Forecasting the Burden of Type 2 Diabetes Using Electronic Health Records
March 31 @ 9:00 am - 10:30 am
One-size-fits-all approaches to patient care for individuals with Type 2 Diabetes (DM2) have often failed to show objective results in managing DM2. Patients with risk factors for adverse DM2 health outcomes are more likely to struggle with managing DM2. We propose methods, using electronic health records, to improve DM2 patient monitoring by measuring individualized risk level to forecast the burden of the disease. In order to help facilitate greater taxonomy in how to care for individual DM2 patients, we consider the importance in identifying both potentially modifiable clinical and economic risk factors (i.e. depression diagnosis, socioeconomic position, hemoglobin A1c level) as well as the selection pressures of aging and time via age-period-cohort effects that have influenced the course of DM2 disease progression and mortality.