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Enregistrement W2159177854 · doi:10.1542/gr.13-6-65

Hospitalist System versus Housestaff System, And the Winner Is…

2005· article· en· W2159177854 sur OpenAlexaboutno aff

Notice bibliographique

RevueAAP Grand Rounds · 2005
Typearticle
Langueen
DomaineMedicine
ThématiqueHospital Admissions and Outcomes
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineSubspecialtyCitationIconDownloadFamily medicineWorld Wide Web

Résumé

récupéré en direct d'OpenAlex

Source: Dwight P, MacArthur C, Friedman JN, et al. Evaluation of a staff-only hospitalist system in a tertiary care, academic children’s hospital. Pediatrics. 2004;114:1545–1549.In 1995, responding to newly limited resident duty hours, the division of pediatrics at The Hospital for Sick Children in Toronto reorganized inpatient pediatric teams to include 2 distinct hospitalist models: a hospitalist/housestaff model (CTU) and hospitalist staff-only model (CPU). Citing a lack of published data assessing the staff-only pediatric hospitalist model, the authors designed a cohort study of 3807 admissions to the general inpatient pediatric unit between July 1, 1996 and June 30, 1997.Length of stay was the primary outcome measure, and secondary outcome measures included frequency of subspecialty consultation, readmission to the hospital, and death. Consultations were measured as none or ≥1, and readmissions were defined as admission within 7 days of discharge for the same or a related diagnosis. Clinically relevant information collected for each patient included age, gender, referral source, stay in a special care unit, most responsible diagnosis, and comorbidity. Comorbidity was defined as a stay complicated by a chronic illness, serious or important conditions, and/or a potentially life-threatening condition. The CTU team had a daily census of 24 to 30 patients and consisted of 1 attending pediatrician, 3–4 pediatric residents, and 2 medical students. CTU pediatricians attended this service 4 to 8 weeks each year. The CPU was staffed with 3 pediatricians who were responsible for all aspects of care Monday through Friday and on weekends during daytime. Medical students were included on this team. During nights and weekends clinical fellows not otherwise associated with the CPU team provided coverage. Each CPU physician maintained a daily census of 8 to 10 patients. These physicians spent approximately 11 months of the year providing inpatient care.During the study there were 3807 admissions, of which 33% were to the CPU and 67% were to the CTU (based on maintaining a census of 24–30 on the CTU, with the remainder assigned to the CPU). Patients admitted to the CPU were older (median age: 95 weeks vs 69 weeks, P<.01) and less likely to have comorbidity (24% vs 30%; P<.01). The patient diagnoses for the 2 teams were not significantly different. The median length of hospital stay for the CPU team was 2.5 days (interquartile range [IQR]: 1.6–4.4 days) versus 2.9 days (IQR: 1.8–4.9) for the CTU team (P<.01). Multivariate linear regression demonstrated that this difference in length of stay remained significant after adjustment for age, gender, and comorbidity (P<.04). Stratified analysis of the 10 most frequent diagnoses demonstrated a shorter median length of stay on the CPU team for these diagnoses when combined as a group (2.1 days vs 2.6 days, P<.01). There was no significant difference between the 2 teams with respect to readmissions, frequency of consultation, or death.Dr. pate has disclosed no financial relationships relevant to this commentary.Demonstrating improved efficiency without an increase in morbidity and mortality is an important first step in evaluation of a new model of providing inpatient care. The decrease in median length of stay shown in this study, although statistically significant, is not clearly clinically and/or financially significant and the study was not designed to test the authors’ assertion that this shortened length of stay might positively affect hospital efficiency by improving throughput. Further investigation needs to be directed at measuring the effect that a hospitalist-only service integrated with a traditional resident-attending service will have on “non-clinical” variables such as resident patient encounters, resident education, and the satisfaction of residents, medical staff, and patients. These effects could be significant and unique.In the United States, pediatric inpatient admissions increased an average of 16% between 1998 and 2002.1 As of July 1, 2003, the Accreditation Council for Graduate Medical Education (ACGME) limited the availability of resident physicians to an 80-hour weekly work limit and 24-hour continuous on-duty time.2 The disparity created between a growing pediatric inpatient census and a more tightly controlled pediatric resident workforce will create a need for novel solutions, and the independent pediatric hospitalist practicing parallel with a resident-attending team is a potential solution.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,578
Score d'incertitude au seuil0,384

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,010
Tête enseignante GPT0,250
Écart entre enseignants0,240 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2005
Routes d'admission1
Résumé présentoui

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