title: Tradeoffs of using administrative claims and medical records to identify the use of personalized medicine for patients with breast cancer.
abstract: BACKGROUND
  Administrative claims and medical records are important data sources to examine healthcare utilization and outcomes. Little is known about identifying personalized medicine technologies in these sources.
  
  
  OBJECTIVE
  To describe agreement, sensitivity, and specificity of administrative claims compared with medical records for 2 pairs of targeted tests and treatments for breast cancer.
  
  
  METHODS
  Retrospective analysis of medical records linked to administrative claims from a large health plan. We examined whether agreement varied by factors that facilitate tracking in claims (coding and cost) and that enhance medical record completeness (records from multiple providers).
  
  
  METHODS
  Women (35 to 65 y of age) with incident breast cancer diagnosed in 2006 to 2007 (n=775).
  
  
  METHODS
  Use of human epidermal growth factor receptor 2 (HER2) and gene expression profiling (GEP) testing, trastuzumab, and adjuvant chemotherapy in claims and medical records.
  
  
  RESULTS
  Agreement between claims and records was substantial for GEP, trastuzumab, and chemotherapy, and lowest for HER2 tests. GEP, an expensive test with unique billing codes, had higher agreement (91.6% vs. 75.2%), sensitivity (94.9% vs. 76.7%), and specificity (90.1% vs. 29.2%) than HER2, a test without unique billing codes. Trastuzumab, a treatment with unique billing codes, had slightly higher agreement (95.1% vs. 90%) and sensitivity (98.1% vs. 87.9%) than adjuvant chemotherapy.
  
  
  CONCLUSIONS
  Higher agreement and specificity were associated with services that had unique billing codes and high cost. Administrative claims may be sufficient for examining services with unique billing codes. Medical records provide better data for identifying tests lacking specific codes and for research requiring detailed clinical information.
authors.affiliation: Department of Clinical Pharmacy, School of Pharmacy, University of California, San Francisco, CA 94143, USA. liangs@pharmacy.ucsf.edu
authors.forename: Su-Ying
authors.initials: SY
authors.lastname: Liang
authors.forename: Kathryn A
authors.initials: KA
authors.lastname: Phillips
authors.forename: Grace
authors.initials: G
authors.lastname: Wang
authors.forename: Carol
authors.initials: C
authors.lastname: Keohane
authors.forename: Joanne
authors.initials: J
authors.lastname: Armstrong
authors.forename: William M
authors.initials: WM
authors.lastname: Morris
authors.forename: Jennifer S
authors.initials: JS
authors.lastname: Haas
mesh_terms: D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000191:economics / Q000453:epidemiology* / Q000628:therapy*; D059019:Clinical Coding / Q000191:economics / Q000706:statistics & numerical data*; D005260:Female; D017048:Health Care Costs / Q000706:statistics & numerical data*; D006801:Humans; D007344:Insurance Claim Reporting / Q000191:economics / Q000706:statistics & numerical data*; D008498:Medical Record Linkage / Q000379:methods*; D008875:Middle Aged; D057285:Precision Medicine / Q000191:economics / Q000706:statistics & numerical data*