Addressing the Critical Challenge
One crucial yet unaddressed need is medical vocabulary standardization, vital for improving interoperability, patient safety, and clinical outcomes. While technical interoperability has been addressed through standards like DICOM, HL7, and FHIR, gaining insights from patient studies still falls short due to inconsistent data governance strategies in radiology studies. Different labels for similar exams make it challenging to search for similar cohorts, posing a dilemma for healthcare providers.
To make sense of the vast amounts of healthcare data generated daily, innovative analytical tools like artificial intelligence (AI) and machine learning (ML) are essential. These tools help healthcare organizations understand patient needs, identify patterns and trends, and develop personalized and effective treatments. Real-world evidence (RWE) frameworks, unlike traditional clinical trials conducted in controlled settings, offer insights into the effectiveness and safety of drugs and medical devices in real-world scenarios, reflecting diverse patient populations and clinical settings.
Enlitic's technology utilizes computer vision and natural language processing to analyze DICOM images, identifying various parameters like body parts, orientation, contrast, and slice thickness for CT, MR, and X-ray images. Additionally, the technology uses pixel data, metadata, and DICOM header tags to identify and protect Protected Health Information (PHI).
If you cannot easily control the way the studies and series descriptions are reaching you, then you need something like ENDEX™. Because otherwise, how do you expect the display protocols to work?
- Ernest Montañà, TMC