Articles: palliative-care.
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Pharmacotherapy is essential in palliative medicine. Besides potential benefits, pharmacotherapy also poses potential risks that need to be minimized for patient safety. Pharmacists can play an important role in identifying, solving, and avoiding drug-related problems (DRPs). ⋯ Clinically relevant DRPs are common in palliative medicine. The systematic assessment can support therapy decisions. This can result in optimized drug therapy, subsequently having a positive effect on symptom control and quality of life.
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Palliative medicine · Dec 2021
The impact of digital health interventions on the psychological outcomes of patients and families receiving paediatric palliative care: A systematic review and narrative synthesis.
Digital health interventions are becoming increasingly important and may be particularly relevant for paediatric palliative care. In line with the aims of palliative care, digital health interventions should aim to maintain, if not improve, psychological wellbeing. However, the extent to which the psychological outcomes of digital health interventions are assessed is currently unknown. ⋯ Despite the design and development of digital health interventions that span the technological landscape, little research has assessed their psychosocial impact in the paediatric palliative care community. Whilst the evidence base around the role of these interventions continues to grow, their impact on children and their families must not be overlooked.
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Palliative medicine · Dec 2021
Trends in quality of care and dying perceived by family caregivers of nursing home residents with dementia 2005-2019.
Dementia palliative care is increasingly subject of research and practice improvement initiatives. ⋯ We identified divergent trends over 14 years of increased quality of care, while quality of dying did not increase and well-being in dying decreased. Further research is needed on what well-being in dying means to family. Quality improvement requires continued efforts to treat symptoms in dying with dementia.
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Machine learning (ML) is an emerging tool for predicting need of end-of-life discussion and palliative care, by using mortality as a proxy. But deaths, unforeseen by emergency physicians at time of the emergency department (ED) visit, might have a weaker association with the ED visit. ⋯ In patients discharged to home from the ED, three-quarters of all 30-day deaths did not surprise an adjudicating committee with emergency medicine specialists. When only unsurprising deaths were included, ML mortality prediction improved significantly.