Reducing Type 2 Diabetes Diagnostic Delays Using Decision Support
Condition: Diabetes; Prediabetes
MeSH terms: Diabetes Mellitus
Study type: Interventional

Summary: 
      This study will focus on the cohort of 20,000 established patients cared for by 31 attending
      physicians in the outpatient, adult primary care practices at UT Southwestern (two general
      internal medicine one family medicine and one geriatric practice). The investigators will
      develop and implement an automated Diabetes Detection Tool (DDT) that does data mining on
      electronic medical record (EMR) lab data to systematically identify all primary care patients
      with elevated random plasma glucose results (RPGs) who are at high risk of diabetes and thus
      in need of further testing. In a cluster-randomized trial, primary care providers will be
      randomized to either the intervention/DDT arm or usual care. Providers in the intervention
      arm will receive visit-based, EMR-enabled case identification and real-time decision support.
      Outcomes will be tracked at a patient level. All subjects will be followed for 12 months to
      assess rates of follow-up diabetes testing, time to testing, rates of subsequent diabetes
      diagnosis, and time to diagnosis. The investigators hypothesize that the visit-based provider
      decision support will be superior to usual care.
    

Detailed description: 
      The growing epidemic of type 2 diabetes affects over 8.3% of the US population and presents a
      major challenge to healthcare systems and public health. An additional 7 million people have
      undiagnosed diabetes and over 79 million have pre-diabetes, which if unrecognized and
      untreated can progress to full-blown diabetes. Although screening and diagnostic tests are
      routinely available, health systems struggle to diagnose patients with diabetes in a timely
      manner. In fact, clinical diagnosis lags 8-12 years behind the onset of glucose
      dysregulation, resulting in diagnostic delays and the presence of diabetes complications at
      the time of diagnosis. Among patients engaged in clinical care without a known diagnosis of
      diabetes, nearly all patients have random plasma glucose (RPG) data available which
      potentially provides valuable, early warning safety signals regarding the need for further
      diabetes testing. However, elevated glucose values are commonly unrecognized and over 60% of
      abnormal values are not followed-up with diabetes testing in a timely fashion. Opportunities
      exist to leverage existing data within electronic medical records (EMR) to identify patients
      in need of further diabetes testing and develop systems-based solutions to reduce: 1)
      failures in following-up abnormal glucose tests, 2) delays in diagnosing diabetes, and 3)
      frequency of missed diagnoses of diabetes.

      This proposal will leverage the Epic EMR at the University of Texas Southwestern Medical
      Center (UTSW) to improve the detection and follow-up testing rates of abnormal glucose values
      in real-world practice.

      The investigators will conduct a cluster randomized, pragmatic trial comparing the
      effectiveness of a clinical decision support strategy versus usual care to reduce failures in
      timely follow-up of abnormal RPGs.

      The investigators will focus on the cohort of 20,000 established patients cared for by 31
      attending physicians in three outpatient, adult primary care practices at UTSW (two general
      internal medicine one family medicine and one geriatric practice). Primary care providers
      (PCPs) will be randomized to either the clinical decision support intervention or usual care.
      Providers in the clinical decision support/intervention arm will receive clinical decision
      support that identifies abnormal random glucose values and prompts providers to conduct
      diabetes screening. Outcomes will be tracked at the patient level and all subjects will be
      followed for 12 months to assess rates of follow-up diabetes testing, time to testing, rates
      of subsequent diabetes diagnosis, and time to diagnosis. Data on study eligibility, patient
      clinical risk factors and sociodemographics, provider and visit characteristics, and outcomes
      will be ascertained using the comprehensive Epic EMR. The investigators hypothesize that the
      visit-based provider decision support will be superior to usual care.
    

Intervention name(s): Clinical Decision Support
Intervention type(s): Other
Intervention description(s): Investigators will develop and implement an automated Diabetes Detection Tool (DDT) that does data mining on EMR lab data to systematically identify all primary care patients with elevated RPGs who are at high risk of diabetes and in need of further diabetes testing/screening.
Allocation: Randomized
Intervention model: Parallel Assignment
Masking: None (Open Label)
Primary purpose: Health Services Research
Keywords: Clinical decision support; Diabetes screening