Potentials of Clinical Pathway Analysis Using Process Mining on the Indonesia National Health Insurance Data Samples: an Exploratory Data Analysis

Abstract

Clinical pathway analysis is an important analysis in the healthcare domain. This approach learns from historical data of patient pathways of clinical treatments and finds patterns to be used for further purposes, including treatment recommendations and precision medicine. Indonesia has the opportunity for clinical pathway analysis by using the Indonesia national health insurance data samples provided by the Social Security Administrator-Healthcare. The data samples are representative of the Indonesian population and potentially useful for initial explorations of clinical pathways in Indonesia. This study applied an exploratory data analysis using process mining for clinical pathway analysis. Process mining is a promising approach to learn from time-stamped datasets to find sequenced clinical pathway patterns. We examine the data samples carefully to define the minimum components of process mining for clinical pathway analysis and provide samples of the results of the clinical pathway analysis. Contributions of this study are two folds: to promote process mining for clinical pathway analysis and to present a case study of clinical pathway analysis using the Indonesia National Health Insurance data samples. The contributions of this paper are to promote clinical pathway analysis to improving health services using real data from BPJS Kesehatan system, and to propose a method for clinical pathway analysis based on process mining. The results of this study are disease trajectory visualization through a process model and statistics evaluating the performance of the results using process mining techniques.