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Maria Martynova

Pediatric ophthalmologist, Retina imaging expert

Reading time

7 min read

Ophthalmology has the highest average number of patients waiting, but up to 75% of patients make preventable trips to eye hospitals and general practitioners. Some of these patients are referred by optometrists who, more often than not, receive no feedback on the quality of their referrals, perpetuating this cycle. This article examines the referral procedure and potential solutions for increasing referral efficiency in eye care that practitioners can implement.

More than 25% of U.S. counties lack a single practicing eye care provider, and the situation isn’t unique to the U.S. In the UK, ophthalmology has been the most overburdened healthcare sector for some time. With a globally aging population and an increasing prevalence of age-related diseases, ensuring accessible eye care is crucial. Unfortunately, the reality is quite the opposite. One contributing factor is the high number of failures in the referral process.

How did we arrive at this point, and what can be done to improve it?

Altris AI’s survey identified a lack of data and increased patient wait times as the top problems with referrals for practitioners, while lack of co-management tools and poor communication/feedback ranked lower.

What are the top problems with the referral that eye care specialists are facing

Let’s dive into more details:

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What are the top problems that eye care specialists are facing with referrals?

  • Lack of diagnostic data

The ultimate goal of a referral is to ensure patients receive appropriate treatment for their specific pathology or confirmation of its absence. The receiving specialist’s first step is to review the referral report, making its completeness and clarity paramount. While there is a clear need for specialised assessment and treatment, almost 80% of those attending eye casualty do not require urgent ophthalmic attention following triage, and up to 60% of patients are seen and discharged on their first visit.

In eye care, both text information and accompanying images are crucial in ensuring efficient and accurate diagnoses. 

However, handwritten and fragmented data continue to pose significant challenges in the patient referral process. Despite the prevalence of electronic health records (EHRs), over half of referrals are still handled through less efficient channels like fax, paper, or verbal communication. This can lead to fragmented or doubled patient data, potential gaps in care, and delays in treatment. 

The study on the Impact of direct electronic optometric referral with ocular imaging to a hospital eye service showed that, given some limitations, electronic optometric referral with images to a Hospital Eye Service (HES) is safe, speedy, efficient, and clinically accurate, and it avoids unnecessary HES consultations. 

Antonella-Durante

Direct electronic referrals with images reduced the need for hospital eye service appointments by 37% compared to traditional paper referrals. Additionally, while 63% of electronic referrals led to HES appointments, this figure was 85% for paper referrals. 

Biomarkers measuring on Altris AI OCT report

While incorporating images like OCT scans can significantly enhance understanding, some subtle or early-stage pathologies might still be overlooked. This is where detailed and customized reports become invaluable.

To illustrate the point, here is a handwritten referral compared to one of the types of customised OCT report from the Altris AI system, a platform that automates AI-powered OCT scan analysis for 70+ pathologies and biomarkers. This screenshot, in particular, shows segmented retina layers and highlights biomarkers of Dry AMD alongside a comparison of the patient’s macular thickness over visits.

Increasing Referral Efficiency in Eye Care: customizable OCT reports vs written reports

  • Lack of experience and access to second opinion

Research reveals a notable inverse relationship between clinician experience and the frequency of false-positive referrals in optometry, echoing findings in other medical fields where diagnostic proficiency typically improves with experience. This highlights the importance of recognizing the learning curve inherent in optometric practice and supporting less experienced practitioners. 

The challenge is amplified by the fact that optometrists often practice in isolation, lacking the immediate professional support network available to their hospital-based counterparts. Unlike colleagues in hospital settings who have ready access to peer consultation for other opinions or guidance, optometrists often face limited opportunities for collaborative decision-making and skill development. 

Another problem specialists often face is a lack of confidence in diagnosing, which may or may not be linked to experience. Knowing that their patients could potentially suffer irreversible vision loss from a pathology not yet detected during an exam, they often err on the side of caution and refer to a hospital. While this “better safe than sorry” approach is understandable, it places a significant burden on hospitals, extending wait times for those already at risk of blindness.

These concerns primarily revolve around glaucoma, age-related macular degeneration (AMD), and diabetic retinopathy (DR). AI can help identify these and other eye diseases at their earliest stages during routine visits. Some retinal changes are so minute that they escape detection by the human eye, making the program’s ability to detect tiny retinal changes invaluable.

Another significant benefit of AI systems lies in their approach to OCT analysis for glaucoma. Traditional methods rely on normative databases to assess retinal normality, but these databases are often limited in size and represent a select group of individuals. This can result in missed diagnoses of early glaucoma in those who deviate from the “norm” or unnecessary referral from optometry to ophthalmology for those who don’t fit the “normal” profile but have healthy eyes. AI can overcome this limitation by providing more personalized and comprehensive analysis.

