (95%CI 2.24-10.05), US$91,496 (95%CI 34,700-168,050; p < 0.001) for the PHS, both considering adhered and total interventions, respectively. CONCLUSIONS: The CP service was not directly cost-benefit at the hospital perspective, but it presented savings for forecast cost related to the occurrence of preventable morbidities, measuring a good cost-benefit for the PHS.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.487
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.149
GPT teacher head0.414
Teacher spread0.265 · 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
Published2020
Admission routes1
Has abstractyes

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