Detected and reported likelihood of VTE cases, correctly identifying 98%* of patients. *Excellent NLP industry performance
Lowered reviewer workload from 2.0 FTE to 0.4 FTE. *
Saved $120,000 in staff time assuming the use of in-house RN coders. *
*National average salary of $75,000
NLP for Risk Detection
Melax Technologies’ AI and machine learning simultaneously improved cost, quality measures, and patient health outcomes through highly accurate venous thromboembolism (VTE) detection.
The results our client achieved are iterative across a number of similar readmission risks such as fall risk, infection, Polypharmaceutical interactions, and more. Using NLP to review discharge notes for potential irregularities can improve your results, your patients’ health, and your bottom line.
Extracted medical concepts in clinical notes and mapped them to the best matched ICD-10 code.
Reduced medical coders’ workload* by one-third (1/3).
*Facility with 20,000 in-patient discharges and 40,000 emergency department visits would need the equivalent of 5.3 coders.
Saved $74,200 in staff time of in-house coders. *
*National median salary of $42,000. Savings will vary based on training level of coders (RN vs. non)
NLP for Coding Accuracy
The Melax CLAMP toolkit was applied to Computer-Assisted Coding (CAC) at a major academic cancer center to facilitate transition from ICD-9 to ICD-10 coding, improving accuracy and reducing workload.
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