首页|Reports on Machine Learning from Stanford University Provide New Insights (Lessons Learned From a Multi-site, Team-based Serious Illness Care Program Implementation At an Academic Medical Center)
Reports on Machine Learning from Stanford University Provide New Insights (Lessons Learned From a Multi-site, Team-based Serious Illness Care Program Implementation At an Academic Medical Center)
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Mary Ann Liebert, Inc
Investigators publish new report on Machine Learning. According to news reporting originating in Stanford, California, by NewsRx journalists, research stated, “Patients with serious illness benefit from conversations to share prognosis and explore goals and values. To address this, we implemented Ariadne Labs’ Serious Illness Care Program (SICP) at Stanford Health Care.” Financial support for this research came from Stanford Department of Medicine. The news reporters obtained a quote from the research from Stanford University, “Improve quantity, timing, and quality of serious illness conversations. Initial implementation followed Ariadne Labs’ SICP framework. We later incorporated a team-based approach that included nonphysician care team members. Outcomes included number of patients with documented conversations according to clinician role and practice location. Machine learning algorithms were used in some settings to identify eligible patients. Ambulatory oncology and hospital medicine were our largest implementation sites, engaging 4707 and 642 unique patients in conversations, respectively. Clinicians across eight disciplines engaged in these conversations. Identified barriers that included leadership engagement, complex workflows, and patient identification.” According to the news reporters, the research concluded: “Several factors contributed to successful SICP implementation across clinical sites: innovative clinical workflows, machine learning based predictive algorithms, and nonphysician care team member engagement.”
StanfordCaliforniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningStanford University
NETL
NSTL
Mary Ann Liebert, Inc
