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Record W4307947741 · doi:10.3390/healthcare10112160

The Implementation of a Geriatrics Co-Management Model of Care Reduces Hospital Length of Stay

2022· article· en· W4307947741 on OpenAlexaff
Homero Teixeira Leite, Alex C. Manhães, Luisa A. Antunes, Tevy Chan, Guy Hajj‐Boutros, José Morais

Bibliographic record

VenueHealthcare · 2022
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsMcGill University Health CentreMcGill University
Fundersnot available
KeywordsMedicineGeriatricsGeriatric careEmergency medicineNursing

Abstract

fetched live from OpenAlex

(1) Background: Older adults comprise a large proportion of hospitalized patients. Many are frail and require complex care. Geriatrics has developed models of care specific to this inpatient population. Our objective was to demonstrate the effect of a geriatric co-management team on clinical administrative indicators of care in Clinical Teaching Units (CTUs) that have adopted the Age-friendly Hospital (AFH) principles in Brazilian hospitals. (2) Methods: Following 3 months of implementation of the AFH principles in CTUs, two periods of the same 6 months of two consecutive years were compared. (3) Results: The total number of participants in the study was 641 and 743 in 2015 and 2016, respectively. Average length of patient-stay (length of stay: 8.7 ± 2.7 vs. 5.4 ± 1.7 days) and number of monthly complaints (44.2 ± 6.5 vs. 13.5 ± 2.2) were significantly lower with the co-management model. Number of homecare service referrals/month was also significantly higher (2.5 ± 1 vs. 38.3 ± 6.3). The 30-day readmission rates and total hospital costs per patient remained unchanged. (4) Conclusion: The presence of a geriatric co-management team in CTUs is of added benefit to increase the efficiency of the AFH for vulnerable older inpatients with reduced LOS and increased referrals to homecare services without increasing hospital costs.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.277

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.023
GPT teacher head0.353
Teacher spread0.330 · 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 designQualitative
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

Citations6
Published2022
Admission routes1
Has abstractyes

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