Proactive Care Management of AI-Identified At-Risk Patients Decreases Preventable Admissions
A care management intervention targeting AI-identified at-risk patients was followed by a onetime, significant, sizable reduction in preventable HA rates. Further exploration is needed to assess the potential of integrating AI and care management in preventing acute hospital encounters.
Authors
- Chad Villaflores (ORCID: https://orcid.org/0000-0003-1367-0882)
- Catherine A. Sarkisian (ORCID: https://orcid.org/0000-0003-2768-2323)
- Samuel A. Skootsky (ORCID: https://orcid.org/0000-0002-9995-436X)
- Hanina L. Rosenstein
- Sarah D. Meshkat
- Ann C. Raldow (ORCID: https://orcid.org/0000-0001-8996-6810)
- Sitaram S. Vangala
- Naveen Raja
- Elizabeth A Jaureguy
Institutions
- University of California, Los Angeles (US)
Publication Details
- Journal
- The American Journal of Managed Care
- Published
- 2024-11-01
- DOI
- https://doi.org/10.37765/ajmc.2024.89625
- Citations
- 7
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.84