177517 Developing Evidence-Driven Strategies Using Data Mining for Improving Patient Satisfaction

Monday, October 27, 2008: 1:30 PM

Yoon-Ho Seol, PhD , Department of Health Informatics, Georgia Health Sciences University, Augusta, GA
Carole Ferrang , Office of Outcomes Management, MCG Health, Inc., Augusta, GA
Genny Carrillo, MD, ScD , Department of Health Informatics, Medical College of Georgia, Augusta, GA
Miguel A. Zuniga, MD, DrPH , Department of Health Policy and Management, Texas A&M Health Science Center, McAllen, TX
The growing recognition of patient satisfaction as a quality indicator necessitates that health care providers focus on issues that will have most impact on patient satisfaction. The main objective of the study was to provide deeper insight into a development of decision rules for prioritizing efforts and resources in improving patient satisfaction.

We analyzed patient satisfaction surveys in five service areas: outpatient, inpatient, ambulatory surgery, emergency, test services. We used decision tree induction and association rule mining to predict overall patient satisfaction. A decision tree for each service was created and it was translated into a set of rules to facilitate developing action plans for improving patient satisfaction. We examined the data at two levels in selecting study variables: 1) overall mean scores of the question categories and 2) the score of each question in the question categories. We transformed the Likert scale variables to corresponding binary variables. To examine what causes a difference in the degree of satisfaction, we tested different splitting criteria for the binary variables. The decision trees were evaluated using cross-validation and independent test datasets. We also discovered co-occurrence patterns of the variables using association rule mining. The results of the study showed that patient satisfaction exhibited distinct characteristics according to the service areas, and the decision trees and co-occurrence patterns provided explicit decision rules for our prioritization process. Effective application of data mining methods would enable us to focus on specific patient needs that could maximize our efforts in enhancing quality of patient care.

Learning Objectives:
1) Understand the issues and implications related to applying data mining techniques in the context of patient satisfaction 2) Recognize the opportunities and challenges for developing strategies to better respond to patientsí values, preferences, and expectations.

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I have strong experience in this topic and I am a primary author of the abstract.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.