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Record W4396779499 · doi:10.4212/cjhp.3544

Optimizing the Hospital Discharge Process: Perspectives of the Health Care Team

2024· article· en· W4396779499 on OpenAlexafffundvenueabout
Patrick Yeh, Karen Dahri, Michael Legal, Colleen Inglis, Jenifer Tabamo, Kiana Rahnama, Danielle Froese, Leslie Chin

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2024
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsNorth Island CollegeLions Gate HospitalVancouver General HospitalUniversity of British ColumbiaIsland HealthRoyal Columbian Hospital
FundersCanadian Institutes of Health Research
KeywordsMultidisciplinary approachProcess (computing)Health careMultidisciplinary teamNursingHospital dischargeMedicineProcess managementBusinessSociologyComputer scienceIntensive care medicinePolitical science

Abstract

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Background: Prior research capturing pharmacists’ perspectives on the discharge process has shown that their involvement is essential. Given the multidisciplinary nature of the hospital environment, it is important to understand the perspectives of nonpharmacist health care providers. Objectives: To explore the perspectives of nonpharmacist health care providers concerning current discharge practices, components of an effective discharge plan, and perceived barriers to an optimal discharge, and to explore their expectations of pharmacists at discharge. Methods: This qualitative study used key informant interviews of allied health professionals and prescribers at Vancouver General Hospital and North Island Hospital Comox Valley (British Columbia). Participants primarily working on general medicine, family practice, or hospitalist wards were invited to participate. Results: A total of 16 health care providers participated, consisting of 12 allied health professionals and 4 prescribers. Thematic analysis of the interview transcripts revealed 5 themes for each group. The following 3 themes were common to both groups: systems-related barriers to an optimal discharge; patient- and community-related barriers to an optimal discharge; and patient involvement and education. For allied health professionals, themes of prioritization of patients for discharge and direct communication/teamwork were also key for an optimal discharge. Prescriber-specific themes were limitations related to technology infrastructure and inefficiency of existing collaborative processes. Key responsibilities expected of the pharmacist at discharge included preparing the discharge medication reconciliation and prescriptions, addressing medication-related cost concerns, organizing adherence aids/tools, and providing medication counselling. Conclusions: Further studies are warranted to investigate optimization of the discharge process through implementation of standardized discharge protocols and electronic health record–related tools. The primary responsibilities of the pharmacist at discharge, as perceived by study participants, were consistent with previous literature. Keywords: hospital pharmacists, pharmacy services, discharge planning RÉSUMÉ Contexte : Des recherches antérieures recueillant le point de vue de pharmaciens sur le processus associé au congé de l’hôpital ontdémontré que leur implication est essentielle. Compte tenu de la nature multidisciplinaire du milieu hospitalier, il est important de comprendre les perspectives des prestataires de soins de santé non pharmaciens. Objectifs : Étudier les points de vue des prestataires de soins de santé non pharmaciens au sujet des pratiques actuelles relatives au congé, des éléments d’un plan de congé efficace et des obstacles perçus à un congé optimal, et, enfin, prendre connaissance des attentes des prestataires à l’égard des pharmaciens au moment du congé. Méthodologie : Cette étude qualitative a utilisé des entretiens avec des informateurs clés, des professionnels paramédicaux et des prescripteurs au Vancouver General Hospital et au North Island Hospital Comox Valley (en Colombie-Britannique). Les participants travaillant principalement dans les services de médecine générale, de médecine familiale ou d’hospitalisation ont été invités à participer. Résultats : Au total, 16 prestataires de soins de santé ont participé, 12 professionnels paramédicaux et 4 prescripteurs. L’analyse thématique des transcriptions des entretiens a permis d’identifier 5 thèmes pour chaque groupe. Les 3 thèmes suivants étaient communs aux deux groupes : obstacles au congé optimal liés aux systèmes; obstacles au congé optimal liés aux patients et à la communauté; et participation et sensibilisation des patients. Pour les professionnels paramédicaux, les thèmes de la priorisation des patients pour le congé et de la communication directe/ du travail d’équipe étaient essentiels pour un congé optimal. Les thèmes spécifiques aux prescripteurs étaient les limitations liées à l’infrastructure technologique et l’inefficacité des processus de collaboration existants. Les principales responsabilités attendues du pharmacien à la sortie comprenaient la préparation du bilan comparatif des médicaments et des ordonnances au moment du congé, la résolution des problèmes de coûts liés aux médicaments, l’organisation des aides/outils à l’observance et la fourniture de conseils en matière de médication. Conclusions : D’autres études sont nécessaires pour étudier l’optimisation du processus associé au congé grâce à la mise en œuvre de protocoles standardisés et d’outils liés aux dossiers de santé électroniques. Les principales responsabilités du pharmacien au moment du congé, telles que perçues par les participants à l’étude, correspondaient à la littérature antérieure. Mots-clés : pharmaciens hospitaliers, services de pharmacie, planification des congés hospitaliers"

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)0.016
metaresearch head score (Gemma)0.028
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 score0.016
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0080.005
Scholarly communication0.0080.004
Open science0.0020.006
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0030.000

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 head0.043
GPT teacher head0.384
Teacher spread0.341 · 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".

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Citations6
Published2024
Admission routes4
Has abstractyes

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