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.
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. With the use of the Enlitic Curie platform and ENDEX, PACS Administrators can now be working more on technology deployment, updates and other important duties.
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. Enlitic 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.
When mapping series descriptions, there may be numerous ways 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 AI point solution algorithm. 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 AI results and the original study 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.