To describe barriers to implementation of diabetic retinopathy (DR) teleretinal screening programs and artificial intelligence (AI) integration at the University of California (UC).
Methods:
Institutional representatives from UC Los Angeles, San Diego, San Francisco, Irvine, and Davis were surveyed for the year of their program's initiation, active status at the time of survey (December 2021), number of primary care clinics involved, screening image quality, types of eye providers, image interpretation turnaround time, and billing codes used. Representatives were asked to rate perceptions toward barriers to teleretinal DR screening and AI implementation using a 5-point Likert scale.
Results:
Four UC campuses had active DR teleretinal screening programs at the time of survey and screened between 246 and 2,123 patients at 1–6 clinics per campus. Sites reported variation between poor-quality photos (<5% to 15%) and average image interpretation time (1–5 days). Patient education, resource availability, and infrastructural support were identified as barriers to DR teleretinal screening. Cost and integration into existing technology infrastructures were identified as barriers to AI integration in DR screening.
Conclusions:
Despite the potential to increase access to care, there remain several barriers to widespread implementation of DR teleretinal screening. More research is needed to develop best practices to overcome these barriers.
Get full access to this article
View all access options for this article.
References
1.
ZhengY, LeySH, HuFB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol, 2018; 14(2):88–98.
2.
OgurtsovaK, GuariguataL, BarengoNC, et al.IDF diabetes Atlas: Global estimates of undiagnosed diabetes in adults for 2021. Diabetes Res Clin Pr, 2022; 183:109118.
3.
TeoZL, ThamYC, YuM, et al.Global prevalence of diabetic retinopathy and projection of burden through 2045: Systematic review and meta-analysis. Ophthalmology, 2021; 128(11):1580–1591.
4.
FlaxelCJ, AdelmanRA, BaileyST, et al.Diabetic Retinopathy Preferred Practice Pattern®. Ophthalmology, 2020; 127(1):P66–P145.
5.
SolomonSD, ChewE, DuhEJ, et al.Diabetic retinopathy: A position statement by the American Diabetes Association. Diabetes Care, 2017; 40(3):412–418.
6.
Quality ID#117 (NQF0055): Diabetes: Eye Exam. Available from: https://qpp.cms.gov/docs/QPP_quality_measure_specifications/CQM-Measures/2020_Measure_117_MIPSCQM.pdf [Last accessed: April 15, 2022].
7.
BresnickG, CuadrosJA, KhanM, et al.Adherence to ophthalmology referral, treatment and follow-up after diabetic retinopathy screening in the primary care setting. BMJ Open Diabetes Res Care, 2020; 8(1):e001154.
8.
KuoJ, LiuJC, GibsonE, et al.Factors associated with adherence to screening guidelines for diabetic retinopathy among low-income metropolitan patients. Mo Med, 2020; 117(3):258–264.
9.
LiuY, ZupanNJ, ShiyanbolaOO, et al.Factors influencing patient adherence with diabetic eye screening in rural communities: A qualitative study. PLoS One, 2018; 13(11):e0206742.
10.
MisraA, BachmannMO, GreenwoodRH, et al.Trends in yield and effects of screening intervals during 17 years of a large UK community-based diabetic retinopathy screening programme. Diabet Med, 2009; 26(10):1040–1047.
11.
Walton OBIV, GaroonRB, WengCY, et al.Evaluation of automated teleretinal screening program for diabetic retinopathy. JAMA Ophthalmol, 2016; 134(2):204–209.
12.
AfsharAR, OldenburgCE, StewartJM. A novel hybrid fixed and mobile ultra-widefield imaging program for diabetic teleretinopathy screening. Ophthalmol Retina, 2019; 3(7):576–579.
13.
KhouV, KhanMA, JiangIW, et al.Evaluation of the initial implementation of a nationwide diabetic retinopathy screening programme in primary care: A multimethod study. BMJ Open, 2021; 11(8):e044805.
14.
ThomasEE, HaydonHM, MehrotraA, et al.Building on the momentum: Sustaining telehealth beyond COVID-19. J Telemed Telecare, 2022; 28(4):301–308.
15.
ConlinPR, FischBM, OrcuttJC, et al.Framework for a national teleretinal imaging program to screen for diabetic retinopathy in Veterans Health Administration patients. J Rehabil Res Dev, 2006; 43(6):741–748.
16.
DaskivichLP, VasquezC, MartinezCJr, et al.Implementation and Evaluation of a Large-Scale Teleretinal Diabetic Retinopathy Screening Program in the Los Angeles County Department of Health Services. JAMA Intern Med, 2017; 177(5):642–649.
17.
MamillapalliCK, PrenticeJR, GargAK, et al.Implementation and challenges unique to teleretinal diabetic retinal screening (TDRS) in a private practice setting in the United States. J Clin Transl Endocrinol, 2020; 19:100214.
18.
Autonomous diabetic retinopathy screening system gains FDA approval. 2020. Available from: https://www.aao.org/headline/autonomous-diabetic-retinopathy-screening-system-g [Last accessed: April15, 2022].
19.
