Validation of Medicare Claims Data for Mammography

描述（由申请人提供）：超过一半的乳腺癌发生在参加医疗保险的妇女中，参加医疗保险的妇女接受了全国约三分之一的筛查乳房x光检查（每年约1300万次）。因此，提高医疗保险参保人接受乳房x光检查的质量仍然是公共卫生的优先事项。2001年，国会将医疗保险的覆盖范围扩大到数字乳房x线照相术和计算机辅助检测（CAD）在乳房x线照相术筛查中的应用。严格评估在医疗保险人群中传播这些技术的临床和经济影响具有重大的政策意义。尽管医疗保险索赔可能是此类评估的有效数据源，但关键数据元素的有效性仍然存在不确定性。项目公告“使用基于健康索赔的数据系统进行癌症监测”，呼吁研究扩大医疗保险索赔文件的科学效用。作为回应，本研究将使用新链接的乳腺癌监测联盟(BCSC)-医疗保险数据来验证医疗保险乳房x光检查索赔的关键数据元素，并评估基于索赔的算法的性能，以区分筛查与诊断性乳房x光检查以及正常与异常放射科医生的解释。具体目标是：1)完善和验证一种有前途的基于索赔的算法，以区分筛查和诊断性乳房x光检查。2)外部验证数字乳房x线摄影和计算机辅助检测（CAD）的索赔程序代码。3)开发并验证基于索赔的算法，以区分筛查乳房x线照片是否被解释为正常或异常。分析将证明在乳房x线摄影技术扩散的背景下，索赔算法在比较有效性研究或放射科医生水平质量评估方面的潜在效用。

DESCRIPTION (provided by applicant): Over one-half of incident breast cancers occur among Medicare-enrolled women, and Medicare enrollees receive approximately one-third of all screening mammograms nationwide (~13 million annual mammograms). Thus, improving the quality of screening mammography received by Medicare enrollees remains a public health priority. In 2001, Congress extended Medicare coverage to digital mammography and the application of computer-aided detection (CAD) during screening mammography. Rigorous evaluation of the clinical and economic impact of dissemination these technologies within the Medicare population have substantial policy significance. Although Medicare claims could be fruitful data source for such evaluations, uncertainty remains about the validity of key data elements. The Program Announcement, "Cancer Surveillance Using Health Claims-based Data System," calls for research to expand the scientific utility of Medicare claims files. In response, this study will use the newly linked Breast Cancer Surveillance Consortium (BCSC)-Medicare data to validate critical data elements of Medicare mammography claims and to evaluate the performance of claims-based algorithms for distinguishing screening from diagnostic mammograms and normal from abnormal radiologist interpretations. Specific aims are: 1) To refine and validate a promising claims-based algorithm to distinguish screening from diagnostic mammograms. 2) To externally validate claims procedure codes for digital mammography and computer-aided detection (CAD). 3) To develop and validate a claims-based algorithm to distinguish whether screening mammograms were interpreted as normal vs. abnormal. Analyses will demonstrate the potential utility of the claims algorithms for comparative effectiveness research or radiologist-level quality assessment in the context of mammography technology diffusion. PUBLIC HEALTH RELEVANCE: This study may provide essential methodological support for subsequent claims-based comparative effectiveness, economic, quality improvement research related to mammography. By elucidating the strengths and weaknesses of the claims data, study findings may also direct programmatic efforts to improve the quality of Medicare claims data.