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Use of Image-Based Analytics for Ultrasound Practice Management and Efficiency Improvement

  • Scott F. StekelEmail author
  • Zaiyang Long
  • Donald J. Tradup
  • Nicholas J. Hangiandreou
Article
  • 20 Downloads

Abstract

Our ultrasound practice is becoming even more focused on managing practice resources and improving our efficiency while maintaining practice quality. We often encounter questions related to issues such as equipment utilization and management, study type statistics, and productivity. We are developing an analytics system to allow more evidence-based management of our ultrasound practice. Our system collects information from tens of thousands of DICOM images produced during exams, including structured reporting, public and private DICOM headers, and text within the images via optical character recognition (OCR). Inventory/location information augments the data aggregation, and statistical analysis and metrics are computed such as median exam length (time from the first image to last), transducer models used in an exam, and exams performed in a particular room, practice location, or by a given sonographer. Additional reports detail the length of a scan room’s operational day, the number and type of exams performed, the time between exams, and summary data such as exams per operational hour and time-based room utilization. Our findings have already helped guide practice decisions: two defective probes were not replaced (a savings of over $10,000) when utilization data showed that three or more of the shared probe model were always idle; neck exams are the most time-consuming individually, but abdomen exam volumes cause them to consume the most total scan time, making abdominal exams the better candidates for efficiency optimization efforts. A small subset of sonographers exhibit the greatest scanning and between-scan efficiency, making them good candidates for identifying best practices.

Keywords

Image-based analytics Ultrasound practice management OCR 

Notes

Copyright information

© Society for Imaging Informatics in Medicine 2019

Authors and Affiliations

  1. 1.Mayo ClinicRochesterUSA

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