Effect of Remote Glucose Monitoring Utilizing Computerized Insulin Dose Adjustment Algorithms: A Pilot Project

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Abstract

Introduction: Primary care physicians are often challenged to adjust insulin doses. To facilitate this process, we evaluated in a safety net clinic the work flow and glycemic effects of remote glucose monitoring utilizing Federal Drug Administration (FDA) cleared, Conformité Européenne (CE) registered software that contained computerized algorithms for insulin dose adjustments to help clinicians make dosing decisions for insulin-requiring patients. Methods: Patients taking insulin for at least 6 months with HbA1c levels of at least 8.0% measured glucose levels with a meter attached to their smartphones. Readings were automatically transmitted to a secure, Health Insurance Portability and Accountability Act (HIPAA)-approved server. Values were analyzed every 2–3 weeks and reports, including recommendations for insulin dose changes, were sent to a clinic nurse practitioner (NP) who modified or accepted the recommendations. A staff person contacted patients with the new doses determined by the NP. Results: Insulin regimens included basal alone (N = 11), basal/bolus (N = 14), and self-mixed/split (N = 3). Baseline HbA1 levels of 10.0% fell to 8.1% at 3 months (N = 28) and 7.6% at 6 months (N = 17) without any clinic visits for dose adjustments. There were 268 reports which allowed providers to see 268 other patients during these avoided clinic visits. The NP agreed with 82% of the recommendations. The total doses of insulin increased by 24%. No patient experienced severe hypoglycemia or visited an emergency department for hypoglycemia. Conclusion: Remote glucose monitoring utilizing computerized insulin dose adjustment algorithms saved time for both providers and patients while effectively improving glycemia. Funding: The Leonard M. Lipman Charitable Trust and Mellitus Health.

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Davidson, M. B., & Davidson, S. J. (2019). Effect of Remote Glucose Monitoring Utilizing Computerized Insulin Dose Adjustment Algorithms: A Pilot Project. Diabetes Therapy, 10(2), 523–533. https://doi.org/10.1007/s13300-019-0565-y

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