Introduction to Diabetic Retinopathy and Its Impact

Introduction to Diabetic Retinopathy and Its Impact

Diabetic retinopathy (DR) is a significant complication of diabetes mellitus (DM) and a leading cause of blindness globally, especially among older adults. This condition arises from damage to the blood vessels in the retina due to prolonged high blood sugar levels, leading to gradual vision loss if left untreated (Raman et al., 2022). Globally, 10.9% of individuals aged 65 years and above with diabetes have DR, with 2.3% suffering from vision-threatening diabetic retinopathy (VTDR). VTDR is characterized by severe retinal damage, necessitating urgent medical intervention (Vujosevic et al., 2021).

The prevalence of DR is expected to rise as the global population ages and the incidence of diabetes increases. In India, the rapid growth of the elderly population, projected to reach 198 million by 2030, exacerbates the challenge of managing DR (Government of India, 2023). Regular screening and timely treatment can prevent most cases of vision loss caused by DR; however, many individuals do not receive appropriate care due to barriers such as lack of awareness, limited access to healthcare facilities, and logistical challenges (Padhy et al., 2020).

Importance of Referral Adherence in Diabetes Management

Referral adherence is crucial in diabetes management, particularly following diabetic retinopathy screening (DRS). Effective management of DR involves not only screening but also ensuring that patients who are diagnosed are referred to the appropriate healthcare services for further evaluation and treatment. Studies have shown that adherence to referral recommendations directly impacts the likelihood of patients receiving necessary treatments that can prevent vision loss (Wong et al., 2022).

Despite the availability of AI-driven screening technologies that have shown promise in improving DR detection, referral adherence rates remain suboptimal. For instance, only 14.5% of patients who were referred after AI-enhanced screening in a recent study followed through with appointments (Chauhan et al., 2025). This highlights the need to explore and address the factors influencing adherence to referrals.

Barriers to Referral Adherence Among Older Adults

The challenges faced by older adults in adhering to referral recommendations can be attributed to various factors, including:

1. Limited Awareness and Knowledge: Many older adults lack adequate understanding of diabetic retinopathy and its potential consequences. Misconceptions about their eye health lead to a lack of urgency in seeking further care (Kumar et al., 2023).

2. Logistical Challenges: Access to healthcare can be significantly constrained by factors such as transportation difficulties, long travel distances, and high treatment costs, which are burdensome for older adults (Piyasena et al., 2021).

3. Health System Limitations: Overburdened health systems, especially in rural areas, can lead to long wait times and reduced availability of specialists, discouraging follow-up visits (Bonilla-Escobar et al., 2021).

4. Attitudinal Barriers: Some patients may feel that their condition is stable and therefore do not prioritize follow-up care. This attitude can be detrimental as it leads to delayed treatment and worsening of the disease (Adhikari et al., 2019).

5. Financial Constraints: The cost associated with treatments, including advanced therapies like anti-VEGF injections, can deter patients from following through with referrals (Kumar et al., 2023).

Table 1 summarizes key barriers identified in various studies regarding referral adherence among older adults with diabetic retinopathy.

Role of AI in Diabetic Retinopathy Screening

Artificial intelligence (AI) has emerged as a transformational tool in enhancing diabetic retinopathy screening. AI algorithms can analyze retinal images quickly and accurately, providing timely assessments and recommendations for further referral. The integration of AI in DRS has been shown to improve diagnostic accuracy and streamline workflows in ophthalmology clinics, consequently enhancing patient access to necessary care (Mathenge et al., 2022).

Several studies have highlighted the effectiveness of AI in DRS, where it enabled earlier detection of DR, increased screening rates, and improved referral adherence (Kumar et al., 2022). For instance, a recent study indicated that AI-driven feedback significantly increased the likelihood of patients adhering to referral recommendations post-screening (Chauhan et al., 2025).

Strategies to Improve Referral Adherence and Outcomes

Improving referral adherence among older adults following AI-enabled diabetic retinopathy screening requires a multifaceted approach:

1. Enhancing Patient Education: Increasing awareness about diabetic retinopathy and its implications can empower patients to seek timely care. Educational interventions should be tailored to address common misconceptions and emphasize the importance of follow-up appointments (Raman et al., 2022).

2. Streamlining Logistics: Addressing transportation challenges through community-based solutions, such as transport vouchers or mobile health units, can facilitate access to healthcare facilities (Piyasena et al., 2021).

