Explainable data-driven modeling of patient satisfaction survey data

In the personalized patient-centered healthcare, self-reported patient satisfaction survey data plays an important role. Given the patient survey data, it is necessary to identify the drivers of patient satisfaction and explain them so that such patterns can be used in future as well as necessary corrective actions can be taken. In healthcare, both accuracy and interpretability are important criteria for choosing a reliable predictive model for analyzing patient data. Usually, complex models such as Random Forest, neural networks can achieve high prediction accuracy but lack necessary interpretation to their prediction results. In this paper, we address this problem by proposing a local explanation method to interpret complex model prediction results. First, we build a predictive model using Random Forest to fit the patient satisfaction data. Second, we utilize local explanation method to provide insights into the Random Forest prediction results so as to discover true reasons behind patient experiences and overall ratings. Specifically, our approach allows us to interpret patient's overall rating of a hospital at the individual level, and find out the set of the most influential factors for each patient. We focus on all unhappy patients to investigate the top reasons for patient dissatisfaction. Our approach and findings will help to establish guidelines for a quality healthcare.