Mixed integer nonlinear programming model for analyzing patient satisfaction data

Patient satisfaction is one of the most critical indicators of healthcare quality. In the era of patient-centered healthcare, it is considerably desired to investigate and identify the main factors that affect patient satisfaction. Self-reported patient satisfaction survey data plays a vital role since it provides abundant information on the performance of the hospital care delivered to the patients. Given the survey data, it is necessary to find the key reasons that drive patient satisfaction. The findings can be used in the future for corrective actions and quality improvements. In the paper, we propose a mixed integer nonlinear programming model for identifying the features that affect patient satisfaction. Results show that variables related to courtesy and respect from nurses and doctors, communication between doctors and patients, room cleanliness and quietness significantly impact overall satisfaction of patients. The model can be flexibly extended to various healthcare settings. Our approach and findings will help establish guidelines for quality healthcare.