Real-world Data Helping to Improve Personalized Care in Ophthalmology

In the world of medicine, the one-size-fits-all approach is increasingly giving way to personalized care. Ophthalmology is no exception. Personalized medicine in ophthalmology is poised to revolutionize the way clinicians diagnose, treat, and manage eye conditions. At the heart of this transformative approach, lies real-world data.

Understanding Personalized Medicine in Ophthalmology

Ophthalmology, similar to other medical fields, is embracing personalized medicine. This approach recognizes that every patient is unique, and what works for one individual, may not be effective for another. In the context of eye care, personalized medicine involves tailoring ophthalmic treatments to the individual characteristics of a patient’s eyes and their specific condition.

The Importance of Real-world Data in Personalized Care

Data, in its various forms, is vital to personalized medicine. Here are six common ways in which real-world data is used, or will soon be used, to personalize medicine within ophthalmology:

1. Genomic Data – By analyzing a patient’s genetic makeup, ophthalmologists could identify specific genetic variants associated with specific or rare eye diseases. This data would allow for the selection of targeted treatments and early risk assessment. Some examples of how this could be done include the use of genome-wide association studies (GWAS), whole-exome sequencing (WES) and whole genome sequencing (WGS)

2. Patient Histories and Medical Records – Electronic health records (EHRs) and patient histories provide a treasure trove of data. They help clinicians track the progression of eye diseases, assess treatment efficacy, and understand how a patient’s condition has evolved over time. EHR data is also often used in clinical drug development.

3. Imaging and Diagnostic Data – Data from ophthalmic imaging modalities, such as optical coherence tomography (OCT) and fundus photography, offer crucial insights into the structure and function of the eye. Artificial Intelligence (AI) and machine learning (ML) analyze these images, and could aid in early diagnosis and monitoring of eye conditions, such as geographic atrophy.

4. Biomarker Data – Biomarkers, found in blood or tissues, could assist in assessing disease progression and treatment efficacy. They may soon be able to help ophthalmologists tailor treatments to individual patients.

5. Clinical Trial Data – Data from clinical trials provide foundational insights into the safety and effectiveness of new treatments. They contribute to the development of targeted therapies and the identification of patient subgroups likely to benefit from specific interventions.

6. Artificial Intelligence and Machine Learning: AI and ML algorithms can process large datasets to help predict disease progression, identify patterns, and streamline decision-making for ophthalmologists. This is an ongoing and exciting potential area of research.

Advantages of Personalized Medicine

Personalized medicine in ophthalmology offers the following advantages:

• Treatment tailored to the individual – Patients receive treatments that may be more likely to be effective for their specific condition.
• Risk assessment – Early risk assessment and preventive measures for patients with a genetic predisposition to certain eye conditions.
• Minimized side effects – Treatment plans can be personalized to reduce potential side effects, making them more tolerable and effective.
• Enhanced patient engagement – Could help patients to be more actively involved in their healthcare, leading to better adherence to treatment plans and improved outcomes.

Advancing Personalized Medicine

Verana Health (Verana) is well positioned to be a key partner in advancing personalized medicine in ophthalmology. With exclusive partnerships with three leading medical societies, Verana is using advanced AI technology to curate quality insights, called Qdata^®, which can help life sciences companies accelerate research. Additionally, Verana provides clinical trial solutions, spearheaded by VeraSite and Verana Trial Connect, employing methods to help match the right patients with the right trials. 

Researchers have noted that data not gleaned directly from the clinical experience, such as from clinical trials or small registries, may not properly represent real world applications. Therefore, registries built around the entire patient experience, like the ones Verana facilitates, are becoming more widely used. 

Verana continues to research the best ways to utilize all of the above mentioned tools to help support personalized treatment and drug development. One important tool Verana is using to drive more efficient drug development to allow for more personalized care, is the Verana Research Network (VRN). This network  includes thought partners in programs designed to improve patient recruitment, study execution, and closing the diversity gap in ophthalmology care and research.