Insights on Research Data Management

Analyzing large volumes of imaging data can help ophthalmology researchers see patterns in disease progression, develop new treatments and improve patient care. But achieving efficient ophthalmology data management is another story. 

To improve utilization of ophthalmology imaging data in research and patient care, you need ways to store and broadly access that data while standardizing it and maintaining security and compliance. Read on to learn our tips on achieving better ophthalmology imaging data management. 

Centralize your imaging data 

Your organization may already be using a picture archival application to collect its ophthalmology imaging data, providing researchers and clinicians with more complete digital data from multiple diagnostic devices in one place. However, these formats and systems may not be interoperable, making it difficult to share imaging data between departments, hospitals and caregivers in a simple way, leading to siloed data that can’t be easily accessed or shared. 

These closed systems often lock users into vendor-specific workflows, making it difficult to integrate with other platforms or analyze data across different sources. It also may increase manual effort, slow down research and analytical workflows, and can result in inconsistent data formats, ultimately hindering AI development, multi-site studies and large-scale analytics. 

Using a centralized medical imaging platform can help researchers and clinicians share data in a less complex way. This leads to greater access to more data, which, in turn, can lead to the ability to develop new diagnostic tools and therapies faster and at less cost. 

Prioritize data security and compliance 

With increased access comes a greater chance of data breaches. So, you need any system you use to be not only secure, but also compliant with regulations such as HIPAA and GDPR that must be followed in ophthalmology care and research, as well as 21 CFR Part 11, in case of an audit. 

Whatever system you’re using, it should be able to de-identify health records to comply with HIPAA and GDPR and have security features such as role-based access controls to protect against data breaches that can violate these regulations. In addition, your system should be able to keep records of what changes are made to data, when they were made and who made them, in case of an FDA audit. 

Make it scalable 

To make meaningful advances in patient care, you need data—lots of it. Clinical studies that use a large volume of data from disparate populations tend to be more accurate, and their results can be applied more broadly. And because you can measure many ophthalmic conditions over time with fast imaging modalities, ophthalmology, in particular, is benefiting from an increase of usable data. 

However, using larger imaging datasets with more advanced imaging technologies makes data management tricky. Add in traditional radiology data, which can be used in conjunction with ophthalmology data for studies that involve multiple therapeutic areas, and it gets even more complicated. A scalable, cloud-based solution can help you maintain efficiency while incorporating more data from across the enterprise as well as data lakes so you can uncover more impactful results and future-proof your data management for ongoing research. 

Ensure data standardization 

Ophthalmology imaging data standards may vary greatly, using diverse imaging formats that include OCT, FDA, FDS and E2E. This makes sharing across departments and institutions difficult, as data may be stored in different locations, formats and at different levels of granularity.  

One way to solve this? A platform that connects to multiple systems and has the power to convert various ophthalmic formats to DICOM and back again, as well as conduct quality assurance on that data. This helps researchers harmonize data and develop reader studies to establish ground truth data so they can conduct large-scale analyses while implementing the necessary quality control measures. 

Leverage AI and automation  

Using AI and machine learning can help organizations tag, organize and analyze medical imaging data faster and more efficiently. Automation can streamline routine tasks such as data classification and retrieval so radiologists and other healthcare professionals can spend more time on analysis. 

However, it’s difficult to adequately prepare data for AI development and to deploy the right algorithms on your data. Without a proper system to implement AI, you could end up wasting more time and money than you would have without utilizing these technologies to begin with. 

How Flywheel can help 

Flywheel's unified imaging platform can help ophthalmologists and researchers get more from their imaging data with features tailor-made for ophthalmology. Flywheel centralizes storage from multiple locations and systems into one platform, with robust security and compliance features and advanced AI capabilities.  

In addition, Flywheel features: 

• A built-in viewer with ophthalmology-specific tooling, such as an interactive ETDRS grid and stereo fundus hanging protocols to accelerate image review and annotation 
• The ability to convert diverse formats, such as OCT, FDA, FDS and E2E, to DICOM 
• Ready-to-use ophthalmology algorithms and the ability to upload custom algorithms 
• Configurable security capabilities, including de-identification, role-based access control, the ability to create fully provenanced data sets and more 

Learn more about how Flywheel empowers better ophthalmology research and care. And get in touch to start the journey toward more efficient, secure and scalable ophthalmology imaging data management.