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Record W4396779522 · doi:10.4212/cjhp.3545

What Patients Want: A Qualitative Study of Patients’ Perspectives on Optimizing the Hospital Discharge Process

2024· article· en· W4396779522 on OpenAlexafffundvenue
Shun Luo, Karen Mundeep Dahri, Jacqueline Kwok, Colleen Inglis, Jenny Hong, Michael Legal

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2024
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsProvincial Health Services AuthorityUniversity of British ColumbiaIsland HealthCoquitlam College
FundersCanadian Institutes of Health Research
KeywordsDischarge planningPharmacistHospital dischargeMedicineProcess (computing)Qualitative researchPatient dischargeAdverse effectMedical emergencyIntensive care medicineNursingMEDLINEComputer scienceInternal medicinePharmacySociology

Abstract

fetched live from OpenAlex

Background: Poor discharge planning can lead to increases in adverse drug events, hospital readmissions, and costs. Prior research has identified the pharmacist as an integral part of the discharge process. Objectives: To gain patients’ perspectives on the discharge process and what they would like pharmacists to do to ensure a successful discharge. Methods: Twenty patients discharged from tertiary care hospitals were interviewed after discharge. A phenomenological approach was used to conduct this qualitative study. Results: Five main themes were identified from the patient interviews: interactions with health care professionals, importance of discharge documentation, importance of seamless care, comprehensive and patient-specific medication counselling, and patients’ preference for involvement and communication at all stages of hospital stay. Conclusions: Although participants generally reported positive interactions with health care providers at discharge, several areas for improvement were identified, particularly in terms of communication, discharge documentation, and continuity of care. A list of recommendations aligning with patient preferences is provided for clinicians. Keywords: patient discharge, health service needs and demands, health knowledge, patients’ attitudes, pharmacy practice, qualitative research RÉSUMÉ Contexte : Une mauvaise planification du congé hospitalier peut entraîner une augmentation des événements indésirables liés aux médicaments, des réadmissions et des coûts. Des recherches antérieures ont reconnu le pharmacien comme faisant partie intégrante du processus associé au congé de l’hôpital. Objectifs : Recueillir le point de vue des patients sur le processus relatif au congé et sur ce qu’ils aimeraient que les pharmaciens fassent pour assurer la réussite de celui-ci. Méthodologie : Vingt patients d’hôpitaux de soins tertiaires ont été interrogés après leur congé. Cette étude qualitative a été menée en adoptant une approche phénoménologique. Résultats : Cinq thèmes principaux ont émergé à partir des entretiens avec les patients : les interactions avec les professionnels de la santé, l’importance de la documentation au moment du congé, l’importance de soins continus, des conseils complets et spécifiques au patient en matière de médication, et la préférence des patients pour l’implication et la communication à toutes les étapes de leur séjour à l’hôpital. Conclusions : Bien que les participants aient généralement signalé des interactions positives avec les prestataires de soins de santé au moment de leur congé, plusieurs domaines d’amélioration ont été dépistés, notamment sur les plans de la communication, de la documentation au moment du congé et de la continuité des soins. Une liste de recommandations alignées sur les préférences des patients est fournie aux cliniciens. Mots-clés : congé des patients, besoins et demandes en matière de services de santé, connaissances en matière de santé, attitudes des patients, pratique pharmaceutique, recherche qualitative"

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0130.013
Scholarly communication0.0060.008
Open science0.0030.008
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0050.001

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 head0.071
GPT teacher head0.421
Teacher spread0.349 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2024
Admission routes3
Has abstractyes

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