Enhancing the Management of Deployment-Limiting Medical Conditions in the U.S. Department of Navy Leveraging Electronic Health Records and Predictive Analytics Methods
Our research highlighted potential benefits of using predictive analytics in a medical assessment to identify SMs with DLMCs and to predict RTD outcomes once placed on LIMDU. This capability is being deployed for real-time clinical decision support to enhance health care provider's deployability assessment capability, improve accuracy of the DLMC population, and enhance combat readiness of the U.S Military.
Authors
- Michael McShea (ORCID: https://orcid.org/0000-0001-5110-1758)
- Çağlar Çağlayan (ORCID: https://orcid.org/0000-0002-9991-8457)
- Raghav Ramachandran (ORCID: https://orcid.org/0000-0002-8845-8950)
- Mark J. Panaggio (ORCID: https://orcid.org/0000-0002-1790-5899)
- John de Geus
- Winnie Wang
- Denise Boggs-Wilkerson (ORCID: https://orcid.org/0009-0007-3153-8419)
- Michael Mercado
Institutions
- Johns Hopkins University Applied Physics Laboratory (US)
- United States Department of the Navy (US)
- Bureau of Medicine and Surgery (US)
Publication Details
- Journal
- Military Medicine
- Published
- 2024-08-19
- DOI
- https://doi.org/10.1093/milmed/usae141
- Citations
- 1
- Primary Topic
- Occupational Health and Safety Research
- Type
- article
- Field-Weighted Citation Impact
- 0.45
Funders
- U.S. Department of Defense