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Record W4200552282 · doi:10.1186/s13054-021-03857-2

Patient discharge from intensive care: an updated scoping review to identify tools and practices to inform high-quality care

2021· article· en· W4200552282 on OpenAlexafffund
Kara M. Plotnikoff, Karla D. Krewulak, Laura Hernández, Krista Spence, Nadine E. Foster, Shelly Longmore, Sharon E. Straus, Daniel J. Niven, Jeanna Parsons Leigh, Henry T. Stelfox, Kirsten M. Fiest

Bibliographic record

VenueCritical Care · 2021
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsDalhousie UniversityUniversity of TorontoSt. Michael's HospitalUniversity of CalgaryAlberta Health Services
FundersCanadian Institutes of Health Research
KeywordsCINAHLMedicineWorkloadMEDLINEHealth careHospital dischargeQuality managementIntensive care unitNursingDischarge planningEmergency medicineFamily medicineMedical emergencyIntensive care medicinePsychological intervention

Abstract

fetched live from OpenAlex

BACKGROUND: Critically ill patients require complex care and experience unique needs during and after their stay in the intensive care unit (ICU). Discharging or transferring a patient from the ICU to a hospital ward or back to community care (under the care of a general practitioner) includes several elements that may shape patient outcomes and overall experiences. The aim of this study was to answer the question: what elements facilitate a successful, high-quality discharge from the ICU? METHODS: This scoping review is an update to a review published in 2015. We searched MEDLINE, EMBASE, CINAHL, and Cochrane databases from 2013-December 3, 2020 including adult, pediatric, and neonatal populations without language restrictions. Data were abstracted using different phases of care framework models, themes, facilitators, and barriers to the ICU discharge process. RESULTS: We included 314 articles from 11,461 unique citations. Two-hundred and fifty-eight (82.2%) articles were primary research articles, mostly cohort (118/314, 37.6%) or qualitative (51/314, 16.2%) studies. Common discharge themes across all articles included adverse events, readmission, and mortality after discharge (116/314, 36.9%) and patient and family needs and experiences during discharge (112/314, 35.7%). Common discharge facilitators were discharge education for patients and families (82, 26.1%), successful provider-provider communication (77/314, 24.5%), and organizational tools to facilitate discharge (50/314, 15.9%). Barriers to a successful discharge included patient demographic and clinical characteristics (89/314, 22.3%), healthcare provider workload (21/314, 6.7%), and the impact of current discharge practices on flow and performance (49/314, 15.6%). We identified 47 discharge tools that could be used or adapted to facilitate an ICU discharge. CONCLUSIONS: Several factors contribute to a successful ICU discharge, with facilitators and barriers present at the patient and family, health care provider, and organizational level. Successful provider-patient and provider-provider communication, and educating and engaging patients and families about the discharge process were important factors in a successful ICU discharge.

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)0.006
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 score0.405
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.006
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 head0.206
GPT teacher head0.515
Teacher spread0.309 · 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

Citations70
Published2021
Admission routes2
Has abstractyes

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