AIMC Topic: Terminal Care

Clear Filters Showing 1 to 10 of 19 articles

Post-biographical dignity in the age of artificial intelligence: Narrative, ePROMs and ethical challenges in end-of-life care.

Palliative & supportive care
OBJECTIVES: The growing integration of artificial intelligence (AI) and patient-reported digital tools (ePROMs and ePREMs) in palliative care offers new opportunities for personalised care yet also raises profound ethical and philosophical concerns. ...

Predicting mortality dynamics in cancer patients: A machine learning approach to pre-death events.

PloS one
Capturing the dynamic changes in patients' internal states as they approach death due to fatal diseases remains a major challenge in understanding individual pathologies and improving end-of-life care. However, existing methods primarily focus on spe...

Artificial intelligence for better goals of care documentation.

BMJ supportive & palliative care
OBJECTIVES: Lower rates of goals of care (GOC) conversations have been observed in non-white hospitalised patients, which may contribute to racial disparities in end-of-life care. We aimed to assess how a targeted initiative to increase GOC documenta...

Machine Learning for Targeted Advance Care Planning in Cancer Patients: A Quality Improvement Study.

Journal of pain and symptom management
CONTEXT: Prognostication challenges contribute to delays in advance care planning (ACP) for patients with cancer near the end of life (EOL).

Integrating machine-generated mortality estimates and behavioral nudges to promote serious illness conversations for cancer patients: Design and methods for a stepped-wedge cluster randomized controlled trial.

Contemporary clinical trials
INTRODUCTION: Patients with cancer often receive care that is not aligned with their personal values and goals. Serious illness conversations (SICs) between clinicians and patients can help increase a patient's understanding of their prognosis, goals...

Documentation of Palliative and End-of-Life Care Process Measures Among Young Adults Who Died of Cancer: A Natural Language Processing Approach.

Journal of adolescent and young adult oncology
Few studies have investigated palliative and end-of-life care processes among young adults (YAs), aged 18-34 years, who died of cancer. This retrospective study used a natural language processing algorithm to identify documentation and timing of four...

Development and Validation of a Deep Learning Algorithm for Mortality Prediction in Selecting Patients With Dementia for Earlier Palliative Care Interventions.

JAMA network open
IMPORTANCE: Early palliative care interventions drive high-value care but currently are underused. Health care professionals face challenges in identifying patients who may benefit from palliative care.

Robotic technology for palliative and supportive care: Strengths, weaknesses, opportunities and threats.

Palliative medicine
BACKGROUND: Medical robots are increasingly used for a variety of applications in healthcare. Robots have mainly been used to support surgical procedures, and for a variety of assistive uses in dementia and elderly care. To date, there has been limit...