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IT & PACS Administrators Gain Capacity With AI Powered Tools

Healthcare Data Solutions

Your PACS can only work with the data it receives. Too often the inputs are inconsistent, inaccurate or missing and this impacts the outputs. Even more often administrators are called upon to fix mundane data issues that could be easily resolved with some level of data governance. But even then, subtle changes in modality settings, quality control updates or technologist changes can impact the outputs.

You are in charge of creating a database to send to a research organization that purchased the data from you, but with all the clinical relevance removed from the anonymization tool, you are struggling to get the studies relabeled. With Curie|ENCOG™, the study metadata and pixel data get anonymized and the clinically relevant data remains in tact. If you are wanting to monetize your data, you need ENCOG.

It’s 7:30 AM and you get called to fix a hanging protocol before your morning coffee. The label was slightly different than any of the mapping already set up. With Curie|ENDEX™, study descriptions and labels are standardized and enhanced with clinical relevancy allowing for proper study routing and consistent hanging protocols. If you have high value tasks that keep getting pushed to fix study information, you need ENDEX.

You have been notified that a study was supposed to be received by a radiologist, but it is taking a long time to get there. The system is getting bogged down. You need to figure out where the study is and make sure it gets to the worklist. Curie|ENDEX™ standardizes and enhanced study information and sends only the series the radiologist needs to their worklist the first time. The network doesn’t get bogged down. If you have experienced this. you need ENDEX.

healthcare data management solutions

Healthcare Data Management Solutions

While building a database containing real-world evidence, it must be both standardized and searchable, but also must have personal health information (PHI) completely removed.

  • Anonymize and protect PHI in medical imaging data
  • Data is cleaned of all PHI contained in the metadata, pixel data, and tags
  • High value, clinical information remains in tact
  • Get a complete audit trail and validations

How Enlitic Curie Helps Healthcare IT Departments

Enlitic has created a solution to this problem by standardizing data from medical images. This creates the base of normalized data to feed into other technologies.

  • The healthcare IT department can now better use their resources, starting with the PACS Administrators time. These folks spend a lot of time fixing data entry errors, re-entering patient data, manually routing images, etc.
  • Enlitic also helps with network capacity issues by minimizing the need to re-route these massive images. Because of incorrect or missing data, images often get sent to the wrong workstation or radiologist worklist, consuming valuable network bandwidth.
  • The Curie framework reduces costs by creating efficiencies, reducing risk by de-identifying data while leaving clinical information, and helps the organization to use healthcare data to help deliver better patient care.
AI in Radiology - solutions

Data Standardization in Healthcare

When mapping series descriptions, there may be numerous ways to name a study or series. Curie|ENDEX analyzes the medical image and its metadata to consolidate all of these different descriptions into a clinically relevant, useful description. Before implementation of ENDEX, we found that 47% of medical images have no relevant information associated with them. This problem impacts everyone from the PACS admin, the radiologists to the Billing and Coding department.

Healthcare data standardization can ensure that the relevant studies are routed to the correct worklist, AI point solution algorithm, or other software. By using the clinically relevant study and series descriptions generated by ENDEX, the correct series can be routed to the AI algorithm necessary to help diagnose the issue.

The same data standardization can also ensure that the original study and AI results arrive at the appropriate radiologist worklist eliminating the interruptions of needing to reroute the data to the correct worklist. Too many series are poorly labeled that it is difficult to determine what body part it is, making data routing near impossible without intervention.