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Use of a real-time, algorithm-driven, publicly displayed, automated signal to improve insulin prescribing practices

      Highlights

      • Remote monitoring of blood glucose and insulin orders improved glycemic control.
      • An automated, publicly displayed signal improved insulin prescribing practices.
      • Insulin therapy for hyperglycemia in the absence of diabetes increased hypoglycemia.
      • Algorithm-driven treatment recommendations were safe and effective for patients with diabetes.

      Abstract

      Aim

      The clinical andon board (CAB) is a novel electronic surveillance and communication system, which alerts providers to and prompts treatment of dysglycemia. This investigation was designed to determine the CAB’s effectiveness in supporting adherence to standardized evidence-based protocols, as well as improving glycemic control.

      Methods

      This study was a retrospective pre/post analysis of insulin orders and blood glucose values. We used a Student’s t-test for continuous variables and Chi2 for all other variables. This study included patients 18 years or older admitted to the hospital medical service as an inpatient with a length of stay greater than 24 h and less than 90 days. We used Pearson’s correlation coefficient to evaluate the relationship between CAB and blood glucose.

      Results

      The rate of compliance in prescribing basal insulin for patient with diabetes increased from 56% to 77% (p < 0.001). Similarly, compliance rates for prescribing correctional insulin in patients without diabetes increased from 15% to 37% (p < 0.001). Performance on the CAB was linearly related to blood glucose (p = 0.004), and there was a small statistically (not clinically) significant improvement in mean blood glucose values.

      Conclusion

      This approach is effective in alerting and engaging providers to prescribe insulin in a standardized manner with potential to improve glycemic control.

      Keywords

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