Detecting early signs of clinical risk and disease gives us a greater chance of a successful outcome. Many clinical risk models that predict patient outcomes rely on a mixture of structured and unstructured data. However, timely use of clinical risk models to identify high-risk patients requires real-time mining of the mountains of unstructured data flowing into EHRs every day.
How can clinical risk models be applied in real time to identify at-risk individuals?
Linguamatics Health, powered by a combination of I2E and AMP, provides a real-time NLP processing engine to highlight at-risk individuals. This combination allows clinical risk models to be applied for immediate patient benefit.
- Identify early signs of lung cancer by mining radiology reports for mentions of pulmonary nodules, so these at-risk patients can be identified for follow-up
- Assess social determinant data and ambulatory status to understand 30-day readmission risk
- Integrate into existing enterprise infrastructure using RESTful web services
Read more about I2E's Extract Transform Load (ETL) solutions here.
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