In recent years, a lot of computer-assisted coding products have hit the health care market as organizations gear up for the ICD-10 coding system and implement population health management and data analytics programs to succeed in evolving care delivery and reimbursement methods.
Figuring out how to choose a CAC is the focus of an educational session at HIMSS13 in New Orleans. For instance, all CACs use natural language processing engines, which also may be called natural language understanding engines, to recognize and parse a word in clinical documentation, assign the word as a data element, categorize it (such as diabetes), and determine if the element is related to a diagnosis or procedure. Then the engine maps the data element to the correct terminology, such as ICD-10, SNOMED CT or LOINC. But how does an organization determine how well one vendor’s NLP engine works to do all this, compared with other engines?
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