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Talking Quality : Guidance for sponsors of consumer reports on health care quality
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Guidance for sponsors of consumer reports on health care quality

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Types of Quality Measures

Measures used to assess and compare the quality of health care organizations are classified as either a structure, process, or outcome measure. Known as the Donabedian model, this classification system was named after the physician and researcher who formulated it.

Structural Measures

Structural measures give consumers a sense of a health care provider’s capacity, systems, and processes to provide high-quality care. For example:

  • Whether the health care organization uses electronic medical records or medication order entry systems.
  • The number or proportion of board-certified physicians.
  • The ratio of providers to patients.

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Process Measures

Process measures indicate what a provider does to maintain or improve health, either for healthy people or for those diagnosed with a health care condition. These measures typically reflect generally accepted recommendations for clinical practice. For example:

  • The percentage of people receiving preventive services (such as mammograms or immunizations).
  • The percentage of people with diabetes who had their blood sugar tested and controlled.

Process measures can inform consumers about medical care they may expect to receive for a given condition or disease, and can contribute toward improving health outcomes. The majority of health care quality measures used for public reporting are process measures.

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Outcome Measures

Outcome measures reflect the impact of the health care service or intervention on the health status of patients. For example:

  • The percentage of patients who died as a result of surgery (surgical mortality rates).
  • The rate of surgical complications or hospital-acquired infections.

Outcome measures may seem to represent the “gold standard” in measuring quality, but an outcome is the result of numerous factors, many beyond providers’ control. Risk-adjustment methods—mathematical models that correct for differing characteristics within a population, such as patient health status—can help account for these factors. However, the science of risk adjustment is still evolving. Experts acknowledge that better risk-adjustment methods are needed to minimize the reporting of misleading or even inaccurate information about health care quality.

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