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Improving End-of-Life Care through AI-Based Clinical Decision Support.
Jessica Londeree Saleska
, Nathan Moore
,
Kyle Pitzer
, Randi Foraker
,
Patrick White
Division of Palliative Medicine
Institute of Clinical and Translational Sciences (ICTS)
Siteman Cancer Center
Division of General Medicine & Geriatrics
Department of Medicine
Research output
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peer-review
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Keyphrases
Clinical Decision Support
100%
End-of-life Care
100%
Goals of Care Conversations
100%
AI-based Methods
100%
Palliative Care
42%
Mortality Prediction
28%
Mortality Risk
28%
Prolonged Life
14%
Health Systems
14%
Patient Satisfaction
14%
Staff Members
14%
Risk Estimation
14%
Clinical Workflow
14%
Clinical Training
14%
Patient Eligibility
14%
Healthcare Data
14%
High Response Rate
14%
Palliative Care Consult
14%
Serious Illness
14%
Observed-to-expected Ratio
14%
Warning Signs
14%
Clinician Training
14%
Person-to-person
14%
Expected Mortality
14%
Adult Hospital
14%
End-of-life Experiences
14%
Vizient
14%
Medicine and Dentistry
Clinician
100%
End of Life Care
100%
Artificial Intelligence
100%
Symptomatic Treatment
80%
Health Care Cost
20%
Patient Satisfaction
20%
Personal Experience
20%
Risk Evaluation
20%
Diseases
20%
Nursing and Health Professions
Palliative Therapy
100%
Artificial Intelligence
100%
Terminal Care
100%
Healthcare System
25%
Patient Satisfaction
25%
Diseases
25%
Personal Experience
25%
Life Experience
25%