Machine learning algorithms have become a popular choice for optimizing processes in various industries, including banking, insurance, and other sectors. This case study focuses on the development of an IT solution specifically tailored for health insurance, utilizing machine learning algorithms to automate patient management and budget control.
The primary goal was to develop an automated solution to streamline patient interactions and financial management for insurance companies.
Leveraging artificial intelligence algorithms, the system accurately predicts medical episodes by analyzing historical data, including past services, socio-demographic information, and previous diagnoses. Additionally, the system identifies uninsured cases, offers post-discharge support strategies, recommends procedures within the insurance coverage list, and forecasts the patient's well-being at 30, 60, and 90 days. This combined approach enhances both the quality and cost-effectiveness of patient care.
We successfully developed a platform to house these models from scratch, trained the system using three years of patient referral data, integrated with multiple medical systems, and continue to refine and train machine learning models for ongoing improvement.
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