Enhancing Knowledge Management in Healthcare: An Embedding Fusion Approach to Business Rule Representation
Keywords:Natural Language Processing, Large Language Models, Knowledge Management, Decision Management Systems, Knowledge-based Systems, Rule-Based Systems, Common Dental Terminology, Affinity Analysis, Enterprise Decision Management Systems
Enterprise Decision Management systems are vital for delivering efficient healthcare services. However, the ever-changing clinical terminology creates complex business rules, making healthcare IT systems difficult to maintain. In this study, we present an embedding fusion technique using unsupervised Natural Language Processing (NLP) to represent business rules as semantic vectors by incorporating multiple text data sources for each rule. We apply this method to a dental insurance administration case study and find that our approach is over 200 times more likely to identify redundant rule pairs compared to random pairs. This case study suggests that an embedding-based technique can significantly improve knowledge management efficiency in healthcare IT systems.
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Copyright (c) 2023 Brady Berg, Mukundan Agaram, Shikha Mohindra, Abhinav Nadh Thirupathi
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.