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Record W2143618093 · doi:10.4037/ajcc2013141

Effect of Collaborative Care on Cost Variation in an Intensive Care Unit

2013· article· en· W2143618093 on OpenAlexaff
Allan Garland

Bibliographic record

VenueAmerican Journal of Critical Care · 2013
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineInterquartile rangeIntensivistIntensive care unitSubspecialtyEmergency medicineConfoundingObservational studyHealth careIntensive careIntensive care medicineFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Improving the cost-effectiveness of health care requires an understanding of the genesis of health care costs and in particular the sources of cost variation. Little is known about how multiple physicians, caring collaboratively for patients, contribute to costs. OBJECTIVE: To explore the effect of collaborative care by physicians on variation in discretionary costs in an intensive care unit (ICU) by determining the contributions of the attending intensivists and ICU fellows. METHODS: Prospective, observational study using a multivariable model of median discretionary costs for the first day in the ICU, adjusting for confounding variables. Analysis included 3514 patients who spent more than 2 hours in the ICU on the initial day. Impact of the physicians was assessed via variables representing the specific intensivist and ICU fellow responsible on the first ICU day and allowing for interaction terms. RESULTS: On the initial day, patients spent a median of 10.6 hours (interquartile range, 6.3-16.5) in the ICU, with median discretionary costs of $1343 (interquartile range, $788-2208). There was large variation in adjusted costs attributable to both the intensivists ($359; 95% CI, $244-$474) and the fellows ($756; 95% CI, $550-$965). The interaction terms were not significant (P = .12-.79). CONCLUSIONS: In an ICU care model with intensivists and subspecialty fellows, both types of physicians contributed significantly to the observed variation in discretionary costs. However, even in the presence of a hierarchical arrangement of clinical responsibilities, the influences on costs of the 2 types of physicians were independent.

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)0.001
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 score0.311
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

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

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 head0.032
GPT teacher head0.400
Teacher spread0.369 · 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

Citations4
Published2013
Admission routes1
Has abstractyes

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