610 From Flames to Facts: Unveiling Discrepancies in Burn Patient Documentation
Notice bibliographique
Résumé
Abstract Introduction Comprehensive and accurate burn documentation is essential for initial and ongoing patient care. However, the challenge of inaccurate and incomplete records poses preventable risks. Our study evaluates burn documentation at a tertiary care burn centre, focusing on discrepancies between initial Emergency Department (ED) assessments and final evaluations by the Plastic Surgery Burn Consultant (PS). We hypothesize that the ED reports inaccurate and incomplete burn injury details, leading to differences in burn size and severity compared to PS assessments. Methods We conducted a retrospective review of our provincial burn registry from January 1, 2016, to December 31, 2021. We included patients admitted for burns warranting a PS consultation, excluding isolated first-degree, ocular, and inhalational burns, and those not requiring burn unit admission. Data covering time, date, etiology, injury details, treatment, and follow-up were collected and compared between ED and PS records. Incomplete entries lacked burn-specific data points. Mann-Whitney and Kruskal-Wallis H tests were used to compare continuous outcomes, while Pearson’s Chi-Square test was employed for categorical outcomes. Wilcoxon’s Signed-Rank test was used to identify significant variations in TBSA estimates, with PS considered the "gold" standard. Statistical significance was set at p< 0.05. Results 358 patients were included, with most burns in male patients (76%) occurring at home and involving the head and neck. Burn etiology, circumstances, place, and anatomic location were well reported and consistent across PS and ED documentation. However, there were significant differences in TBSA estimates. The ED calculated a median TBSA of 20 (IQR: 19.8), while PS estimated a median TBSA of 14 (IQR: 16) (p< 0.0019). Notably, TBSA estimates for burns < 10% and 10-25% showed significant differences (p< 0.0001), tending toward overestimation by the ED. Deeper burns were consistently over-reported during initial ED assessments compared to the final PS determinations. Furthermore, 81% of the initial records were incomplete: 66% lacked initial treatment data, 49% missed TBSA, and 39% omitted burn depth. Conclusions Significant discrepancies were appreciated in the initial ED documentation of burn injuries at our tertiary care burn centre, with overestimations in TBSA and burn depth. Over 80% of initial documentation was incomplete, with TBSA omitted in 49% of charts in both local and peripheral transfer consultations. There is a collective urgent need for enhanced awareness and education on the importance of accurate and comprehensive burn patient documentation. Applicability of Research to Practice This research emphasizes the need for improved documentation strategies for burn patients seen in the acute care setting, raising the potential to venture into modalities such as artificial intelligence and electronic health record systems to enhance burn documentation.
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 | 0,035 | 0,193 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,008 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,001 | 0,004 |
| 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 ».