March 2012
Volume 53, Issue 14
Free
ARVO Annual Meeting Abstract  |   March 2012
Telemedicine-based Digital Retinal Imaging Improves Diabetic Retinopathy Screening Compliance
Author Affiliations & Notes
  • Seema Garg
    Dept of Ophthalmology, University of North Carolina, Chapel Hill, North Carolina
  • Bradley King
    Dept of Ophthalmology, University of North Carolina, Chapel Hill, North Carolina
  • Pooja Jani
    Dept of Ophthalmology, University of North Carolina, Chapel Hill, North Carolina
  • Sam Weir
    Dept of Ophthalmology, University of North Carolina, Chapel Hill, North Carolina
  • Thomas Karnowski
    Oak Ridge National Laboratory, Memphis, Tennessee
  • Stacy Li
    Hamilton Eye Institute, University of Memphis, Memphis, Tennessee
  • Ed Chaum
    Hamilton Eye Institute, University of Memphis, Memphis, Tennessee
  • Footnotes
    Commercial Relationships  Seema Garg, None; Bradley King, None; Pooja Jani, None; Sam Weir, None; Thomas Karnowski, TRIAD (I); Stacy Li, None; Ed Chaum, TRIAD (I)
  • Footnotes
    Support  NIH-funded EY 017065 and Prevent Blindness NC
Investigative Ophthalmology & Visual Science March 2012, Vol.53, 5743. doi:
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    • Get Citation

      Seema Garg, Bradley King, Pooja Jani, Sam Weir, Thomas Karnowski, Stacy Li, Ed Chaum; Telemedicine-based Digital Retinal Imaging Improves Diabetic Retinopathy Screening Compliance. Invest. Ophthalmol. Vis. Sci. 2012;53(14):5743.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract
 
Purpose:
 

Screening rates for diabetic retinopathy, a leading cause of preventable blindness, are low in the U.S. Telemedicine-based digital retinal imaging (DRI) has great potential to improve access to screening, yet data on its effectiveness are limited. We evaluated the impact of telemedicine-based-DRI on screening rates in a university-based primary care clinic. In addition, predictors of diabetic retinopathy were examined.

 
Methods:
 

A non-mydriatic retinal camera with TRIAD, our ocular telehealth network, was installed in the Family Medicine Center. A Chronic Disease Support specialist was trained to take retinal photographs and obtain patient characteristics including age, race, type of diabetes, total cholesterol,history of smoking, kidney disease, stroke, heart attack, coronary artery disease or hypertension. The images were transmitted to a single retina specialist (SG) who remotely classified the retinal images according to severity of diabetic retinopathy (DR). Logistic regression analysis was used to determine predictors of retinopathy. The proportion of diabetic patients screened for DR in the Family Medicine Center was determined prior to and one year after the installation of the telemedicine-based-DR.

 
Results:
 

One thousand and two (1002) patients with diabetes were screened. The mean age was 57 years. The mean duration of diabetes was 9.2 years. 46% were Caucasian and 48% were African-American. Among patients with any type of DR (11%), there was a strong racial difference (70% African-American, 24% Caucasian). Race,HbA1c and duration of DM were significantly associated with DR. The adjusted odds ratio (95% confidence interval) of DR for non-white patients was 2.2 (1.2 to 4.1), every one point increase in HbA1c was 1.5 (1.3 to 1.7) and for every 5 years - duration of diabetes was 1.5 (1.3 to 1.7). Within one year of the implementation of our ocular telehealth network, the screening rate improved from 32% to 71%.

 
Conclusions:
 

In this study, we demonstrated a dramatic improvement in DR screening rates in a large university-based primary care setting using a telemedicine-based-DRI . This technology has the potential to dramatically improve HEDIS compliance and access for patients at high risk of vision loss from DR.  

 
Keywords: diabetic retinopathy • clinical (human) or epidemiologic studies: outcomes/complications • imaging/image analysis: clinical 
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