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Enhancement of Identifying Cancer Specialists through the Linkage of Medicare Claims to Additional Sources of Physician Specialty
Author(s) -
Pollack Lori A.,
Adamache Walter,
Eheman Christie R.,
Ryerson A. Blythe,
Richardson Lisa C.
Publication year - 2009
Publication title -
health services research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 121
eISSN - 1475-6773
pISSN - 0017-9124
DOI - 10.1111/j.1475-6773.2008.00935.x
Subject(s) - medicine , specialty , subspecialty , identification (biology) , family medicine , cancer registry , linkage (software) , medline , medicare part d , population , prescription drug , environmental health , nursing , medical prescription , political science , law , biochemistry , chemistry , botany , gene , biology
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.