Real-world data is becoming one of the most important tools shaping modern ophthalmology. As healthcare systems increasingly rely on electronic health records, registries, imaging platforms, and patient-reported outcomes, researchers are gaining access to larger volumes of clinical information than ever before. That information has the potential to improve treatment strategies, evaluate long-term outcomes, and strengthen clinical decision-making across retinal disease management. But according to a new Delphi consensus published in Eye, one major problem continues limiting the value of ophthalmology real-world data: inconsistency.
Researchers from across the global ophthalmology community argue that variability in data collection, reporting standards, treatment methodologies, and patient outcome tracking is making it increasingly difficult to compare studies, assess treatment effectiveness, and generate reliable real-world evidence.
“The ophthalmological clinical community agrees there is a need to standardise RWD methodologies.”
— Delphi consensus published in Eye (2026)
Why real-world data matters in ophthalmology
Real-world data, often referred to as RWD, helps bridge the gap between controlled clinical trials and routine patient care. While randomized controlled trials remain essential for evaluating safety and efficacy, they often involve highly selective patient populations and tightly controlled treatment environments.
Real-world ophthalmology data provides a broader picture of how treatments actually perform in clinical practice across diverse patient populations, healthcare systems, and treatment settings. This has become especially important in retinal disease management, including conditions such as neovascular age-related macular degeneration and diabetic macular edema.
As anti-VEGF therapies, imaging technologies, and treatment strategies continue evolving, ophthalmologists increasingly depend on real-world evidence to evaluate treatment durability, patient adherence, injection intervals, long-term outcomes, and disease progression. The challenge, however, is that many clinics and healthcare systems collect data differently.
The problem researchers are trying to solve
The consensus study gathered responses from 244 healthcare professionals, the majority of whom were retina specialists with more than five years of clinical experience. Participants represented multiple global regions and reached consensus across all 38 statements included in the Delphi process.
One of the strongest themes throughout the study was the growing concern surrounding fragmented data collection systems and inconsistent methodologies across ophthalmology research.
Researchers noted that clinics often record visual acuity, optical coherence tomography findings, intraocular pressure measurements, treatment intervals, and adverse events using different formats, systems, and reporting standards. Electronic health record interoperability also remains a major challenge, making cross-center comparisons difficult.
These inconsistencies create significant barriers when researchers attempt to aggregate findings or compare treatment outcomes between studies. According to the authors, the lack of standardization ultimately weakens the reliability, reproducibility, and generalisability of ophthalmology real-world evidence.
Why consistency improves the quality of evidence
The consensus emphasized the need for standardized frameworks capable of improving both clinical research quality and long-term patient care. Researchers recommended developing minimum datasets that consistently capture demographic information, anatomical findings, functional outcomes, treatment intervals, adverse events, and follow-up assessments.
The study also highlighted the importance of balancing aspirational standards with practical implementation. While advanced imaging data and artificial intelligence-assisted analysis may strengthen future ophthalmology datasets, researchers acknowledged that not all healthcare systems currently possess the infrastructure needed to support highly complex reporting systems.
Because of this, the consensus encourages healthcare systems to focus on scalable and clinically practical approaches that reduce documentation burden while still improving data quality.
One of the strongest recommendations involved the development of single-point data entry systems that integrate more naturally into routine clinical workflows. Researchers believe reducing duplicate documentation could significantly improve consistency while making real-world data collection more sustainable for busy ophthalmology practices.
The growing role of patient-reported outcomes
Another important theme throughout the consensus involved patient-reported outcome measures, commonly known as PROMs. Researchers argued that ophthalmology research should move beyond purely anatomical and imaging-based measurements by incorporating patient experience and quality-of-life metrics more consistently.
The consensus recommended collecting PROMs at baseline and then approximately every 12 months to better understand how treatment affects daily functioning, visual quality of life, and long-term patient satisfaction.
While PROMs are widely recognized as valuable, many current tools remain difficult to implement consistently in routine practice because of time constraints and workflow limitations.
The authors suggest that simplified digital questionnaires integrated directly into electronic health record systems may eventually help improve adoption and support more patient-centered ophthalmology care.
“The near-unanimous agreement across all statements highlights that establishing a standard framework for ophthalmology RWD methodologies is an important step toward improving the quality of research, supporting clinical decision-making, and enabling more reliable assessment of treatment outcomes.”
— Eye (2026) Delphi consensus discussion
What this could mean for future ophthalmology research
For ophthalmology, stronger real-world data standards could eventually improve everything from treatment optimization and registry development to healthcare policy decisions and clinical trial design.
Researchers also emphasized the need for formalized education and training programs focused specifically on real-world data methodologies. Building stronger literacy around data quality, methodology, and reporting standards may become increasingly important as ophthalmology continues adopting digital healthcare systems and AI-assisted analytics.
Although many of the recommendations remain aspirational, the consensus represents one of the clearest signals yet that the ophthalmology community increasingly recognizes the need for more unified and standardized approaches to real-world evidence generation.
Ultimately, better data consistency may lead to stronger evidence, more reliable treatment evaluation, improved collaboration between healthcare systems, and better long-term outcomes for patients living with retinal disease.
Source: Eye Journal
