Predicting Agitation Events in the Emergency Department Through Artificial Intelligence
Using a cross-sectional cohort of ED visits across 9 hospitals, the prediction model included factors for detecting risk of agitation that demonstrated high accuracy and applicability across diverse patient populations. These results suggest that clinical application of the model may enhance patient-centered care through preemptive deescalation and prevention of agitation.
Authors
- Ambrose H. Wong (ORCID: https://orcid.org/0000-0001-7471-1647)
- Leah Robinson (ORCID: https://orcid.org/0000-0002-5804-6947)
- Steven L. Bernstein (ORCID: https://orcid.org/0000-0002-9964-7036)
- Isaac V. Faustino (ORCID: https://orcid.org/0000-0001-7445-4265)
- Anusha Kumar
- Dhruvil Shah (ORCID: https://orcid.org/0009-0003-4120-451X)
- Guangyu Tong (ORCID: https://orcid.org/0000-0002-7697-5029)
- Can Meng (ORCID: https://orcid.org/0000-0002-8257-6243)
- James Dziura (ORCID: https://orcid.org/0000-0001-8953-6183)
- Bidisha Nath (ORCID: https://orcid.org/0000-0002-8385-3417)
- Edward R. Melnick (ORCID: https://orcid.org/0000-0002-6509-9537)
- Yue Hu (ORCID: https://orcid.org/0000-0002-3846-9096)
- Kaicheng Wang (ORCID: https://orcid.org/0000-0003-2519-2670)
- R K Desai (ORCID: https://orcid.org/0009-0000-3325-7783)
- Robert A. Taylor
- A. V. Sapre
- Kimberly A. Yonkers (ORCID: https://orcid.org/0000-0001-7326-7636)
Institutions
- Dartmouth College (US)
- University of Massachusetts Chan Medical School (US)
- Yale University (US)
Publication Details
- Journal
- JAMA Network Open
- Published
- 2025-05-07
- DOI
- https://doi.org/10.1001/jamanetworkopen.2025.8927
- Citations
- 6
- Primary Topic
- Healthcare Decision-Making and Restraints
- Type
- article
- Field-Weighted Citation Impact
- 13.84
Funders
- Patient-Centered Outcomes Research Institute
- National Institute of Mental Health
- National Institute of Nursing Research
- National Institute on Minority Health and Health Disparities