982 Quality Conversations: Addressing the Problem of Pressure Injuries in Burn ICU
Notice bibliographique
Résumé
Abstract Introduction In 2023 the results of an annual hospital acquired pressure injury (HAPI) audit showed increased incidence of HAPI in ICUs at our Level 1 Trauma Center. Our ABA Verified Burn Centre was among the units showing increased HAPI. After sharing this data with front line staff we sought to engage them about their perceptions of why there were more HAPIs compared to previous years and how we can move towards HAPI prevention. Methods We a quality improvement strategy called Quality Conversations to co-create an HAPI prevention plan with frontline staff. Quality Conversations are staff huddles held at a designed time each week. At these huddles staff were asked to identify root causes of HAPI in burn patients in four domains: The Provider, The Patient, The Organization and The Equipment. This data was collected over several weeks and was enhanced by engaging staff in many formats including: in person; by email; Quality Conversation board posted in the unit allowing staff to add input freely. The most common responses were tabulated and shared with staff. A second phase of Quality Conversation asked staff to explore ways to prevent HAPIs based on the challenges the had identified in the first round. The HAPI prevention strategies derived from staff input were organized into a Safe Turn Checklist specific to our Burn Centre. The list was shared with staff and trailed in patient rooms. The Quality Conversation board was then used to shared monthly HAPI reports with staff including: Summary of incident reports; Percent of device related and non-device related HAPIs; Distribution of HAPI by location; Distribution of HAPI by device. Results At total of 79 unique responses were collected and analyzed. Respondents were mainly nurses but also included Personal Support Workers, Administrative Assistants, Physicians, Patient Care Manager, Advanced Practice Nurse, Occupation and Physical Therapists. Results were tabulated on a Pareto Chart and identified 6 root causes of HAPI in Burn patients: Lack of help to support q2h turns; Not removing wet linens; Multiple layers; Heavy patients; Support staff occupied in dressings; high proportion of agency staff who are unfamiliar with unit protocols. Through this exercise and sharing of data, awareness of HAPI and the need to prevent them was enhanced at our Burn Centre. Conclusions The Quality Conversation process embodies principles of burn care by providing a forum for staff to engage in quality improvement both individually and as an inter-professional team. Staff voice was used to drive both the root cause analysis and tools for change. Applicability of Research to Practice Our use of Quality Conversations demonstrates the power of shared governance and staff engagement to drive increased awareness and dissemination of best practices at the bedside. We believe that burn teams are an invaluable resource. As such, leaders must show staff caring for burn patients that they are valued by hearing and implementing their ideas whenever possible. Funding for the Study We did not receive funding for this project.
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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 | 0,017 | 0,051 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,011 | 0,003 |
| Communication savante | 0,007 | 0,006 |
| Science ouverte | 0,002 | 0,013 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
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 ».