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Research Article

Predictive Environmental Exposure Modeling in High-Risk Healthcare Environments

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Abstract

Predictive Environmental Exposure Modeling (PEEM) is an important development in the protection of high-risk medical settings, including intensive care units, OR rooms, and wards with infectious diseases. This paper discusses how predictive analytics, environmental surveillance, and machine learning could be placed to predict potential exposure routes of airborne and surface-borne contaminants. PEEM will allow identifying and preventing the risk in advance by using real-time sensor measurements of air quality, humidity, temperature, and microbial presence, as well as patient movement and staff activity trends. The modeling framework uses the spatial-temporal analysis to predict the spread of contamination enabling healthcare facilities to streamline strategies used in ventilation, sterilization, and the organization of work. Findings indicate that predictive modeling has the potential to decrease infection rates, increase adherence to environmental health measures, and the general response of healthcare systems to emerging pathogens. This study highlights the radical capabilities of information-based environmental modeling in enhancing patient safety and efficiency in risky clinical matters.

Keywords:

Environmental Exposure Environmental Monitoring Healthcare-Associated Infections (HAIs) High-Risk Healthcare Environments Machine Learning Predictive Modeling Risk Assessment

Article information

Journal

Scientific Journal of Engineering, and Technology

Volume (Issue)

2(2), (2025)

Pages

158-164

Published

02-12-2025

How to Cite

Holt, K. L. (2025). Predictive Environmental Exposure Modeling in High-Risk Healthcare Environments. Scientific Journal of Engineering, and Technology, 2(2), 158-164. https://doi.org/10.69739/sjet.v2i2.1211

References

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