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Assessing and improving quality depends upon good measurement. This chapter will discuss basic principles of measuring quality in medical practice and how data can be used to identify opportunities for improvement and support improvement efforts.



At all levels of the health care industry, from national to local and from health systems to individual practitioners, performance assessment is now routine. Care is assessed in multiple domains, such as clinical outcomes, adherence to standards, the patient experience of care, and the cost of care. Through external comparison, or “benchmarking,” payers, the government, the public, and others attempt to judge the quality of health care providers and organizations. Performance measurement is more and more being tied to payment in the form of value-based contracts, with financial risks for performance shifting toward the providers of care, behooving us to understand how performance is assessed and improved. In order for external comparisons to be meaningful, several criteria need to be met:

  • The measure should be accurately and reliably recorded across sites. As an example, length of stay for an emergency department visit will have defined starting and ending times that are routinely captured, recorded, and reported. If the standard definitions and practices are followed across all emergency departments, then length of stay comparisons should be meaningful.

  • The measure should be valid and meaningfully reflect an important outcome or process. Mortality is an example of an outcome measure that has face validity, but how it is defined will affect its meaningfulness. If mortality is measured only during the inpatient stay, it will appear better for hospitals that are effective at transferring patients to other settings prior to death. Measuring mortality over a longer term, such as 30 days, 6 months, or 1 year, may be more meaningful.

Measures like mortality, patient functional status, and unintended hospital readmissions are examples of outcome measures. Outcome measures tend to be the most salient to patients, the public, and to health care providers. When using outcome measures for external comparison, however, the validity of comparison often requires adjusting for influences on the outcome that are not determined by the quality of care provided. Factors such as age, comorbid illness, and social support often impact outcomes. Some of these factors can be identified from the available data sources and included in risk adjustment algorithms. The degree to which an algorithm can truly adjust for risk depends upon the quality of the data available for the adjustment model and the power of the model itself to reflect these influences.

Process measures reflect the steps in clinical care provided to patients. Common examples of process measures used to assess hospital performance are the CMS core measures. Generally, process measures do not have to be adjusted for clinical or demographic factors, as it is ...

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