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通过将医疗保险理赔数据与医生专业的其他来源相联系来加强对癌症专科医生的识别。

Enhancement of identifying cancer specialists through the linkage of Medicare claims to additional sources of physician specialty.

作者信息

Pollack Lori A, Adamache Walter, Eheman Christie R, Ryerson A Blythe, Richardson Lisa C

机构信息

Epidemiology and Applied Research Branch, Division of Cancer Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA 30341-3717, USA.

出版信息

Health Serv Res. 2009 Apr;44(2 Pt 1):562-76. doi: 10.1111/j.1475-6773.2008.00935.x. Epub 2008 Dec 15.

DOI:10.1111/j.1475-6773.2008.00935.x
PMID:19207588
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2677054/
Abstract

OBJECTIVE

To examine the number of cancer specialists identified in three national datasets, the effect of combining these datasets, and the use of refinement rules to classify physicians as cancer specialists.

DATA SOURCES

1992-2003 linked Surveillance, Epidemiology, and End Results (SEER)-Medicare data and a cancer-free comparison population of Medicare beneficiaries, Unique Physician Identification Number (UPIN) Registry, and the American Medical Association (AMA) Masterfile.

STUDY DESIGN

We compared differences in counts of cancer specialists identified in Medicare claims only with the number obtained by combining data sources and after using rules to refine specialty identification.

DATA EXTRACTION

We analyzed physician specialty variables provided on Medicare claims, along with the specialties obtained by linkage of unencrypted UPINs on Medicare claims to the UPIN Registry, the AMA Masterfile, and all sources combined.

PRINCIPLE FINDINGS

Medicare claims identified the fewest number of cancer specialists (n=11,721) compared with 19,753 who were identified when we combined all three datasets. The percentage increase identified by combining datasets varied by subspecialty (187 percent for surgical oncologists to 50 percent for radiation oncologists). Rules created to refine identification most affected the count of radiation oncologists.

CONCLUSIONS

Researchers should consider taking the additional effort and cost to refine classification by using additional data sources based on their study objectives.

摘要

目的

研究三个全国性数据集中所确定的癌症专科医生数量、合并这些数据集的效果以及使用细化规则将医生分类为癌症专科医生的情况。

数据来源

1992 - 2003年链接的监测、流行病学和最终结果(SEER)-医疗保险数据以及一组无癌症的医疗保险受益人群作为对照、唯一医生识别号码(UPIN)登记处和美国医学协会(AMA)主文件。

研究设计

我们比较了仅在医疗保险理赔记录中确定的癌症专科医生数量与通过合并数据源以及使用规则细化专科识别后获得的数量之间的差异。

数据提取

我们分析了医疗保险理赔记录中提供的医生专科变量,以及通过将医疗保险理赔记录上未加密的UPIN与UPIN登记处、AMA主文件以及所有来源合并后获得的专科信息。

主要发现

与合并所有三个数据集后确定的19753名癌症专科医生相比,医疗保险理赔记录确定的癌症专科医生数量最少(n = 11721)。合并数据集后确定的数量增加百分比因亚专科而异(外科肿瘤学家为187%,放射肿瘤学家为50%)。为细化识别而创建的规则对放射肿瘤学家的数量影响最大。

结论

研究人员应根据其研究目标,考虑付出额外的努力和成本,通过使用额外的数据源来细化分类。

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