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联邦医疗保险的女性中，参加联邦医疗保险的人接受了全国大约三分之一的筛查乳房X光检查(每年约1300万次乳房X光检查)。因此，提高医疗保险参与者接受的筛查乳房X光检查的质量仍然是公共卫生的优先事项。2001年，国会将医疗保险覆盖范围扩大到数字乳房X光检查和筛查乳房X光检查中计算机辅助检测(CAD)的应用。对在医疗保险人群中传播这些技术的临床和经济影响进行严格评估具有重大的政策意义。尽管联邦医疗保险声称可能是此类评估的富有成效的数据源，但关键数据元素的有效性仍然存在不确定性。该计划的公告“使用基于健康索赔的数据系统进行癌症监测”，呼吁进行研究，以扩大医疗保险索赔文件的科学效用。作为回应，这项研究将使用新建立联系的乳腺癌监测联盟(BCSC)-联邦医疗保险数据来验证联邦医疗保险乳房X光检查声明的关键数据元素，并评估基于声明的算法的性能，以区分筛查与诊断性乳房X光检查和正常与异常放射科医生的解释。具体目标是：1)改进和验证一种有前景的基于声明的算法，以区分筛查和诊断性乳房X光检查。2)对数字化乳房X光摄影和计算机辅助检测(CAD)的索赔程序代码进行外部验证。3)开发和验证一种基于声明的算法，以区分筛查乳房X光检查是否被解释为正常和异常。分析将展示在乳房X光摄影技术推广的背景下，索赔算法在比较有效性研究或放射科医生级别的质量评估中的潜在效用。 公共卫生相关性：这项研究可能为随后与乳房X光检查相关的基于声明的比较有效性、经济性和质量改进研究提供必要的方法学支持。通过阐明索赔数据的优势和劣势，研究结果还可能指导提高医疗保险索赔数据质量的规划努力。

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.