Impact of Electronic Health Record Clinical Decision Support on Diabetes Care: A Randomized Trial

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This is a review of the study entitled Impact of Electronic Health Record Clinical Decision Support on Diabetes Care: A Randomized Trial[1]


This study was performed to assess the impact of a clinical decision support tool called the Diabetes Wizard on the control of hemoglobin A1c, blood pressure, and low density lipoprotein (LDL) cholesterol levels of diabetic patients. The study was performed at HealthPartners Medical Group (HPMG) in Minnesota in 2007.


Statistics gathered in 2008 indicate that less than 20% of patients with diabetes maintain hemoglobin A1c levels, blood pressure, and LDL values within recommended guidelines. The high number of patients without optimal control of these parameters is attributed to a lack of timely increase in medications due to short physician visits and also patient noncompliance. The authors of this study suggested that a clinical decision support tool could help improve patients’ diabetic markers. The Diabetes Wizard used the patient’s past and current clinical information to make detailed clinical recommendations. In contrast to previous studies of CDS tools that presented alerts during or at the end of the patient’s visit, the Diabetes Wizard was presented to the physician at the beginning of the patient’s visit.


Physicians participated on a volunteer basis. Diabetic patients, as determined by a search of the EHR for certain prescribed medications and laboratory values, were selected randomly to be in the study arm and the control arm. When a patient in the study presented to the clinic, the nurse clicked the Diabetes Wizard icon within the patient’s electronic health record. The wizard gathered clinical information about the patient and printed a sheet with pertinent data and personalized clinical recommendations using evidence-based guidelines published by the Institute for Clinical Systems Improvement. This sheet was given to the physician just before entering the patient’s exam room. After the patient visit, the physician completed a short visit-resolution form indicating actions taken. Recommendations included specific changes in medication regimen or treatment plan for patients with renal insufficiency or congestive heart failure. Blood tests may have been recommended as well. Finally, shorter followup intervals were recommended based on previous clinical trials that showed more frequent followup visits are associated with better outcomes. To incentivize the staff, the nurses shared a bonus payment and the physicians were compensated based on the percentage of completed visit resolution forms.

Diabetes Wizard

Diabetes Wizard was built in to the electronic medical record system to help guide clinician in their decision making process regarding treatments that might be best for the patients. It would allow clinicians to both provide standardize and personalize care to patients using a single tool and to provide better results glucose and blood pressure control.

Recommendation for Diabetes Wizard

  • Suggests specific changes in medications for patients not as individualized hemoglobin, blood pressure or lipid goals.
  • Suggests changes in treatment for patients with contraindications to existing treatment.
  • Suggests obtaining overdue laboratory tests
  • Suggests short follow-up intervals


Outcomes of the study were based on a comparison of preintervention parameters (hemoglobin A1c, blood pressure, and LDL) compared to postintervention parameters. The data support the hypothesis that an EHR-based clinical decision support system can improve compliance with evidence-based guidelines. The cohort in the study arm showed a modest but significant improvement in glucose control and some aspects of blood pressure control. The monetary incentives ended after 6 months, but physicians were free to continue to use the Diabetes Wizard for an additional 12 months. Some clinicians continued to use it but at a lesser rate than during the compensated period.


This study of a CDS took a slightly different approach than other studies in that nurses were a key part of the workflow and the tool was presented to the clinician before seeing the patient. In addition, the clinicians were offered monetary incentives to complete the visit resolution forms. The improved outcomes in the study arm were only slightly better than those in the control arm. This finding suggests that the clinicians learned and applied the recommendations across all patients, not just those in the study arm.


When combined with a modified workflow and sufficient incentives, clinical decision support tools can play a role in improving adherence to guidelines for hemoglobin A1c, LDL cholesterol, and blood pressure control in patients with diabetes. Other studies have looked at using clinical decision support tools to aid in patient specific glucose lowering therapy in Type 2 Diabetic patients.[2]


The authors of this study continued to track the number of completed visit resolution forms for an additional 12 months after the incentive period. The use of this tool dropped by about half when incentives were discontinued. The study did not evaluate the patient’s clinical parameters at the end of those 12 months to see if the improved values were sustained or if the prescribing patterns “learned” during the 6-month course of the study were maintained. Since there was little difference in the improvement between the study arm and the control arm, yet an overall improvement across both cohorts, one might conclude that the CDS served as a learning tool that benefited all patients.

For a similar study, see Outpatient Electronic Health Records and the Clinical Care and Outcomes of Patients With Diabetes Mellitus


  1. O’Connor, P. J., Sperl-Hillen, J. M., Rush, W. A., Johnson, P. E., Amundson, G. H., Asche, S. E., Ekstrom, H. L., Gilmer, T. P. (2011). Impact of electronic health record clinical decision support on diabetes care: A randomized trial. Annals of Family Medicine, 9(1), 12–21. doi:10.1370/afm.1196 Retrieved from
  2. A Decision Support Tool for Appropriate Glucose-Lowering Therapy in Patients with Type 2 Diabetes.