Identifying Determinants of Patient Satisfaction for Improving Evidence-Based Decision Making

This study presents development of an Internet-based data mining tool to enhance healthcare providers' understanding of patients' experience with our facility and services. Our previous study examined patient satisfaction across interacting service units indicated by mail survey data to create a view from the continuum of care perspective. Based on previous efforts, we built a web-based tool to support an informed, data-driven decision making approach for the delivery of outstanding health care.

In this study, we focus on five types of care services: outpatient, inpatient, test and treatment, ambulatory surgery, and emergency services. The primary goal of the study is to determine factors that could derive meaningful changes in patient satisfaction for each service type. We select the survey questions associated with the overall assessment section of the study as dependent variables and use other questions as independent variables. The relationships between the variables are tested using different data mining attribute selection algorithms, as well as statistical correlation analysis. The outcomes provide weights for the questions when computing overall satisfaction levels within and across the service types. Our web-based application also provides a dynamic simulated profile for patient satisfaction utilizing the users' input, which would help an organization conduct comparative, predictive analysis for enhanced quality management.

As a means to assess quality and performance of patient care, analyzing determinants related to levels of patient satisfaction provides valuable insight toward evidence-based decision making, thereby enabling improved care services that are responsive to the patients' needs.