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Improving Transitions of Care between the Intensive Care Unit and General Internal Medicine Ward. A Demonstration Study

2020· article· en· W3043425031 on OpenAlexaff
Thomas Bodley, James Rassos, Wasim Mansoor, Chaim M. Bell, Michael E. Detsky

Bibliographic record

VenueATS Scholar · 2020
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsSinai Health SystemUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsRapid response teamMedicineIntensive care unitAuditEmergency medicineMedical emergencyHospital medicineNursingFamily medicineIntensive care medicine

Abstract

fetched live from OpenAlex

Abstract Background In-hospital transfers such as from the intensive care unit (ICU) to the general internal medicine (GIM) ward place patients at risk of adverse events. A structured handover tool may improve transitions from the ICU to the GIM ward. Objective To develop, implement, and evaluate a customized user-designed transfer tool to improve transitions from the ICU to the GIM ward. Methods This was a pre–post intervention study at a tertiary academic hospital. We developed and implemented a user-designed, structured, handwritten ICU-to-GIM transfer tool. The tool included active medical issues, functional status, medications and medication changes, consulting services, code status, and emergency contact information. Transfer tool users included GIM physicians, ICU physicians, and critical care rapid response team nurses. An implementation audit and mixed qualitative and quantitative analysis of pre–post survey responses was used to evaluate clinician satisfaction and the perceived quality of patient transfers. Results The pre–post survey response rate was 51.8% (99/191). Respondents included GIM residents (58.5%), ICU rapid response team physicians and nurses (24.2%), and GIM attending physicians (17.2%). Less than half of clinicians (48.8%) reported that the preintervention transfer process was adequate. Clinicians who used the transfer tool reported that the transfer process was improved (93.3% vs. 48.8%, P = 0.03). Clinician-reported understanding of medication changes in the ICU increased (69.2% vs. 29.1%, P = 0.004), as did their ability to plan for a safe hospital discharge (69.2% vs. 31.0%, P = 0.01). However, only 64.2% of audited transfers used the tool. Frequently omitted sections included home medications (missing in 83.4% of audits), new medications (33.3%), and secondary diagnosis (33.3%). Thematic analysis of free-text responses identified areas for improvement including clarifying the course of ICU events and enhancing tool usability. Conclusion A user-designed, structured, handwritten transfer tool may improve the perceived quality of patient transfers from the ICU to the GIM wards.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score0.299

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 head0.037
GPT teacher head0.316
Teacher spread0.279 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

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