Palliative Care and Burn Care: Empirically-Derived Referral Criteria.
Daniel H GrossoehmeValerie ShanerSarah FriebertMiraides BrownStephanie SteinerAnjay KhandelwalShari W EickmeyerEsther TeoCarrie BrownRichard LouPublished in: Journal of burn care & research : official publication of the American Burn Association (2022)
Burns frequently require complex interdisciplinary care. Specialist palliative care (PC) minimizes suffering, aids in decision making, and provides family support in addition to end-of-life care. Specialist PC is a limited resource, best conserved by identifying persons most likely to benefit from a PC referral. Little guidance is available for clinicians on whether and when to refer to PC. This study's purpose was to identify referral criteria using a mixed-methods approach. Data were examined for between-group differences using Fisher's exact, Chi-square, or Wilcoxon Rank Sum tests. Qualitative thematic analysis was used to analyze PC provider notes to describe interventions provided. These data formed initial referral criteria, which were reviewed by an expert panel. Significant between-group differences included dying in the burn center; whether multiple patients were transported to the burn center from one event; and ventilator days. Four themes emerged from qualitative analysis. These included managing physical aspects of care; clarifying goals of care; managing end-of-life care; and managing patient/family psychosocial distress. Expert panel input clarified referral criteria language and supplemented the proposed criteria. We present empirically-derived referral criteria to guide burn providers in referring persons for specialist PC. Subsequent testing is required to determine their efficacy in improving patient/family outcomes.
Keyphrases
- palliative care
- advanced cancer
- primary care
- decision making
- healthcare
- case report
- mental health
- physical activity
- ejection fraction
- type diabetes
- newly diagnosed
- wound healing
- electronic health record
- quality improvement
- public health
- prognostic factors
- transcription factor
- metabolic syndrome
- clinical practice
- adipose tissue
- machine learning
- data analysis
- pain management
- density functional theory