Cost-effectiveness of a shared computerized decision support system for diabetes linked to electronic medical records

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Diabetes is becoming a major epidemic not only in the United States but in Ontario, Canada as well. Studies have shown that diabetic complications can be prevented or delayed with proper clinical guidelines and preventative measures. With current limited evidence and no real value on cost-effectiveness, this article examines computerized decision support systems (CDSSs) in respect to patient care and health care costs for diabetes.


Health information was gathered by the Computerization of Medical Practices for the Enhancement of Therapeutic Effectiveness (COMPETE). The research was was based of randomized trials in 47 primary care practices in three regions in Ontario. Patient information was then analyzed and predicted with the patient-level computerized simulation model called ODEM. The ODEM estimates complications, life-expectancy, and costs of complications in people with type 2 diabetes.


The model resulted in a relative risk reduction of 14% and an additional 0.0117 quality-adjusted life year. The cost-effectiveness ratio was $160,845.00 per quality-adjusted year.


This decision support system improved short-term risk factors and moderate improvements in long-term outcomes. Even though there was some improvement in patients, the model will need to refine its processes to improve cost-effectiveness.


The authors did a great job with their methods. I had never heard of a computerized decision support system CDSS that can foresee or predict future life-expectancy and complications. Diabetes was a great disease to explore since children and adults everywhere are being more susceptible. I also like how cost-effective CDS literature was be put in question. It is hard to tell exactly how much money is being saved.


O'Reilly, D., Holbrook, A., Blackhouse, G., Troyan, S., & Goeree, R. (2011). Cost-effectiveness of a shared computerized decision support system for diabetes linked to electronic medical records. Journal of the American Medical Informatics Association, 19(3), 341-345. Retrieved March 1, 2015, from