William L. Rich

Use of Health Care Claims Data to Study Patients with Ophthalmologic Conditions

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. DESIGN Systematic review. PARTICIPANTS Not applicable. METHODS A literature review and synthesis of methods for claims-based data analyses. MAIN OUTCOME MEASURES 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.

The American Academy of Ophthalmology's IRIS® Registry (Intelligent Research in Sight Clinical Data): A Look Back and a Look to the Future

The seeds for the Academy’s IRIS Registry (Intelligent Research in Sight) were planted more than 3 decades ago with the initiation of the Academy’s Quality of Care Committee in 1985. This blossomed into one of the first medical society practice guidelines for improving care and promoting best practices based on expert consensus and scientific evidence from the peer-reviewed literature, known as the Preferred Practice Patterns. The committee then turned its focus on patient outcomes, with the launch of a National Eyecare Outcomes Network for measuring cataract surgery performance in 1995. At its peak, National Eyecare Outcomes Network collected data on 17 000 patients’ preoperative characteristics, operative parameters, and postoperative outcomes, but subsequently met its demise because the time