Case Study: Healthcare
Faster Claim Adjudication results in better customer satisfaction
Automating the adjudication process by classifying each item description into a charge type accurately in order to apply business rules of adjudication.
Abzooba used Natural Language Processing (NLP) and advanced Machine Learning algorithm to understand the context of the item description and prepared a continuous learning system to classify each item into a charge type (e.g. laboratory changes, monitoring charges etc.). Each item was classified with a confidence score and re-evaluated from domain experts. All discrepancies and low confidence items were fed back into the application to do the incremental learning.
The workflow consisted of a classification engine which does the Pre-processing i.e. standardize the item description (spell correction and abbreviation expansion), Concept identification i.e. domain similarity of the item with different charge type and Advanced machine learning to classify item into one of charge class with a confidence score
Classified item was passed through a decision node that sent it basis threshold, either to business rule engine or for a review from domain expert. Rule engine with the help of this classification and contract guidelines decided adjudication.
- 40% increase in assessment speed for Health Insurance Claim adjudication.
- More than 90% accuracy with minimal supervision.
- Increased assessment speeds, higher through-put and reduced processing costs.