1 Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan and University of Central Florida, Orlando, FL, USA.
2 Health Communication Consultants, Inc., Orlando, FL, USA.
3 College of Sciences, National Chengchi University, Taipei, Taiwan.
4 Institute for Information Systems Engineering, Concordia University, Montreal, Canada.
5 College of Engineering and Computer Science, University of Central Florida, Orlando, Florida, USA.
6 Cancer Epidemiology and Prevention Research, University of Calgary, Alberta, Canada.

There are several challenges in diabetes care management including optimizing the currently used therapies, educating patients on selfmanagement, and improving patient lifestyle and systematic healthcare barriers. The purpose of performing a systems approach to implementation science aided by artificial intelligence techniques in diabetes care is two-fold: 1) to explicate the systems approach to formulate predictive analytics that will simultaneously consider multiple input and output variables to generate an ideal decision-making solution for an optimal outcome; and 2) to incorporate contextual and ecological variations in practicing diabetes care coupled with specific health educational interventions as exogenous variables in prediction. A similar taxonomy of modeling approaches proposed by Brennon et al (2006) is formulated to examining the determinants of diabetes care outcomes in program evaluation. The discipline-free methods used in implementation science research, applied to efficiency and quality-of-care analysis are presented. Finally, we illustrate a logically formulated predictive analytics with efficiency and quality criteria included for evaluation of behavioralchange intervention programs, with the time effect included, in diabetes care and research.