Referrals from community optometrists to the hospital eye service in Scotland and England

Analysis of patient referrals from primary care to ophthalmology. The role of the optometrist

PURPOSE

The aim of this study was to characterize the quality of primary care referrals of patients to ophthalmology at the Virgen Macarena Hospital in Seville. This will enable us to optimize ophthalmologic resources and to evaluate the role of the optometrist in improving referrals.

METHODS

We performed a retrospective cross-sectional review of 220 ophthalmology consultations referred from primary care to the hospital from March to May 2022. The following data were extracted: age, sex, reason for consultation, diagnosis, priority level, whether it was an initial consultation or a follow-up visit, whether there was a secondary referral and whether the referral was appropriate. Excel (version 2312) was used for the data analysis.

RESULTS

The age range of the patients was from 3 years to 91 years. The patients were 41.8 % male and 58.2 % female. The conditions found were grouped as follows: cataracts (27.27 %), refractive errors (20.9 %), anterior segment disease (18.8 %), posterior segment disease (14.07 %), normal examination (18.63 %) and others (0.9 %). The most common reason for consultation was blurred vision or loss of vision (43.63 %). In total, 41.36 % of the consultations were considered inappropriate. The age group requiring the highest number of consultations was over 65 years (38.64 %).

CONCLUSIONS

With 41.36 percent of referrals deemed unnecessary, it is clear that referrals can be improved. This would reduce strain on the ophthalmology service and improve patient care. The importance of the optometrist in primary care is demonstrated by the fact that 20.9 % of the diagnoses were refractive errors.

Understanding natural language: Potential application of large language models to ophthalmology

Large language models (LLMs), a natural language processing technology based on deep learning, are currently in the spotlight. These models closely mimic natural language comprehension and generation. Their evolution has undergone several waves of innovation similar to convolutional neural networks. The transformer architecture advancement in generative artificial intelligence marks a monumental leap beyond early-stage pattern recognition via supervised learning. With the expansion of parameters and training data (terabytes), LLMs unveil remarkable human interactivity, encompassing capabilities such as memory retention and comprehension. These advances make LLMs particularly well-suited for roles in healthcare communication between medical practitioners and patients. In this comprehensive review, we discuss the trajectory of LLMs and their potential implications for clinicians and patients. For clinicians, LLMs can be used for automated medical documentation, and given better inputs and extensive validation, LLMs may be able to autonomously diagnose and treat in the future. For patient care, LLMs can be used for triage suggestions, summarization of medical documents, explanation of a patient's condition, and customizing patient education materials tailored to their comprehension level. The limitations of LLMs and possible solutions for real-world use are also presented. Given the rapid advancements in this area, this review attempts to briefly cover many roles that LLMs may play in the ophthalmic space, with a focus on improving the quality of healthcare delivery.

Large language models and their impact in ophthalmology

The advent of generative artificial intelligence and large language models has ushered in transformative applications within medicine. Specifically in ophthalmology, large language models offer unique opportunities to revolutionise digital eye care, address clinical workflow inefficiencies, and enhance patient experiences across diverse global eye care landscapes. Yet alongside these prospects lie tangible and ethical challenges, encompassing data privacy, security, and the intricacies of embedding large language models into clinical routines. This Viewpoint highlights the promising applications of large language models in ophthalmology, while weighing up the practical and ethical barriers towards their real-world implementation. This Viewpoint seeks to stimulate broader discourse on the potential of large language models in ophthalmology and to galvanise both clinicians and researchers into tackling the prevailing challenges and optimising the benefits of large language models while curtailing the associated risks.

Optometrists' Perspectives Regarding Artificial Intelligence Aids and Contributing Retinal Images to a Repository: Web-Based Interview Study

BACKGROUND

A repository of retinal images for research is being established in Scotland. It will permit researchers to validate, tune, and refine artificial intelligence (AI) decision-support algorithms to accelerate safe deployment in Scottish optometry and beyond. Research demonstrates the potential of AI systems in optometry and ophthalmology, though they are not yet widely adopted.

OBJECTIVE

In this study, 18 optometrists were interviewed to (1) identify their expectations and concerns about the national image research repository and their use of AI decision support and (2) gather their suggestions for improving eye health care. The goal was to clarify attitudes among optometrists delivering primary eye care with respect to contributing their patients' images and to using AI assistance. These attitudes are less well studied in primary care contexts. Five ophthalmologists were interviewed to discover their interactions with optometrists.

METHODS

Between March and August 2021, 23 semistructured interviews were conducted online lasting for 30-60 minutes. Transcribed and pseudonymized recordings were analyzed using thematic analysis.

RESULTS

All optometrists supported contributing retinal images to form an extensive and long-running research repository. Our main findings are summarized as follows. Optometrists were willing to share images of their patients' eyes but expressed concern about technical difficulties, lack of standardization, and the effort involved. Those interviewed thought that sharing digital images would improve collaboration between optometrists and ophthalmologists, for example, during referral to secondary health care. Optometrists welcomed an expanded primary care role in diagnosis and management of diseases by exploiting new technologies and anticipated significant health benefits. Optometrists welcomed AI assistance but insisted that it should not reduce their role and responsibilities.

CONCLUSIONS

Our investigation focusing on optometrists is novel because most similar studies on AI assistance were performed in hospital settings. Our findings are consistent with those of studies with professionals in ophthalmology and other medical disciplines: showing near universal willingness to use AI to improve health care, alongside concerns over training, costs, responsibilities, skill retention, data sharing, and disruptions to professional practices. Our study on optometrists' willingness to contribute images to a research repository introduces a new aspect; they hope that a digital image sharing infrastructure will facilitate service integration.