Over 7 million people in the US have undiagnosed diabetes, and an additional 73 million have undiagnosed prediabetes. Although early diagnosis and treatment can improve health outcomes in both conditions, only half of individuals eligible for diabetes screening have been screened in the past 3 years. Automated, electronic medical record (EMR)-based diabetes risk assessment and systematic approaches to diabetes screening may improve screening rates. Although automation of current screening guidelines within EMRs is challenging, data suggest that a single random glucose value is a better predictor of diabetes than national screening guidelines (which are based on age, sex, race, body mass index, and other health conditions). However, the sensitivity of this approach to diagnose both prediabetes and diabetes is poor. Utilization of multiple glucose values over time - a patient's glucose history - may improve the sensitivity of random glucose screening strategies to detect undiagnosed prediabetes and diabetes. This proposal describes a career development plan that will prepare the candidate to become a successful independent investigator and attain his long-term career goal of becoming a national leader in the development and implementation of EMR interventions to improve the diagnosis and health outcomes of patients with type 2 diabetes. This proposed research strategy will develop a novel, computerized glucose history-based diabetes risk assessment tool and then utilize this tool to develop and implement clinical decision support in the EMR to promote diabetes screening in routine clinical practice. The PI's immediate goal is to use the longitudinal glucose values available within the EMR - the "glucose history" - to develop a random blood glucose (RBG)-based diabetes risk assessment tool and clinical decision support. To meet this goal, he has proposed a career development plan that integrates didactic coursework, participation in local and national conferences and workshops, and a progression of mentored research studies within the supportive research environment at University of Texas Southwestern Medical Center and Parkland Hospital in Dallas, TX. This environment includes a NIH-funded CTSA and Clinical and Translational Research Center, an AHRQ-funded Center for Patient-Centered Outcomes Research, and the Parkland Center for Clinical Innovation, an entity that does advanced healthcare analytics and predictive modeling with EMR data.
The research aims of this project are to: 1) characterize the glucose history of patients without known diabetes using glucose data available in the EMR and describe associations between an abnormal glucose history and diabetes screening;2) conduct a prospective diabetes screening study to develop and optimize performance of a RBG risk tool to identify cases of undiagnosed diabetes and prediabetes using the EMR glucose history;and 3) develop and assess the feasibility of EMR-enabled diabetes screening clinical decision support using the RBG risk tool from Aim 2.
These research aims will serve as the platform for the career development plan and training aims which include: 1) training in applied medical informatics;2) advanced quantitative analyses;3) implementation science;and 4) comparative effectiveness research. Together, the research and training aims of the K23 proposal will provide the training, experience, and preliminary data for two R01 applications. One R01 will be a larger diabetes screening study with additional oral glucose tolerance testing based on Aim 2. The other R01 will be based on the pilot in Aim 3 and propose a fully-powered, multisite randomized controlled trial to assess the efficacy of the EMR-based diabetes screening tool and clinical decision support to identify cases of diabetes and prediabetes in clinical practice. The proposed research and training aims will strategically position the PI to become a leader in the development, implementation, and evaluation of evidence-based interventions to improve outcomes in type 2 diabetes. The innovative approach to diabetes risk identification and screening outlined in this K23 proposal has the potential for very high impact on clinical care, population health management, and national screening guideline policies for this very common, serious, and costly disease.
Over 40% of the US population has either diabetes or prediabetes. Alarmingly, an additional 7 million people with diabetes and 73 million people with prediabetes remain undiagnosed. In spite of this, only half of eligible patients complete guideline-indicated diabetes screening. To curb the growing epidemic of diabetes and prediabetes, improved screening strategies are needed. We will harness the electronic medical record (EMR) to identify patients at risk for diabetes and promote the delivery of diabetes screening in clinical practice. The proposed research will characterize glucose data available within the EMR - the glucose history - and harness it to develop a novel random glucose-based risk assessment tool to identify patients at high risk for diabetes. The newly developed random-glucose detection tool will provide the foundation for building EMR- based, diabetes screening clinical decision support to improve screening in clinical practice. This novel approach, which leverages commonly available but underutilized data, will be developed in a common EMR (Epic) and has the potential for wide dissemination and implementation if successful.