Evaluating a System for Detecting Epidemic Risks in Hospitals
Author Information
Author(s): Gerbier Solweig, Yarovaya Olga, Gicquel Quentin, Millet Anne-Laure, Smaldore Véronique, Pagliaroli Véronique, Darmoni Stefan, Metzger Marie-Hélène
Primary Institution: Hospices Civils de Lyon
Hypothesis
Can a natural language processing system effectively extract and encode medical concepts from emergency department records for syndromic surveillance?
Conclusion
The study shows that an automated method for extracting and encoding medical concepts from emergency department reports is feasible and useful for identifying potentially infectious patients.
Supporting Evidence
- The system achieved an overall recall of 85.8% for processing medical concepts.
- Precision was measured at 79.1%, indicating a good level of accuracy in identifying relevant concepts.
- The study evaluated 1,674 medical concepts from 100 emergency department records.
Takeaway
This study created a computer program that helps hospitals find patients who might spread infections by reading their medical notes.
Methodology
The study used a natural language processing application called UrgIndex to analyze narrative reports from emergency department medical records.
Potential Biases
Potential for false positives due to misinterpretation of medical terms and abbreviations.
Limitations
The system may struggle with unrecognized terms and does not contextualize medical concepts based on their occurrence timeline.
Participant Demographics
Adult patients admitted to the emergency department of a university hospital.
Statistical Information
P-Value
0.0001
Confidence Interval
95% CI: 84.1-87.3
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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