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The chapters that follow, including this one, focus on how to analyze data—on how to turn data into information, and information into knowledge. One of the most important kinds of data that you will find yourself analyzing is about the quality of care that health systems deliver. Because quality of care is so important, a substantial amount of research has gone into how to measure the quality, safety, and experience of care. This research has led to common approaches for measuring quality—but it also has revealed numerous challenges in measuring quality fairly and consistently. Finally, Medicare, Medicaid, and private payers have moved increasingly to paying for performance on certain quality and safety measures. Because substantial amounts of hospitals’ and physicians’ revenue is now dependent on quality measures, healthcare delivery scientists should be familiar with some of the major measures involved in measuring quality and safety. This chapter reviews these issues.


The Donabedian Model

Avedis Donabedian is widely regarded as the founder of the study of healthcare quality. In 1966, Donabedian published a seminal paper, “Evaluating the Quality of Medical Care.”1 This paper (worth reading in its entirety) laid out what has become known as the Donabedian Model (Table 12-1). The Donabedian Model is now the standard approach for assessing quality. It includes three categories of measures: structural, process, and outcome. Conceptually, this makes sense: The structure of the health system creates and constrains the processes of care that the health system delivers. The process of care interacts directly with patients to create outcomes. Structure leads to process, and process leads to outcome.

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Donabedian’s Structure-Process-Outcome-(Balancing) framework is a core approach to healthcare quality measurement.


Structural measures assess aspects of the health system that help it deliver high-quality care. Is the infrastructure ...

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