Effect of Using Personalized Estimates of Diabetes Risk During Primary Care Visits for People With Prediabetes.

Abstract

Background: To assess acceptability, feasibility, and effectiveness of incorporating individualized risk prediction into clinical assessment, decision making, and communication of risk of type 2 diabetes, with and without preventive interventions, in patients with prediabetes.

Methods: We integrated a prediction model into the clinical workflow at a U.S. health care organization. We conducted patient and provider focus groups and pre- and post-dissemination surveys among 2500 patients with prediabetes who had primary care visits between May 2018 and December 2019. We compared rates of progression to type 2 diabetes at 3 years between the intervention group and a propensity score-matched cohort of patients who received usual care.

Results: Prior to implementing the predictive model, 41.6% of providers and 63.8% of patients felt confident or very confident in their ability to estimate the risk of progression to diabetes for individual patients. After personalized risk information was made available, this increased to 92.8% for providers and 66.9% for patients. People with prediabetes who had a primary care visit where their care team had access to personal risk of developing type 2 diabetes assessed by the EHR-based prediction model were significantly less likely to progress to diabetes within 3 years, compared to a propensity-score-matched cohort who received usual care in the same health system without individualized risk estimates (19.5% vs. 27.6%, p = 0.042).

Conclusions: Used at the point of care during a primary care visit, the EHR-based diabetes risk calculator helped providers prioritize patients for diabetes preventive interventions, facilitated communication, and improved health outcomes among patients with prediabetes.