Improving Quality and Safety in Thoracostomy Management: A Digital Chest Drainage System Intervention
Notice bibliographique
Résumé
Background: Trauma care across Vancouver Island is delivered at two trauma centers which serves a population of 864,000. As part of program planning and delivery we routinely monitor Patient Safety Learning System (PSLS) reports, survey staff, and conduct case (MTPRC) reviews. Using these mechanisms, we identified the need to standardize care practices, improve staff training, and simplify the management of thoracostomy tubes. Our intervention introduced a digital chest drainage system to replace the gravity drainage system. Methods: We used a mixed-methods approach to identify practice issues and design and analyse quality improvement (QI) efforts. We analyzed open and directed staff surveys, text parsed bulk PSLS reports, and manual selected MTPRC cases. PSLS and MTPRC responses were coded thematically using conventional content analysis. Staff satisfaction with the QI work was assessed using Wilcoxon signed-ranks testing. Ongoing longitudinal assessment of the QI intervention will be used to describe the clinical impact of the QI intervention. Results Surveys identified that most nurses, 57.5% (n=40), wanted chest tube training. The least understood aspects of care were assessing air leaks, tidalling and excess negativity; and changing the collection canisters. We screened PSLS reports between 2022 and 2024 (n=4300), limited them by catchment area (n=1945), text-parsed them as chest-tube related (n=116), then manually screened them to identify 11 trauma-related chest tube events. Coding of PSLS (n=11) and MTPRC cases (n=14) identified two causal themes: i) management inconsistency (in physician ordering and clinical governance) and, ii) devices issues (chest tubes, securement, collection canisters and space). These cases resulted in delayed care (including prolonged stay) in three cases, unnecessary tube (re)placement (n=12), and clinical deterioration (n=3). Our QI initiative implemented a digital drainage system. Digital systems automatically modulate thoracic negativity, digitally display air-fluid leaks and tidalling, and provide audio-visual alarms and prompts to address pump and collection canister issues. Orientation sessions trained 76.5% of staff. Postimplementation surveys (n=18) showed 61.1% or respondents had used the digital system. Most rated the digital system as safer (z=3.67, r=0.61, p<0.01), easier (z=3.66, r=0.61, p<0.01), superior (z=3.78, r=0.63, p<0.01), and preferable (z=3.87, r=0.64, p<0.01) to gravity drainage. The response was not attributable to difference in the perceived level of training (z=1.41, p=0.16). Ongoing analysis on the clinical effect of the system will be available by the time of the conference. Implications and lessons learned We used small-scale surveys to assess staff, large-scale PSLS surveillance to identify rare safety events, and a mixed-methods approach to identify opportunities for QI. We used digital chest drainage system to address the most cited challenges in chest tube care. As a result, we have seen an increase in staff perceptions of patient safety and ease of care. Ongoing analyses will determine if this initiative is correlated with changes in patient outcomes and safety events.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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
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