Exclusive Coverage of the UME Patient Population
Confirmed Diagnosis of UME Patients
Detailed Understanding of Treatments and Outcomes
Only found here from the IRIS Registry (Intelligent Research in Sight)
Using ICD-10 codes and clinical notes
Including key variables such as VA, CST, and IOP
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Key Variables in UME Research
Qdata UME offers insights into the entire patient journey, from diagnosis to visual acuity and more. Verana Health provides UME real-world data with curated variables, which are extracted from EHR data, to be utilized based on your research needs. These key variables include:
Visual Acuity
Measures how well the eye can distinguish objects and shapes from a distance
Central Subfield Thickness
Measurement of the thickness of the central part of the retina, typically determined using optical coherence tomographic imaging
Intraocular Pressure
The pressure inside the eye. An increase in IOP is a common adverse effect of UME steroid treatment
Unstructured UME Real-World Data With Patient Outcomes
Real-world evidence through analysis of quality uveitic macular edema real-world data can help advance treatments for this condition. Artificial intelligence (AI) techniques, such as machine learning and natural language processing, enable Verana Health to identify UME patients based on data within the EHR clinical notes. These techniques are also applied to extract key UME patient outcomes, such as visual acuity and central subfield thickness, as well as intraocular pressure, which can be a common side effect of UME steroid medication. These variables are key in furthering UME research.
Qdata UME can provide insights to help support clinical development teams looking to identify UME patients for their next study, HEOR and medical affairs teams looking to understand real-world therapy utilization or commercialization teams looking to gain market insights on their UME therapies.
0%
of Medical Data Remains Unstructured
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