Families as Partners in Hospital Error and Adverse Event Surveillance
Notice bibliographique
Résumé
Importance: Medical errors and adverse events (AEs) are common among hospitalized children. While clinician reports are the foundation of operational hospital safety surveillance and a key component of multifaceted research surveillance, patient and family reports are not routinely gathered. We hypothesized that a novel family-reporting mechanism would improve incident detection. Objective: To compare error and AE rates (1) gathered systematically with vs without family reporting, (2) reported by families vs clinicians, and (3) reported by families vs hospital incident reports. Design, Setting, and Participants: We conducted a prospective cohort study including the parents/caregivers of 989 hospitalized patients 17 years and younger (total 3902 patient-days) and their clinicians from December 2014 to July 2015 in 4 US pediatric centers. Clinician abstractors identified potential errors and AEs by reviewing medical records, hospital incident reports, and clinician reports as well as weekly and discharge Family Safety Interviews (FSIs). Two physicians reviewed and independently categorized all incidents, rating severity and preventability (agreement, 68%-90%; κ, 0.50-0.68). Discordant categorizations were reconciled. Rates were generated using Poisson regression estimated via generalized estimating equations to account for repeated measures on the same patient. Main Outcomes and Measures: Error and AE rates. Results: Overall, 746 parents/caregivers consented for the study. Of these, 717 completed FSIs. Their median (interquartile range) age was 32.5 (26-40) years; 380 (53.0%) were nonwhite, 566 (78.9%) were female, 603 (84.1%) were English speaking, and 380 (53.0%) had attended college. Of 717 parents/caregivers completing FSIs, 185 (25.8%) reported a total of 255 incidents, which were classified as 132 safety concerns (51.8%), 102 nonsafety-related quality concerns (40.0%), and 21 other concerns (8.2%). These included 22 preventable AEs (8.6%), 17 nonharmful medical errors (6.7%), and 11 nonpreventable AEs (4.3%) on the study unit. In total, 179 errors and 113 AEs were identified from all sources. Family reports included 8 otherwise unidentified AEs, including 7 preventable AEs. Error rates with family reporting (45.9 per 1000 patient-days) were 1.2-fold (95% CI, 1.1-1.2) higher than rates without family reporting (39.7 per 1000 patient-days). Adverse event rates with family reporting (28.7 per 1000 patient-days) were 1.1-fold (95% CI, 1.0-1.2; P = .006) higher than rates without (26.1 per 1000 patient-days). Families and clinicians reported similar rates of errors (10.0 vs 12.8 per 1000 patient-days; relative rate, 0.8; 95% CI, .5-1.2) and AEs (8.5 vs 6.2 per 1000 patient-days; relative rate, 1.4; 95% CI, 0.8-2.2). Family-reported error rates were 5.0-fold (95% CI, 1.9-13.0) higher and AE rates 2.9-fold (95% CI, 1.2-6.7) higher than hospital incident report rates. Conclusions and Relevance: Families provide unique information about hospital safety and should be included in hospital safety surveillance in order to facilitate better design and assessment of interventions to improve safety.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,067 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».