Sociotechnical Needs of Registered Nurses in the Heart Failure Hospitalizations of African American Patients: Cross-Sectional Study
Notice bibliographique
Résumé
BACKGROUND: African Americans are disproportionately impacted by congestive heart failure (CHF). The impact includes a two and a half times greater hospitalization rate and a fourth of a day longer length of hospitalization than Caucasians, of which nursing care has been associated with nearly a 30% decrease in hospitalizations and readmissions. Prior studies have demonstrated that registered nurses (RNs), working in conjunction with electronic health record systems (EHRs) to conduct care tasks, may optimize length of stay in African Americans with CHF. OBJECTIVE: The objective of the study was to identify the needs of RNs who performed socio-technical tasks, the perceived importance of these socio-technical tasks, and the perceived performance of these tasks by RNs, in relation to the length of stay of their African American patients with CHF. METHODS: The study employed an observational, cross-sectional survey design in RNs who were randomly selected from a total population of 3,498 RNs who provided care to 22,703 African Americans with CHF within 113,543 heart failure hospitalizations between January 1, 2015, and January 1, 2024. The RNs were retrospectively stratified into two groups based on EHR data: those whose African American patients had a mean length of stay of 10 days or less (Group A) and those whose mean length of stay was greater than 10 days (Group B). Descriptive statistics, Cohen's d, and a two-sided unpaired t-test were used to analyze the data. RESULTS: The total sample of 200 RNs responded to the survey (100% survey completion rate). Group A (100 RNs) reported the least important task as drawing conclusions about how to use the EHR to care for African Americans (Mean=4.66, SD=1.82). The least important task in Group B (100 RNs) was reading published research on African Americans (mean=4.88, SD=1.70). Group A reported performing best in caring for African American patients (Mean=5.61, SD=1.44). Group B reported performing best at caring for all patients (Mean=5.86, SD=1.04). A total of seventeen significant socio-technical needs were identified among groups. Two socio-technical needs were unique to Group B: caring for patients (i.e., the full scope of social and technological processes in nursing care) (Cohen's d=0.32, 95% CI: 0.04,0.59, P=.04) and working with information related to a patient's CHF in the EHR (e.g., laboratory results, discharge summaries, or radiographic images) to care for the patient (Cohen's d=0.33, 95% CI: 0.05,0.61, P=.03). CONCLUSIONS: Lengths of patient stay may be reduced by identifying and addressing socio-technical needs through targeted training, nursing care interventions, and RN-led risk stratification guidelines for working with EHRs to reduce lengths of stay in those who are disproportionately impacted by CHF. CLINICALTRIAL: N/a.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».