Specifications and description
EMS advance analytic code technology or machines learning model enhances data mining techniques and statistical data modeling by evaluating large historical datasets and data elements to predict present and future outcomes.
To achieve these outcomes or results in the healthcare industry, data mining is performed specifically by analyzing historic diagnosis through scanning reports, indications, diagnosis codes, demographic data, and exam CPT codes in real time.
Conclusively the resulting real-time cognitive algorithmic code renders:
- Better workflow management by predictively assigning the physician best suited by training, experience, and interdisciplinary expertise.
- Outstanding comparative reporting by machine learning of unstructured data elements as text to discern best predictive findings and diagnosis by patient’s conditions and previous case outcomes.
- Best predictive reporting by mining and generating formatted structured reports with normalized medical terminology
- Recursive reports by defining data patterns, rules, and definitions that forecast outcomes based on data trends, behavior, and activity
- Opportunities to educate medical peers and staff to review edge case outcomes or predictive outcomes that explicitly vary from the predicted many successful outcomes
- DICOM image comparison as a layered structured data set by stateful inspection of the current DICOM image study pixel by pixel to determine change or discrepancy from the previous DICOM image study and displaying hotspots visually for the physician. (Example:, a current X-ray compared to an X-ray from 3 months prior, and compared to an X-ray from 6 months prior). Hotspots represent finding(s) or change(s) and concurrently using features 1-4 to predict the best physician or physicians in ranking order that will predictively make the best diagnosis and assign the study
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