Comparing patient outcomes across payer types: implications for using hospital discharge records to assess quality

Health Serv Res. 2011 Dec;46(6pt1):1720-40. doi: 10.1111/j.1475-6773.2011.01285.x. Epub 2011 Jun 20.


Objective: To explain observed differences in patient outcomes across payer types using hospital discharge records. Specifically, we address two mechanisms: hospital-payer matching versus unobserved patient heterogeneity.

Data source: Florida's hospital discharge records (1996-2000) of major surgery patients with private health insurance between the ages of 18 and 65, Health Maintenance Organization (HMO) market penetration data, hospital systems data, and the Area Resource File.

Study design: The dependent variable is occurrence of one or more in-hospital complications as identified by the Complication Screening Program. The key independent variable is patients' primary-payer type (HMO, Preferred Provider Organization, and fee-for-service). We estimate five different logistic regression models, each representing a different assumption about the underlying factors that confound the causal relationship between the payer type and the likelihood of experiencing complications.

Principal finding: We find that the observed differences in complication rates across payer types are largely driven by unobserved differences in patient health, even after adjusting for case mix using available data elements in the discharge records.

Conclusion: Because of the limitations inherent to hospital discharge records, making quality comparisons in terms of patient outcomes is challenging. As such, any efforts to assess quality in such a manner must be carried out cautiously.

MeSH terms

  • Economic Competition
  • Florida
  • Health Maintenance Organizations / statistics & numerical data
  • Health Services Research
  • Hospital Records / statistics & numerical data*
  • Humans
  • Insurance, Health / statistics & numerical data*
  • Logistic Models
  • Patient Discharge / statistics & numerical data*
  • Patient Safety / statistics & numerical data
  • Preferred Provider Organizations / statistics & numerical data
  • Quality Improvement / statistics & numerical data
  • Quality Indicators, Health Care / statistics & numerical data
  • Quality of Health Care / statistics & numerical data*
  • Risk Adjustment
  • Treatment Outcome*