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Record W2081037419 · doi:10.1287/mnsc.1070.0839

Does the Standardization Process Matter? A Study of Cost Effectiveness in Hospital Drug Formularies

2008· article· en· W2081037419 on OpenAlexaff
Seok‐Woo Kwon

Bibliographic record

VenueManagement Science · 2008
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsStandardizationProcess (computing)Argument (complex analysis)PharmacyProcess managementCost effectivenessMedicineComputer scienceRisk analysis (engineering)BusinessFamily medicine

Abstract

fetched live from OpenAlex

While research on the cost effectiveness of standardization to date has focused on the impact of different degrees of standardization, it has paid insufficient attention to the process an organization uses to formulate and implement standardized procedures. Drawing on the organizational decision-making literature and procedural fairness literature, this study identifies a number of key process features in standardization and argues that variations in these process features across organizations help account for the varying success of standardization in achieving cost effectiveness. Using hospital drug standardization for coronary artery and pneumonia cases to ground much of the argument, I analyzed inpatient discharge data from Florida, Illinois, New York, and Texas, combined with an original survey of 243 hospital pharmacy directors. The results indicated that an increasing degree of standardization was associated with cost effectiveness when the level of formal objectivity in creating the standardized procedure was high and when there was due process in resolving disputes about the standardized procedure. This finding broadly supports the argument that the cost effectiveness of standardization depends not just on the degree of standardization but also on the process by which the standardized procedures are created and implemented.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.194
GPT teacher head0.521
Teacher spread0.327 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2008
Admission routes1
Has abstractyes

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