553 INTERVENTIONAL PAIN THERAPY IN PALLIATIVE CARE: A CASE SERIES

2006 ◽  
Vol 10 (S1) ◽  
pp. S146-S146
Author(s):  
M. Escher ◽  
A. Cahana ◽  
L. Robert ◽  
S. Pautex
2020 ◽  
Vol 36 (1) ◽  
pp. 46-49
Author(s):  
Colleen Webber ◽  
Aurelia Ona Valiulis ◽  
Peter Tanuseputro ◽  
Valerie Schulz ◽  
Tavis Apramian ◽  
...  

Background: Limited research has characterized team-based models of home palliative care and the outcomes of patients supported by these care teams. Case presentation: A retrospective case series describing care and outcomes of patients managed by the London Home Palliative Care Team between May 1, 2017 and April 1, 2019. Case management: The London Home Palliative Care (LHPC) Team care model is based upon 3 pillars: 1) physician visit availability 2) active patient-centered care with strong physician in-home presence and 3) optimal administrative organization. Case outcomes: In the 18 month study period, 354 patients received care from the London Home Palliative Care Team. Most significantly, 88.4% ( n = 313) died in the community or at a designated palliative care unit after prearranged direct transfer; no comparable provincial data is available. 21.2% ( n = 75) patients visited an emergency department and 24.6% ( n = 87) were admitted to hospital at least once in their final 30 days of life. 280 (79.1%) died in the community. These values are better than comparable provincial estimates of 62.7%, 61.7%, and 24.0%, respectively. Conclusion: The London Home Palliative Care (LHPC) Team model appears to favorably impact community death rate, ER visits and unplanned hospital admissions, as compared to accepted provincial data. Studies to determine if this model is reproducible could support palliative care teams achieving similar results.


2020 ◽  
Vol 34 (5) ◽  
pp. 680-683 ◽  
Author(s):  
Thorleif Etgen

Background: The significance of palliative care consultation in psychiatry is unclear. Actual case series: Analysis of the introduction of palliative care consultation in a large psychiatric hospital. Possible courses of action: Continue without offering, survey the need for or offer palliative care consultation, and analyse its introduction. Formulation of a plan: Palliative care consultation was established and details including patient age, department, diagnosis, main problem, solution and discharge were analysed during the first 2 years. Outcome: Two consultations in the first year and 18 consultations in the second year were requested (18 geriatric, 2 addiction, 0 general, clinical social and forensic psychiatry) involving two domains: delirium associated with dementia or another condition (75%) and mental illness (e.g. alcoholic psycho-syndrome, psychosis, suicidal tendency, schizophrenia, depression) and cancer (25%). Recommendations of consultations were realized in 95%. Lessons from the case series: Implementation of palliative care consultation in psychiatry is one possible method of how to introduce palliative care in a field of medicine with lack of palliative care. View: Future research should focus on reasons for reservations about palliative care in psychiatry, include more patients with severe persistent mental illness and assess the value of palliative care consultation in resolving this problem.


2019 ◽  
Vol 10 (4) ◽  
pp. e31-e31 ◽  
Author(s):  
Joshua R Lakin ◽  
Meghna Desai ◽  
Kyle Engelman ◽  
Nina O'Connor ◽  
Winifred G Teuteberg ◽  
...  

ObjectiveTo describe the strategies used by a collection of healthcare systems to apply different methods of identifying seriously ill patients for a targeted palliative care intervention to improve communication around goals and values.MethodsWe present an implementation case series describing the experiences, challenges and best practices in applying patient selection strategies across multiple healthcare systems implementing the Serious Illness Care Program (SICP).ResultsFive sites across the USA and England described their individual experiences implementing patient selection as part of the SICP. They employed a combination of clinician screens (such as the ‘Surprise Question’), disease-specific criteria, existing registries or algorithms as a starting point. Notably, each describes adaptation and evolution of their patient selection methodology over time, with several sites moving towards using more advanced machine learning–based analytical approaches.ConclusionsInvolving clinical and programme staff to choose a simple initial method for patient identification is the ideal starting place for selecting patients for palliative care interventions. However, improving and refining methods over time is important and we need ongoing research into better patient selection methodologies that move beyond mortality prediction and instead focus on identifying seriously ill patients—those with poor quality of life, worsening functional status and medical care that is negatively impacting their families.


2018 ◽  
Vol 55 (2) ◽  
pp. 719
Author(s):  
Clint Pettit ◽  
Lissa Berroa Garcia ◽  
Sanjeev Krishna Ganesh ◽  
Hunter Groninger

2000 ◽  
Vol 18 (7) ◽  
pp. 1598-1600 ◽  
Author(s):  
David P. Seamans ◽  
Gilbert Y. Wong ◽  
Jack L. Wilson

2013 ◽  
Vol 21 (3) ◽  
Author(s):  
Cornelia Meffert ◽  
Gerhild Becker

SummaryRecent statistics reveal a substantial and even growing need for palliative care in present-day society. Providing adequate pain therapy remains a largely unsolved problem, mainly because of the small number of clinical studies in palliative medicine. Hence, clinical research is urgently needed – and therefore suitable tools to measure outcomes must be developed. Contrary to typical clinical studies, the usual outcome parameters such as decreased mortality and/or morbidity are unsuitable. Future research should focus on developing an instrument which allows to measure quality of life as the central outcome criterion of clinical studies in palliative medicine.


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