Primary Palliative Care in the Emergency Department and Acute Care Setting.
Satheesh GunagaJonathan ZygowiecPublished in: Cancer treatment and research (2023)
Amidst a global COVID pandemic, the palliative care community and healthcare systems around the country continue to explore opportunities to improve early patient and family access to end-of-life care resources. They need not look any further than the Emergency Departments (ED) located on their campuses and around their communities for this chance. As advances in medical therapies continue to extend disease specific life expectancies and as the American population continues to age, we will continue to see older adults with chronic medical illnesses visiting the ED in their final stages of life (Smith et al. in Health Aff (Millwood) 31(6):1277-1285, 2012; Albert et al. in NCHS Data Brief 130:1-8, 2013). If the ED is to continue to be the primary portal of hospital entry for patients requiring emergent care for acute and chronic terminal illnesses, then it stands to reason that it should also be equally prepared to provide the earliest access to palliative care and advance care planning resources for patients and families who may want and benefit from these services. This chapter will explore the unique horizon of opportunities that exist for emergency medicine and the palliative care specialty to fulfill this obligation. Discussion will be centered around core principles in screening, assessment, and management of palliative care needs in the ED, importance of goals of care conversations, and the coordination of early palliative care and hospice consults that can facilitate safe transitions of care.
Keyphrases
- palliative care
- healthcare
- emergency department
- advanced cancer
- end stage renal disease
- chronic kidney disease
- ejection fraction
- acute care
- mental health
- advance care planning
- public health
- prognostic factors
- emergency medicine
- peritoneal dialysis
- primary care
- quality improvement
- machine learning
- liver failure
- drug induced
- risk assessment
- deep learning
- patient reported outcomes
- artificial intelligence
- big data