ENDEX™
ENDEX™ transforms new and historical medical image data descriptions to a consistent, clinically relevant standard nomenclature that impacts workflows and capacity within and beyond radiology.
- Standardizes and structures data to a defined universal ontology using the power of Natural Language Processing (NLP) and Computer Vision (CV)
- Studies and series now appear with normalized descriptions enabling effective display protocols
- Relevant, uniform descriptions facilitate faster reporting time, easily queried studies, and less frustration from radiologists
- Medical imaging information is enriched with clinical relevance and has become actionable and useful
- PACS Administrators and technologists reclaim time from mundane tasks
ENCOG™
ENCOG™ deidentifies and anonymizes imaging data to remove PHI from pixel data, metadata, and private tags while retaining clinical relevant information.
- It is a data anonymization tool powered by AI that keeps all clinically relevant information while removing and protecting Protected Health Information (PHI), auditing activities, and maintaining a chain of custody.
- ENCOG uses Computer Vision (CV) and Natural Language Processing (NLP) to differentiate PHI from relevant clinical information.
- Supported modalities include MR, CT, XR, and ultrasound.
- ENCOG maintains the historical associations of a patient study by applying a consistent shift to dates, preserves the identifiers that are needed for associating patient data, and defines redaction rules by DICOM attribute or value representation.
- Data can be re-identified using a decrypt key – this can ONLY be done where the deidentification occurred. The decrypt key is owned by the organization, ensuring security is maintained.
ENCODE™
*Future Development*
ENCODE™ leverages the data standardization from ENDEX™ to compare the patient study that was acquired against what was billed for reimbursement.
- ENCODE examines study and series descriptions and compares these against the coding that was assigned to the study to determine if what was acquired is being correctly submitted for reimbursement.
- It can easily compare discrepancies using advanced algorithms to analyze the data.
- The system can flag potential errors for review by a medical coding specialist, who can then investigate and resolve any issues.
ENSIGHT™
*Future Development*
ENSIGHT™ connects healthcare providers medical imaging databases overcoming true interoperability challenges. Patient studies acquired at different facilities can have differentiated study and series descriptions while being acquired using the same parameters. This makes searching for cohorts of patients sharing similar characteristics difficult, if not impossible.
ENSIGHT™ connects healthcare providers medical imaging databases overcoming true interoperability challenges. Patient studies acquired at different facilities can have differentiated study and series descriptions while being acquired using the same parameters. This makes searching for cohorts of patients sharing similar characteristics difficult, if not impossible.
- Connect multiple facilities together allowing them to share insights and create a RWE medical imaging database
- Facilities can identify patterns and trends, support research, optimize operations and realize a data monetization strategy.
