Developing a clinical decision support framework for integrating predictive models into routine nursing practices in home health care for patients with heart failure
This study provides a detailed plan for a CDS tool that uses the latest AI technology designed to aid nurses in their day-to-day HHC service. Our proposed CDS tool includes an alert system that serves as a guard rail to prevent ED visits and hospitalizations. This tool can potentially improve how nurses make decisions and improve patient outcomes by providing early warnings about ED visits and hospitalizations.
Authors
- Jiyoun Song (ORCID: https://orcid.org/0000-0003-0362-0670)
- Lauren Evans (ORCID: https://orcid.org/0000-0001-7146-3051)
- Kathryn H. Bowles (ORCID: https://orcid.org/0000-0001-7740-5725)
- Maxim Topaz (ORCID: https://orcid.org/0000-0002-2358-9837)
- Yolanda Barrón (ORCID: https://orcid.org/0000-0002-7064-937X)
- Se Hee Min (ORCID: https://orcid.org/0000-0003-2722-6627)
- Anahita Davoudi (ORCID: https://orcid.org/0000-0003-4345-3889)
- Danielle Scharp (ORCID: https://orcid.org/0000-0002-3265-6667)
- Zidu Xu (ORCID: https://orcid.org/0000-0002-6122-8426)
- Sena Chae (ORCID: https://orcid.org/0000-0002-9136-6176)
- Margaret V. McDonald (ORCID: https://orcid.org/0000-0003-1045-0956)
- Sungho Oh (ORCID: https://orcid.org/0000-0002-1221-2305)
Institutions
- University of Iowa (US)
- Columbia University (US)
- University of Pennsylvania (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Journal of Nursing Scholarship
- Published
- 2024-11-07
- DOI
- https://doi.org/10.1111/jnu.13030
- Citations
- 4
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- Agency for Healthcare Research and Quality