From real world data to real world evidence to improve outcomes in neuro-ophthalmology.
Blake D ColmanZhuoting ZhuZiyi QiAnneke Van Der WaltPublished in: Eye (London, England) (2024)
Real-world data (RWD) can be defined as all data generated during routine clinical care. This includes electronic health records, disease-specific registries, imaging databanks, and data linkage to administrative databases. In the field of neuro-ophthalmology, the intersection of RWD and clinical practice offers unprecedented opportunities to understand and treat rare diseases. However, translating RWD into real-world evidence (RWE) poses several challenges, including data quality, legal and ethical considerations, and sustainability of data sources. This review explores existing RWD sources in neuro-ophthalmology, such as patient registries and electronic health records, and discusses the challenges of data collection and standardisation. We focus on research questions that need to be answered in neuro-ophthalmology and provide an update on RWE generated from various RWD sources. We review and propose solutions to some of the key barriers that can limit translation of a collection of data into impactful clinical evidence. Careful data selection, management, analysis, and interpretation are critical to generate meaningful conclusions.
Keyphrases
- electronic health record
- big data
- clinical decision support
- clinical practice
- healthcare
- artificial intelligence
- adverse drug
- gene expression
- type diabetes
- machine learning
- high resolution
- data analysis
- case report
- mass spectrometry
- quality improvement
- chronic pain
- cataract surgery
- glycemic control
- health insurance
- hiv testing