title: Data-driven CT protocol review and management—experience from a large academic hospital.
abstract: OBJECTIVE
  Protocol review plays a critical role in CT quality assurance, but large numbers of protocols and inconsistent protocol names on scanners and in exam records make thorough protocol review formidable. In this investigation, we report on a data-driven cataloging process that can be used to assist in the reviewing and management of CT protocols.
  
  
  METHODS
  We collected lists of scanner protocols, as well as 18 months of recent exam records, for 10 clinical scanners. We developed computer algorithms to automatically deconstruct the protocol names on the scanner and in the exam records into core names and descriptive components. Based on the core names, we were able to group the scanner protocols into a much smaller set of "core protocols," and to easily link exam records with the scanner protocols. We calculated the percentage of usage for each core protocol, from which the most heavily used protocols were identified.
  
  
  RESULTS
  From the percentage-of-usage data, we found that, on average, 18, 33, and 49 core protocols per scanner covered 80%, 90%, and 95%, respectively, of all exams. These numbers are one order of magnitude smaller than the typical numbers of protocols that are loaded on a scanner (200-300, as reported in the literature). Duplicated, outdated, and rarely used protocols on the scanners were easily pinpointed in the cataloging process.
  
  
  CONCLUSIONS
  The data-driven cataloging process can facilitate the task of protocol review.
authors.affiliation: Division of Diagnostic Imaging Physics and Webster Center for Advanced Research and Education in Radiation, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
authors.forename: Da
authors.initials: D
authors.lastname: Zhang
authors.affiliation: Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
authors.forename: Cristy A
authors.initials: CA
authors.lastname: Savage
authors.affiliation: Division of Diagnostic Imaging Physics and Webster Center for Advanced Research and Education in Radiation, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
authors.forename: Xinhua
authors.initials: X
authors.lastname: Li
authors.affiliation: Division of Diagnostic Imaging Physics and Webster Center for Advanced Research and Education in Radiation, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts. Electronic address: bliu7@bics.bwh.harvard.edu.
authors.forename: Bob
authors.initials: B
authors.lastname: Liu
mesh_terms: D000046:Academic Medical Centers / Q000592:standards / Q000706:statistics & numerical data*; D002985:Clinical Protocols / Q000592:standards*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D019983:Guideline Adherence / Q000706:statistics & numerical data; D017408:Guidelines as Topic; D008404:Massachusetts; D014057:Tomography, X-Ray Computed / Q000592:standards* / Q000706:statistics & numerical data*
keywords: CT protocol review; automation; informatics; text matching