title: Quality Measurements in Radiology: A Systematic Review of the Literature and Survey of Radiology Benefit Management Groups.
abstract: OBJECTIVE
  As the US health care system transitions toward value-based reimbursement, there is an increasing need for metrics to quantify health care quality. Within radiology, many quality metrics are in use, and still more have been proposed, but there have been limited attempts to systematically inventory these measures and classify them using a standard framework. The purpose of this study was to develop an exhaustive inventory of public and private sector imaging quality metrics classified according to the classic Donabedian framework (structure, process, and outcome).
  
  
  METHODS
  A systematic review was performed in which eligibility criteria included published articles (from 2000 onward) from multiple databases. Studies were double-read, with discrepancies resolved by consensus. For the radiology benefit management group (RBM) survey, the six known companies nationally were surveyed. Outcome measures were organized on the basis of standard categories (structure, process, and outcome) and reported using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.
  
  
  RESULTS
  The search strategy yielded 1,816 citations; review yielded 110 reports (29 included for final analysis). Three of six RBMs (50%) responded to the survey; the websites of the other RBMs were searched for additional metrics. Seventy-five unique metrics were reported: 35 structure (46%), 20 outcome (27%), and 20 process (27%) metrics. For RBMs, 35 metrics were reported: 27 structure (77%), 4 process (11%), and 4 outcome (11%) metrics. The most commonly cited structure, process, and outcome metrics included ACR accreditation (37%), ACR Appropriateness Criteria (85%), and peer review (95%), respectively.
  
  
  CONCLUSIONS
  Imaging quality metrics are more likely to be structural (46%) than process (27%) or outcome (27%) based (P < .05). As national value-based reimbursement programs increasingly emphasize outcome-based metrics, radiologists must keep pace by developing the data infrastructure required to collect outcome-based quality metrics.
authors.affiliation: Johns Hopkins Hospital, Baltimore, Maryland. Electronic address: anaraya1@jhmi.edu.
authors.forename: Anand
authors.initials: A
authors.lastname: Narayan
authors.affiliation: Brigham and Women's Hospital, Boston, Massachusetts.
authors.forename: Christina
authors.initials: C
authors.lastname: Cinelli
authors.affiliation: Hospital for Special Surgery, New York, New York.
authors.forename: John A
authors.initials: JA
authors.lastname: Carrino
authors.affiliation: Johns Hopkins Hospital, Baltimore, Maryland.
authors.forename: Paul
authors.initials: P
authors.lastname: Nagy
authors.affiliation: Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
authors.forename: Josef
authors.initials: J
authors.lastname: Coresh
authors.affiliation: Johns Hopkins Hospital, Baltimore, Maryland.
authors.forename: Victoria G
authors.initials: VG
authors.lastname: Riese
authors.affiliation: Johns Hopkins Hospital, Baltimore, Maryland.
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Durand
mesh_terms: D003362:Cost-Benefit Analysis; D003952:Diagnostic Imaging / Q000191:economics / Q000592:standards*; D004502:Education, Medical, Continuing; D005260:Female; D006801:Humans; D008297:Male; D017063:Outcome Assessment, Health Care; D011785:Quality Assurance, Health Care; D011871:Radiology / Q000191:economics* / Q000592:standards*; D011795:Surveys and Questionnaires; D014481:United States
keywords: Quality measurements; imaging; measures; metrics; radiology; radiology benefit management groups