HOW TO USE NLP FOR RISK ADJUSTMENT CODING IN FOUR WAYS

HOW TO USE NLP FOR RISK ADJUSTMENT CODING IN FOUR WAYS

The most robust Natural Language Processing (NLP) enabled Risk Adjustment Solution can effectively detect, capture, and categorize riskadjusted conditions for health practitioners, resulting in enhanced billing accuracy and high care coordination.

Healthcare classification systems are considerably more sophisticated these days. Machine learning and artificial intelligence features can interpret vast amounts of data with more specificity while recognizing frames of reference, generating linkages, and interpreting data extractions. NLP service providers are creating business systems to fit the particular risk adjustment criteria of Medicare Advantage (MA) and the Affordable Care Act (ACA). Employing NLP can help reduce coding expenses per data table while shortening the time between document extraction and concluding data processing.

So, what’s the ideal method to include an NLP-enabled workflow into a risk management solution? There are techniques to progressively get the advantages of NLP without having it in first-pass coding.

Following are four techniques to use Natural Language Processing (NLP) to operate a more focused, effective, and precise Risk Adjustment Solution.

1. There Are No Hierarchical Condition Category (HCC) Charts

In most circumstances, a chart must be coded to ascertain whether or not it has a Hierarchical Condition Category (HCC) given diagnostic code. On the other hand, NLP can alert the user before a coding person spends time on a chart. One can investigate if, after running NLP, the user does not need to customize those charts or whether one should code a subset of the charts to comply with the NLP operation.

1. Categorization of Charts

With actionable data from an NLP scan, one may forgo the traditional comprehensive approach to coding in favor of a more specific one. Charts containing suspected leukemia codes, for example, are directed to coders who excel at leukemia coding. Less-skilled programmers can be assigned to charts with more “basic” principles.

1. Significance of a Chart

Operating NLP prior coding also gives more insight into every chart’s potential financial status, allowing users to determine whether to treat charts separately based on financial aspects. To alleviate the additional regulatory risk associated with high-value charts, numerous passes can be managed to perform. This data may also be utilized to coordinate high- and low-value graphs with filing dates.

1. Coding Precision Assessment

The algorithm runs on the charts before the following manual verification in an NLP-enabled precision assessment. To assess the discrepancies, all such NLP findings may be compared to the first-pass outcomes. Manual coding verification should now be performed entirely on the new numbers revealed by the NLP run and on the coding that the NLP did not display.