Evidence-Based Policy Decision Support System to Enhance In-Hospital Patient Experience in the State of Qatar

A patient reflecting on their experience can provide relevant information as to how the patient has been treated and cared for during their stay in a healthcare facility. By analyzing several patients’ experiences, hospital administrators can both evaluate patient satisfaction as well as staff members’ strategic implementations to improve healthcare delivery. This thesis investigates the importance of developing an evidence-based decision support system to strengthen the patient hospital experience in the State of Qatar. Additionally, this thesis explores the role of machine learning tools; specifically, unsupervised exploratory data analysis, to further understand correlations between patient demographics (e.g., age and gender) and the results of the patient experience survey. To do so, features extracted from patient experience surveys were clustered by the most relevant features to assist in properly analyzing a reflection of patient experiences—and specifically, patient satisfaction—in Qatari Public Healthcare facilities. This thesis aims to create a target-based recommendation system that uses inputs from satisfaction scores were generated from the patient experience questionnaire. This method is applied to an aggregated patient experience survey data set covering the period from the April 2017 to December 2019. The applied methods led to the discovery of an association between patient demographic clusters, and patient satisfaction scores. To verify the significance of the associations, a conventional statistical test was applied to measure the validity of the generated recommendation. This test is then presented and visualized in a business intelligence tool.