title: Use of health care claims data to study patients with ophthalmologic conditions.
abstract: OBJECTIVE
  To describe what information is or is not included in health care claims data, provide an overview of the main advantages and limitations of performing analyses using health care claims data, and offer general guidance on how to report and interpret findings of ophthalmology-related claims data analyses.
  
  
  METHODS
  Systematic review.
  
  
  METHODS
  Not applicable.
  
  
  METHODS
  A literature review and synthesis of methods for claims-based data analyses.
  
  
  METHODS
  Not applicable.
  
  
  RESULTS
  Some advantages of using claims data for analyses include large, diverse sample sizes, longitudinal follow-up, lack of selection bias, and potential for complex, multivariable modeling. The disadvantages include (a) the inherent limitations of claims data, such as incomplete, inaccurate, or missing data, or the lack of specific billing codes for some conditions; and (b) the inability, in some circumstances, to adequately evaluate the appropriateness of care. In general, reports of claims data analyses should include clear descriptions of the following methodological elements: the data source, the inclusion and exclusion criteria, the specific billing codes used, and the potential confounding factors incorporated in the multivariable models.
  
  
  CONCLUSIONS
  The use of claims data for research is expected to increase with the enhanced availability of data from Medicare and other sources. The use of claims data to evaluate resource use and efficiency and to determine the basis for supplementary payment methods for physicians is anticipated. Thus, it will be increasingly important for eye care providers to use accurate and descriptive codes for billing. Adherence to general guidance on the reporting of claims data analyses, as outlined in this article, is important to enhance the credibility and applicability of findings. Guidance on optimal ways to conduct and report ophthalmology-related investigations using claims data will likely continue to evolve as health services researchers refine the metrics to analyze large administrative data sets.
authors.affiliation: W. K. Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan.
authors.forename: Joshua D
authors.initials: JD
authors.lastname: Stein
authors.affiliation: H. Dunbar Hoskins, Jr., M.D. Center for Quality Eye Care, American Academy of Ophthalmology, San Francisco, California. Electronic address: flum@aao.org.
authors.forename: Flora
authors.initials: F
authors.lastname: Lum
authors.affiliation: W. K. Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan.
authors.forename: Paul P
authors.initials: PP
authors.lastname: Lee
authors.affiliation: Northern Virginia Ophthalmology Associates, Falls Church, Virginia.
authors.forename: William L
authors.initials: WL
authors.lastname: Rich
authors.affiliation: H. Dunbar Hoskins, Jr., M.D. Center for Quality Eye Care, American Academy of Ophthalmology, San Francisco, California; Jules Stein Eye Institute, David Geffen School of Medicine and Jonathan and Karen Fielding School of Public Health, University of California at Los Angeles, Los Angeles, California.
authors.forename: Anne L
authors.initials: AL
authors.lastname: Coleman
mesh_terms: D003625:Data Collection / Q000379:methods; D003695:Delivery of Health Care; D005128:Eye Diseases / Q000453:epidemiology / Q000517:prevention & control*; D006295:Health Resources / Q000706:statistics & numerical data*; D006302:Health Services Research; D006801:Humans; D007345:Insurance Claim Review; D006278:Medicare; D009885:Ophthalmology / Q000706:statistics & numerical data*; D014481:United States