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This study aims to evaluate whether a quality improvement initiative in hyperglycemia management could result in substantiated practice changes and improved outcomes for coronary artery bypass surgery patients. Retrospective chart review and analysis of 103 randomly selected patients hospitalized for coronary artery bypass surgery was used. After the glycemia awareness initiative, the perioperative use of intravenous insulin infusion therapy increased in the total population (P = .01) as well as in the diabetes population (P = .03). Frequency of blood glucose level tests ordered for nondiabetic patients increased from 2.8 per day to 4.3 per day (P = .38). Blood glucose values improved in the diabetic population (Ps = .02, .048). The average length of stay improved in all nondiabetic patients (10.7-8.1 days, P = .07) including those who had coronary artery bypass graft surgery with cardiac catheterization (7.8-6.2 days, P = .09) and coronary artery bypass graft surgery with catheterization with complications (15.0-9.0 days, P = .12). The glycemia awareness initiative resulted in a positive impact on practice patterns. Undiagnosed diabetes and impaired fasting glucose are important and unrecognized issues within this hospital population. It is recommended that healthcare practitioners assume that cardiac patients have an increased likelihood of impaired fasting glucose and hyperglycemia. Advanced practice nurses can improve patient outcomes by ordering glucose testing and glycemic management as a routine practice for all cardiac surgery patients, regardless of diabetes diagnosis. Cardiac staff nurses can recommend glucose testing and screening for prediabetes and diabetes as a routine part of all nursing assessment.

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Healthcare has become a data-intensive business. Over the last 30 years, we have seen significant advancements in the areas of health information technology and health informatics as well as healthcare modeling and artificial intelligence techniques. Health informatics, which is the science of health information,1 has made great progress during this period (American Medical Informatics Association). Likewise, data mining, which has been generally defined as the application of technology and statistical/mathematical methods to uncover relationships and patterns between variables in data sets, has experienced noteworthy improvements in computer technology (e.g., hardware and software) in addition to applications and methodologies (e.g., statistical and biostatistical techniques such as neural networks, regression analysis, and classification/segmentation methods) (Kudyba & Hoptroff, 2001). Though health informatics is a relatively young science, the impact of this area on the health system and health information technology industry has already been seen, evidenced by improvements in healthcare delivery models, information systems, and assessment/diagnostic tools.