Office of the Commissioner. FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems. 2018. Available from: https://www.fda.gov/news-events/press-announcements/fda-permits-marketing-artificial-intelligence-based-device-detect-certain-diabetes-related-eye [Last accessed: April15, 2022].
20.
GunasekeranDV, TingDSW, TanGSW, WongTY. Artificial intelligence for diabetic retinopathy screening, prediction and management. Curr Opin Ophthalmol, 2020; 31(5):357–365.
21.
GrzybowskiA, BronaP. Analysis and comparison of two artificial intelligence diabetic retinopathy screening algorithms in a pilot study: IDx-DR and Retinalyze. J Clin Med Res, 2021; 10(11):2352.
22.
van der HeijdenAA, AbramoffMD, VerbraakF, et al.Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System. Acta Ophthalmol, 2018; 96(1):63.
23.
BaxterS, QuackenbushQ, CerdaJ, et al.Implementing clinical informatics tools for primary care-based diabetic retinopathy screening. Am J Manag Care, 2022; 28(10):e355–e362.
24.
EllisMP, BacornC, LuuKY, et al.Cost analysis of teleophthalmology screening for diabetic retinopathy using teleophthalmology billing codes. Ophthalmic Surg Lasers Imaging Retina, 2020; 51(5):S26–S34.
25.
LeeSC, AlberS, LiengMK, et al.Teleophthalmology using remote retinal imaging during the COVID-19 pandemic. Telemed J E Health, 2023; 29(1):81–86; doi: 10.1089/tmj.2022.0048
26.
ConnollySL, MillerCJ, GiffordAL, CharnessME. Perceptions and use of telehealth among mental health, primary, and specialty care clinicians during the COVID-19 pandemic. JAMA Netw Open, 2022; 5(6):e2216401.
27.
FishA, GeorgeS, TerrienE, et al.Workflow concerns and workarounds of readers in an urban safety net teleretinal screening study. AMIA Annu Symp Proc, 2011; 2011:417–426.
28.
OgunyemiO, GeorgeS, PattyL, et al.Teleretinal screening for diabetic retinopathy in six Los Angeles urban safety-net clinics: Final study results. AMIA Annu Symp Proc, 2013; 2013:1082–1088.
29.
Centers for Medicare and Medicaid Services. Medicare Program; CY 2022 Payment Policies Under the Physician Fee Schedule and Other Changes to Part B Payment Policies; Medicare Shared Savings Program Requirements; Provider Enrollment Regulation Updates; and Provider and Supplier Prepayment and Post-Payment Medical Review Requirements. Fed Regist, 2021; 86:64996–66031.
30.
LeeSC, LiengMK, AlberS, et al.Trends in remote retinal imaging utilization and payments in the United States. Ophthalmology, 2022; 129(3):354–357.
31.
SnoswellCL, TaylorML, ComansTA, et al.Determining if telehealth can reduce health system costs: Scoping review. J Med Internet Res, 2020; 22(10):e17298.
32.
Bastos de CarvalhoA, WareSL, LeiF, et al.Implementation and sustainment of a statewide telemedicine diabetic retinopathy screening network for federally designated safety-net clinics. PLoS One, 2020; 15(11):e0241767.
33.
PaynePRO, WilcoxAB, EmbiPJ, LonghurstCA. Better together: Integrating biomedical informatics and healthcare IT operations to create a learning health system during the COVID-19 pandemic. Learn Health Syst, 2022; 6(2):e10309.
34.
HeydonP, EganC, BolterL, et al.Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients. Br J Ophthalmol, 2021; 105(5):723–728.
35.
IppE, LiljenquistD, BodeB, et al.Pivotal evaluation of an artificial intelligence system for autonomous detection of referrable and vision-threatening diabetic retinopathy. JAMA Netw Open, 2021; 4(11):e2134254.
36.
MathengeW, WhitestoneN, NkurikiyeJ, et al.Impact of artificial intelligence assessment of diabetic retinopathy on referral service uptake in a low resource setting: The RAIDERS randomized trial. Ophthalmol Sci, 2022; 2(4):100168.
37.
LehneM, SassJ, EssenwangerA, et al.Why digital medicine depends on interoperability. NPJ Digit Med, 2019; 2:79.
38.
EvansNG, WennerDM, CohenIG, et al.Emerging ethical considerations for the use of artificial intelligence in ophthalmology. Ophthalmol Sci, 2022; 2(2):100141.
39.
WolfRM, ChannaR, AbramoffMD, LehmannHP. Cost-effectiveness of autonomous point-of-care diabetic retinopathy screening for pediatric patients with diabetes. JAMA Ophthalmol, 2020; 138(10):1063–1069.
40.
Al-KhaledT, ValikodathN, ColeE, et al.Evaluation of physician perspectives of artificial intelligence in ophthalmology: A pilot study. Invest Ophthalmol Vis Sci, 2020; 61(7):2023.
41.
LaïMC, BrianM, MamzerMF. Perceptions of artificial intelligence in healthcare: Findings from a qualitative survey study among actors in France. J Transl Med, 2020; 18(1):14.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
