A Prescription Trend Analysis using Medical Insurance Claim Big Data

Understanding the spread of diseases and the use of medicines is of practical importance for various organizations, such as medical providers, medical payers, and national governments. This study aims to detect the change in the prescription trends and to identify its cause through an analysis of Medical Insurance Claims (MICs), which comprise the specifications of medical fees charged to health insurers. Our approach is two-fold. (1) We propose a latent variable model that simulates the medication behavior of physicians to accurately reproduce monthly prescription time series from the MIC data, where prescription links between the diseases and medicines are missing. (2) We apply a state space model with intervention variables to decompose the monthly prescription time series into different components including seasonality and structural changes. Using a large dataset consisting of 3.5-year MIC records, we conduct experiments to evaluate our approach in terms of accuracy, usefulness, and efficiency. We also demonstrate three applications for our medical analysis.