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Record W4394873232 · doi:10.1111/hex.14050

Decoding the persistence of delayed hospital discharge: An in‐depth scoping review and insights from two decades

2024· article· en· W4394873232 on OpenAlexafffund
Alyaa Abdelhalim, Manaf Zargoush, Norm Archer, Mehrdad Roham

Bibliographic record

VenueHealth Expectations · 2024
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPersistence (discontinuity)Hospital dischargePsychologyData scienceComputer scienceMedicineIntensive care medicineEngineering

Abstract

fetched live from OpenAlex

OBJECTIVE: This article addresses the persistent challenge of Delayed Hospital Discharge (DHD) and aims to provide a comprehensive overview, synthesis, and actionable, sustainable plan based on the synthesis of the systematic review articles spanning the past 24 years. Our research aims to comprehensively examine DHD, identifying its primary causes and emphasizing the significance of effective communication and management in healthcare settings. METHODS: We conducted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) method for synthesizing findings from 23 review papers published over the last two decades, encompassing over 700 studies. In addition, we employed a practical and comprehensive framework to tackle DHD. Rooted in Linderman's model, our approach focused on continuous process improvement (CPI), which highlights senior management commitment, technical/administrative support, and social/transitional care. Our proposed CPI method comprised several stages: planning, implementation, data analysis, and adaptation, all contributing to continuous improvement in healthcare delivery. This method provided valuable insights and recommendations for addressing DHD challenges. FINDINGS: Our DHD analysis revealed crucial insights across multiple dimensions. Firstly, examining causes and interventions uncovered issues such as limited discharge destinations, signaling unsustainable solutions, and inefficient care coordination. The second aspect explored the patient and caregiver experience, emphasizing challenges linked to staff uncertainty and negative physical environments, with notable attention to the underexplored area of caregiver experience. The third theme explored organizational and individual factors, including cognitive impairment and socioeconomic influences. The findings emphasized the importance of incorporating patients' data, recognizing its complexity and current avoidance. Finally, the role of transitional and social care and financial strategies was scrutinized, emphasizing the need for multicomponent, context-specific interventions to address DHD effectively. CONCLUSION: This study addresses gaps in the literature, challenges prevailing solutions, and offers practical pathways for reducing DHD, contributing significantly to healthcare quality and patient outcomes. The synthesis introduces the vital CPI stage, enhancing Linderman's work and providing a pragmatic framework to eradicate delayed discharge. Future efforts will address practitioner consultations to enhance perspectives and further enrich the study. PATIENT OR PUBLIC CONTRIBUTION: Our scoping review synthesizes and analyzes existing systematic review articles and emphasizes offering practical, actionable solutions. While our approach does not directly engage patients, it strategically focuses on extracting insights from the literature to create a CPI framework. This unique aspect is intentionally designed to yield tangible benefits for patients, service users, caregivers, and the public. Our actionable recommendations aim to improve hospital discharge processes for better healthcare outcomes and experiences. This detailed analysis goes beyond theoretical considerations and provides a practical guide to improve healthcare practices and policies.

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: none
Teacher disagreement score0.624
Threshold uncertainty score0.204

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 head0.051
GPT teacher head0.386
Teacher spread0.335 · 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

Citations15
Published2024
Admission routes2
Has abstractyes

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