Study Shows Care Gap in Diabetic Eye Exams Can Be Closed With AI
Thanks to autonomous AI, the eye screening rates for youth with diabetes have improved, helping to close the gap in care. According to a report by HCPLive.com, the research done shows that diverse populations are seeing a gap reduction in their care.
Researchers in the paper found that the randomized controlled trial revealed that their findings remained accurate despite an expansion in the standard of care referral among the control. This consisted of deliberate education for both the patient and caregiver on the importance of diabetic eye disease.
The lead author of the study, Risa M. Wolf, MD, a pediatric endocrinologist at Johns Hopkins Children’s Center, said, “With AI technology, more people can get screened, which could then help identify more people who need follow-up evaluation,”.
Dr. Wolf continued, “If we can offer this more conveniently at the point of care with their diabetes doctor, then we can also potentially improve health equity, and prevent the progression of diabetic eye disease.”.
At issue is the gap in both screenings and early detection in at-risk communities. With diabetic eye disease being early to the fight is critical for managing symptoms. But, with a lack of access to resources and education, there is a worrying care gap that affects as many as 34 million people with diabetes in the United States.
But with the ACCESS trial, Wolf and collogues hypothesized that autonomous AI diabetic eye exams at the locations of point-of-care would increase diabetic eye exam completion rates among a more diverse youth population.
Youths with both type 1 (8 to 21 years of age) and type 2 (11 to 21 years of age) were included in the trial. It also ran parallel with a randomized control which required that they met the criteria for diabetic eye disease screening per American Diabetes Association 2021 guidelines.
Both sets of participants were randomized to a 1:1 to the intervention arm/control group. The intervention arm consisted of an autonomic AI diabetic eye example at the point of care. This included scripted eye care provider referral and education.
The study had a total of 177 individuals with 81 being part of the intervention group and 83 making up the control. Both groups had similar baseline characteristics which included a mean age of 15.2 years, and a 58% female participation rate.
According to the study, the primary outcome of the intervention group was a significantly higher diabetic eye exam completion rate, 100% compared to 22% of the control group. The researchers believe that the difference of 78% in the gap between the groups was statistically significant and they noted that there were no significant differences in any personal variables within the groups.
The study is an important marker to show where AI can help fill in care gaps for patients, practically those from diverse groups. Dr. Wolf concluded, “The high satisfaction and acceptance rates for autonomous AI in ACCESS, suggest that this racially and socioeconomically diverse patient population is comfortable with a ‘computer’ or autonomous AI diagnosing their disease,”
Dr. Wolf and colleagues wrote, “Importantly, the use of AI did not introduce health disparities into care-gap closure.”
Originally posted on OpenDataScience.com
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