Developing an Explainable AI Model to Identify Members with Diabetes at High Risk for Worsening Glycemic Control in a Large Accountable Care Network (ACN)

People with diabetes and poor glycemic control (HbA1c ≥9%) have a greater likelihood of diabetes complications, increased healthcare utilization, and higher total cost of care (TCOC). Novel approaches to identify patients at high risk for worsening glycemic control can improve outcomes and contain costs within ACNs. We used XGBoost Decision Trees to build and validate a model predicting worsening glycemic control (HbA1c increase ≥1.5% over 12 months) using Medicare ACN administrative claims and electronic health record data. Eligible members had ≥18 mos. continuous enrollment, were age ≥18 years with Type 1 or 2 diabetes, had ≥2 HbA1c values between 6.5 and 14%, and no disability. Members were split into HbA1c outcome stratified training (80%) and validation (20%) datasets for 10-fold cross-validation. The model utilized 1,490 features including medical history, labs, demographics, SDOH, utilization, and quality of care. Of the 44,007 unique members (mean age 72 years, 51% Female, 61% Non-Hispanic White, mean HbA1c 7.2%, 9% on insulin, HTN 92%, Obesity 71%, CKD 33%, CAD 32%, Heart Failure 15% and COPD 13%), 5.23% had an HbA1c increase ≥1.5% over 12 months. Key features in the final model included duration of diabetes, last HbA1c value, treatment intensification, prescribed insulin or sulfonylureas, weight change, depression, acute renal failure, area deprivation index, and no car access. Model AUC was 0.672 (95% CI ±0.005) in the validation sample. The model was able to identify 23.5% of members with worsening glycemic control in the highest predicted decile of HbA1c progression (sensitivity 23.5% at 10% alert rate). Mean TCOC at 10% alert rate was $43,850 PMPY which was substantially higher than training population at $18,208 PMPY. Our model can identify ACN members with diabetes at high risk for worsening glycemic control that may benefit from education, medication adherence, food insecurity and transportation interventions. Disclosure A.Kermani: None. C.J.Mccall: None. J.A.Gartner: None. D.Decaprio: Employee; ClosedLoop.ai. J.M.Mutz: None. J.S.Fish: None. U.Kollipara: None. J.Acosta: None. M.E.Bowen: Research Support; Boehringer-Ingelheim. J.P.Almandoz: Advisory Panel; Eli Lilly and Company, Novo Nordisk. B.Goldberg: None. A.Rozich: None. M.He: None. S.V.J.Acosta: None.