Multidisciplinary clinician perceptions on utility of a machine learning tool (<scp>ALERT</scp>) to predict 6‐month mortality and improve end‐of‐life outcomes for advanced cancer patients
Clinicians expressed widespread acceptability of ALERT and identified clear benefits, particularly in triggering earlier SIC and standardizing prognosis discussions across care teams to avoid aggressive hospital interventions at EOL. Challenges identified, including concerns regarding communication of the prediction and integration with clinical expertise and new research, will guide refinement of the ALERT model.
Authors
- Melissa Mazor (ORCID: https://orcid.org/0000-0003-0966-4578)
- Cardinale B. Smith (ORCID: https://orcid.org/0000-0003-3635-8701)
- Marcos Vargas (ORCID: https://orcid.org/0000-0003-0173-2206)
- Marsha Augustin
- Melanie Besculides (ORCID: https://orcid.org/0000-0003-3250-0993)
- Ksenia Gorbenko (ORCID: https://orcid.org/0000-0003-1010-2257)
- Nithya Krishnamurthy (ORCID: https://orcid.org/0009-0009-9091-1927)
- José Morillo (ORCID: https://orcid.org/0000-0002-8978-3966)
Institutions
- SUNY Downstate Health Sciences University (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Cancer Medicine
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1002/cam4.70137
- Citations
- 2
- Primary Topic
- Palliative Care and End-of-Life Issues
- Type
- article
- Field-Weighted Citation Impact
- 3.76
Funders
- National Institutes of Health
- National Cancer Institute