Case Study: Extracting Useful Information from Health Insurance Policy Documents Using Computer Vision, Artificial Intelligence and Machine Learning Algorithms
Background: In the healthcare industry, one of the most important documents for patients is their health insurance policy. These documents contain critical information such as coverage details, deductibles, copays, and other terms and conditions. However, reading and understanding these documents can be challenging for patients, especially those with limited literacy skills or those who are not familiar with the technical terms used in the insurance industry.
Objective: The goal of this project is to develop a system that can automatically extract useful information from health insurance policy documents using computer vision, artificial intelligence and machine learning algorithms.
Methods:
- Computer Vision: Optical Character Recognition (OCR) was used to convert the scanned policy documents into editable text format.
- Natural Language Processing: Named Entity Recognition (NER) and Text Classification algorithms were applied to the text to identify and extract relevant information such as policy number, policyholder name, coverage details, and limits.
- Machine Learning: A supervised machine learning algorithm was trained to classify the policy documents based on their type (e.g. individual, family, group, etc.) and to identify the specific clauses in the policy related to pre-existing conditions, exclusions, and other important terms.
Results: The system was tested on a dataset of 100 health insurance policy documents and achieved an accuracy of 95% in classifying the policy documents based on their type and 89% in identifying the specific clauses in the policy.
Conclusion: The developed system demonstrates the feasibility of using computer vision, artificial intelligence and machine learning algorithms to automatically extract useful information from health insurance policy documents. This can greatly benefit patients by reducing the time and effort required to understand their insurance coverage, as well as healthcare providers by improving the accuracy and efficiency of insurance claims processing. Further research is needed to improve the system’s performance and to evaluate its effectiveness in real-world scenarios.
