Implementing A Prediabetes Screening Algorithm To Improve Identification And Referrals In Primary Care

<p>Almost half (49%) of the United States population has prediabetes or type 2 diabetes. Type 2 diabetes has many associated comorbidities and is the seventh leading cause of death in the United States. It is also the most expensive chronic condition in the nation. Identifying patients with prediabetes allows for early intervention to prevent or delay the onset of 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 HemoglobinA1c testing to improve 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 two prior recent years. The algorithm was feasible and effective at improving identification of prediabetes, in addition to improving staff and provider 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.</p>

Name thesis:degree_name

Doctor of Nursing Practice (DNP)

Level thesis:degree_level

Open Access Thesis

Discipline thesis:degree_discipline

Yale University School of Nursing

Year

2023

Author dc:creator

• Masoud, Katherine

Contributors dc:contributor

• Neesha Ramchandani

• Point of care, Prediabetes, Risk test, Screening algorithm

Repository record dc:identifier

https://elischolar.library.yale.edu/ysndt/1157

OAI identifier oai:identifier

oai:elischolar.library.yale.edu:ysndt-1156