3. Utilizing Telehealth: Integrating telehealth services can help bridge the gap in access to care, allowing patients to receive consultations and follow-up care remotely, which has become increasingly relevant in the post-COVID-19 era (Bonilla-Escobar et al., 2021).

4. Strengthening Health Systems: Improving the infrastructure and capacity of healthcare systems to handle referrals and manage diabetic retinopathy can enhance patient outcomes. This may involve training additional healthcare providers and ensuring appropriate resources are available at referral centers (Kumar et al., 2022).

5. Building Support Networks: Encouraging family involvement in healthcare decisions can provide emotional and logistical support for older patients, increasing their likelihood of adhering to referrals (Adhikari et al., 2019).

FAQ Section

What is diabetic retinopathy?

Diabetic retinopathy is a complication of diabetes characterized by damage to the blood vessels in the retina, which can lead to vision loss and blindness.

Why is referral adherence important in diabetes management?

Referral adherence is crucial because it ensures that patients receive necessary follow-up care and treatments that can prevent vision loss due to diabetic retinopathy.

What are common barriers to referral adherence for older adults?

Common barriers include limited awareness of the disease, logistical challenges such as transportation, health system limitations, financial constraints, and attitudinal barriers.

How can AI improve diabetic retinopathy screening?

AI can enhance screening by analyzing retinal images with high accuracy, providing timely assessments, and streamlining referral processes, which can lead to improved patient outcomes.

What strategies can be implemented to improve referral adherence?

Strategies include enhancing patient education, streamlining logistics, utilizing telehealth, strengthening health systems, and building support networks involving family members.

References

1. Raman, R., Vasconcelos, J. C., Rajalakshmi, R., et al. (2022). Prevalence of diabetic retinopathy in India stratified by known and undiagnosed diabetes, urban-rural locations, and socioeconomic indices: results from the SMART India population-based cross-sectional screening study. Lancet Global Health, 10(12), e1764-e1773. DOI: [10 22)00411-9)

2. Vujosevic, S., Aldington, S. J., Silva, P., et al. (2021). Screening for diabetic retinopathy: new perspectives and challenges. Lancet Diabetes Endocrinol, 8(4), 337-347. DOI: [10 19)30411-5)

3. Padhy, D., Pyda, G., Marmamula, S., Khanna, R. C. (2020). Barriers to uptake of referral services from secondary eye care to tertiary eye care and its associated determinants in L V Prasad Eye Institute network in Southern India: a cross-sectional study-report I. PLoS ONE, 14(9), e0303401. DOI: 10.1371/journal.pone.0303401

4. Bonilla-Escobar, F. J., Ghobrial, A. I., Gallagher, D. S., Eller, A., Waxman, E. L. (2021). Comprehensive insights into a decade-long journey: the evolution, impact, and human factors of an asynchronous telemedicine program for diabetic retinopathy screening in Pennsylvania, United States. PLoS ONE, 16(1), e0305586. DOI: 10.1371/journal.pone.0305586

5. Kumar, A., The transformation of the Indian healthcare system. (2023). Cureus, 15(7), e39079. DOI: 10.7759/cureus.39079

6. Mathenge, W., Whitestone, N., Nkurikiye, J., et al. (2022). Impact of artificial intelligence assessment of diabetic retinopathy on referral service uptake in a low-resource setting: the RAIDERS randomized trial. Ophthalmology Science, 2(4), 100168. DOI: 10.1016/j.xops.2022.100168

7. Piyasena, M., Murthy, G. V. S., Yip, J. L. Y., et al. (2021). Systematic review on barriers and enablers for access to diabetic retinopathy screening services in different income settings. PLoS ONE, 14(4), e0198979. DOI: 10.1371/journal.pone.0198979

8. Chauhan, A., Goyal, A., Masih, R., et al. (2025). Barriers and determinants of referral adherence in AI-enabled diabetic retinopathy screening for older adults in Northern India during the COVID-19 pandemic: Mixed methods pilot study. JMIR Formative Research, 27. DOI: 10.2196/67047

9. Wong, T. Y., Sun, J., Kawasaki, R., et al. (2022). Guidelines on diabetic eye care: the International Council of Ophthalmology recommendations for screening, follow-up, referral, and treatment based on resource settings. Ophthalmology, 125(10), 1616-1622. DOI: 10.1016/j.ophtha.2018.04.007