Implementing a Prediabetes Screening Algorithm to Improve Identification and Referrals in Primary Care

Almost half (49%) of the United States population has prediabetes or type 2 diabetes. Type 2 diabetes is associated with numerous comorbidities, contributes substantially to morbidity and mortality, and is recognized as the seventh leading cause of death in the country. In addition, it remains the costliest chronic disease in the United States due to long-term complications and the ongoing need for medical care. Early detection of prediabetes creates opportunities for timely intervention, which can effectively prevent or delay the progression to type 2 diabetes. The objective of this quality improvement project was to develop and implement a screening algorithm in the primary care setting using the Prediabetes Risk Test and point-of-care Hemoglobin A1c testing to improve the identification of patients with prediabetes and increase referrals to lifestyle intervention. Over the 12-week implementation period, fifteen patients were identified as having prediabetes, three agreed to a referral to lifestyle intervention, and one was started on metformin. This was a marked increase compared to the two prior recent years. The algorithm was feasible and effective at improving the identification of prediabetes, in addition to improving staff, and providing knowledge and retention. Future studies should include a broader patient population in a variety of locations with longitudinal follow-up. Updating the Prediabetes Risk Test to specify physical activity for future studies may also be beneficial.