  • Increased wait times for patients

The National Health Service (NHS) is grappling with significant backlogs in ophthalmology services, which account for nearly 10% of the 7.8 million patients awaiting treatment. 

The consistently high average number of patients waiting per trust in Ophthalmology, with high follow-up waitlists, delays care that poses substantial risks. The Royal College of Ophthalmologists reported that the risk of permanent visual loss is nine times higher in follow-up patients than in new patients. With 30% more patients on ophthalmology waitlists than pre-pandemic, the number of people at risk of sight loss may have increased.

Community Eyecare (CHEC), a provider of community-based ophthalmology services, received around 1000 referrals per week before the pandemic, further highlighting the strain on the system.

An analysis of electronic waitlists revealed that administrative issues, such as deceased patients or those already under care remaining on the list, artificially inflate wait times by up to 15%. 

Improving administrative processes and reassessing referrals for appropriateness could help address this problem. Additionally, interim optometric examinations could revise referral information or determine the necessity of hospital visits, further reducing wait times.

Artificial intelligence can significantly speed up the screening process while reducing the controversy around diagnoses. This faster and more accurate diagnostic tool will enable more patients to be seen, allow for quicker responses to pathologies that pose a risk to eyesight, and reduce the burden on strained hospitals with needless patient referrals, as well as free up patients from unnecessary stress and wasted time.

International studies have shown that collaborative care also can increase screening and detection rates of eye disease.

  • Lack of comanagement tools for eye care providers

The increasing demand for Hospital Eye Services, projected to grow by 40% in the next two decades and currently accounting for 8% of outpatient appointments, necessitates a re-evaluation of referral pathways and comanagement strategies between optometrists and ophthalmologists.  

The lack of digital connectivity between primary, community, and secondary care creates a significant barrier to effective collaboration. In many cases, optometrists cannot make direct digital referrals to Hospital Eye Service, often relying on general practitioners as intermediaries, causing delays in diagnosis and treatment.

The COVID-19 pandemic highlighted the vital role of optometrists as first-contact providers for eye health, relieving pressure on hospitals. However, better integration between primary and secondary care is essential to build upon this and create a more sustainable eye care system. The current lack of digital connectivity hinders efficient communication and impedes the timely transfer of patient records, potentially leading to unnecessary referrals and delays in care.

As David Parkins, the ex-president of the College of Optometrists, emphasizes, the solution lies in increased integration and streamlined communication between primary and secondary eye care services. Implementing integrated digital platforms for referrals and feedback can enhance collaboration, improve patient outcomes, and reduce the burden on hospitals.

Leveraging optometrists’ expertise through shared care programs and direct digital referral pathways can alleviate the strain on eye hospitals and ensure timely access to care for patients with eye conditions.

  • Poor communication/lack of feedback

A recent study published in Ophthalmic and Physiological Optics revealed that in 73% of cases, the referring optometrist was unaware of the outcome of their referral. 

This lack of closure can lead to unnecessary re-referrals, patient anxiety, and potential treatment delays that could result in preventable vision loss, especially considering the extended waiting times for hospital eye service appointments.

Effective referral in eye care requires a closed feedback loop, where referring providers receive timely updates and reports from specialists. However, studies have shown that up to 50% of primary care providers (PCPs) are unsure whether their patients have even been seen by the referred specialists. This disconnect necessitates time-consuming follow-up calls and manual data integration, increasing the risk of errors and jeopardizing patient care.

The absence of consistent feedback also impacts optometrists’ professional development. Without knowing the accuracy of their referrals, optometrists cannot identify areas for improvement or refine their diagnostic skills. This is particularly relevant for newly qualified practitioners who may benefit from feedback to enhance their clinical judgment.

Implementing electronic referral systems that include feedback mechanisms can significantly improve communication and close the feedback loop. This would enable optometrists to track the progress of their referrals, receive timely updates on patient outcomes, and make informed decisions about future referrals. 

Technology is also bridging the gap in specialist communication by enabling secure online consultations, such as live chat with dedicated ophthalmologists. A notable example in the UK is Pocket Eye, a platform designed to empower eye care professionals with clinical advice, diagnostic and image support, and AI-powered OCT analysis. 

Summing up

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Implementing digital platforms that foster collaboration between eye care providers, increasing confidence in complex cases, and utilizing AI technologies to expedite diagnostics is crucial in a world where an aging population will increasingly rely on healthcare.