title: Do health plans influence quality of care?
abstract: OBJECTIVE
  To investigate the relative impact of physician groups and health plans on quality of care measures.
  
  
  METHODS
  Secondary data analysis of receipt of preventive care services included in the Health Plan Employer Data and Information Set (HEDIS) among 10 758 patients representing 21 health maintenance organizations and 22 large provider groups in the San Francisco and Los Angeles, California, areas in 1997. Each patient was eligible for (at least) one of six HEDIS-measured services. Data identify whether or not the service was provided, the patient's health plan, and the provider group responsible for the care. We used logistic regression to examine variations across plans in HEDIS rates, and whether variations persist after controls for provider groups are included.
  
  
  METHODS
  Patients from 21 health maintenance organizations serving San Francisco and Los Angeles, California, in 1997.
  
  
  METHODS
  Breast cancer screening, childhood immunizations, cervical cancer screening, diabetic retinal exam, prenatal care in the first trimester, and check-ups after delivery among patients for whom these services are appropriate.
  
  
  RESULTS
  There are statistically significant differences across health plans in utilization rates for the six services examined. These differences are not substantially affected when we control for the provider group that cared for the patient. That is, controlling for provider group does not explain variations across plans, consistent with the view that health plans have an impact on HEDIS quality measures independent of the providers that they contract with.
  
  
  CONCLUSIONS
  There are activities that plans can undertake which influence their HEDIS scores. On the face of it, these results suggest that plans can independently improve quality, in contrast to hypotheses that plans would be "too far" from patients to have an influence. Continued attention to collecting plan-level data is warranted. Further work should address other possible sources of variations in HEDIS scores, such as variability in the quality of plan administrative databases.
authors.affiliation: Department of Health Research and Policy, Stanford University, Stanford, CA 94305-5405, USA. laurence.baker@stanford.edu
authors.forename: Laurence C
authors.initials: LC
authors.lastname: Baker
authors.forename: David
authors.initials: D
authors.lastname: Hopkins
authors.forename: Richard
authors.initials: R
authors.lastname: Dixon
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Rideout
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Geppert
mesh_terms: D002140:California; D016009:Chi-Square Distribution; D016208:Databases, Factual; D006264:Health Benefit Plans, Employee / Q000592:standards*; D006279:Health Maintenance Organizations / Q000592:standards*; D006801:Humans; D016015:Logistic Models; D011314:Preventive Health Services / Q000592:standards*; D011785:Quality Assurance, Health Care; D019984:Quality Indicators, Health Care; D014481:United States