title: Comparison of administrative data and medical records to measure the quality of medical care provided to vulnerable older patients.
abstract: BACKGROUND
  Administrative data are used to determine performance for publicly reported in health plan "report cards," accreditation status, and reimbursement. However, it is unclear how performance based on administrative data and medical records compare.
  
  
  METHODS
  We compared applicability, eligibility, and performance on 182 measures of health care quality using medical records and administrative data during a 13-month period for a random sample of 399 vulnerable older patients enrolled in managed care.
  
  
  RESULTS
  Of 182 quality indicators (QIs) spanning 22 conditions, 145 (80%) were applicable only to medical records and 37 (20%) to either medical records or administrative data. Among 48 QIs specific to geriatric conditions, all were applicable to medical records; 2 of these also were applicable to administrative data. Eligibility for the 37 QIs that were applicable to both medical records and administrative data was similar for both data sources (94% agreement, kappa = 0.74). With the use of medical records, 152 of the 182 the QIs that were applicable to medical records were triggered and yielded an overall performance of 55%. Using administrative data, 30 of the 37 QIs that were applicable to administrative data were triggered and yielded overall performance of 83% (P < 0.05 vs. medical records). Restricting to QIs applicable to both data sources, overall performance was 84% and 83% (P = 0.21) for medical records and administrative data, respectively.
  
  
  CONCLUSIONS
  The number and spectrum of QIs that can be measured for vulnerable elderly patients is far greater for medical records than for administrative data. Although summary estimates of health care quality derived from administrative data and medical records do not differ when using identical measures, summary scores from these data sources vary substantially when the totality of care that can be measured by each data source is measured.
authors.affiliation: Greater Los Angeles VA Healthcare System, California, USA. maclean@rand.org
authors.forename: Catherine H
authors.initials: CH
authors.lastname: MacLean
authors.forename: Rachel
authors.initials: R
authors.lastname: Louie
authors.forename: Paul G
authors.initials: PG
authors.lastname: Shekelle
authors.forename: Carol P
authors.initials: CP
authors.lastname: Roth
authors.forename: Debra
authors.initials: D
authors.lastname: Saliba
authors.forename: Takahiro
authors.initials: T
authors.lastname: Higashi
authors.forename: John
authors.initials: J
authors.lastname: Adams
authors.forename: John T
authors.initials: JT
authors.lastname: Chang
authors.forename: Caren J
authors.initials: CJ
authors.lastname: Kamberg
authors.forename: David H
authors.initials: DH
authors.lastname: Solomon
authors.forename: Roy T
authors.initials: RT
authors.lastname: Young
authors.forename: Neil S
authors.initials: NS
authors.lastname: Wenger
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D008297:Male; D008329:Managed Care Programs / Q000706:statistics & numerical data*; D008499:Medical Records / Q000706:statistics & numerical data*; D019984:Quality Indicators, Health Care / Q000706:statistics & numerical data*; D011787:Quality of Health Care / Q000458:organization & administration / Q000706:statistics & numerical data