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3D Printing Healthcare Facility Data Management: Learning Lessons on Structure, Ontology, and Utilization

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Conference Abstract: Data management within a healthcare facility’s (HCF’s) 3D printing lab has largely been accomplished through the bespoke implementation of picture archiving and communication systems (PACS), segmentation software, CAD software, pre-print software, and more. The result of this workflow is a series of 3D datasets (often STLs or OBJs) and a glut of linked datasets managed through ad hoc databases- often just a spreadsheet! This bespoke process, while clearly functional, does not leverage existing infrastructure hospitals use to safely and consistently store medical images while ensuring clear linking of images to patient records (e.g., DICOM) nor are these infrastructures scalable to support quality improvement initiatives, research, or registries (e.g., American College of Radiology’s 3D Printing Registry. We present learning lessons from our HCF regarding data management inclusive of structure, tracking, DICOM interaction, registry participation, and more. The lessons discussed are agnostic to a specific platform/software and can help hospitals build quality improvement and research opportunities.

HCFs have often reported using network-attached storage, local storage, or cloud storage to archive 3D reconstructions and 3D printing data. The process side-steps the well-adopted imaging database standards (PACS/VNA) common in hospitals. Leveraging these imaging databases will help to properly archive the 3D data for future querying/retrieving. Our experience developing a DICOM-capable encapsulation software as well as a bespoke relational database for 3D model production can benefit healthcare 3D printing communication by discussing the lessons learned.