must be able to identify safety problems, test solutions, and determine whether their solutions are working. Patients and their representatives need to know what risks exist and how they might be avoided. Data on clinical performance are one key building block for a safe health care delivery system.

Clinical performance data can serve a full range of purposes, from accountability (e.g., professional licensure, legal liability) to learning (e.g., the redesign of care processes, testing of hypotheses). Different purposes necessitate differences in data collection methods, analytic techniques, and interpretation of results. An ideal clinical performance reporting system should be able to function simultaneously along the entire continuum of applications, but such broad use requires careful data system design, automated systems that link directly to care delivery, and explicit data standards.

There are many legitimate applications of clinical performance data, each having its own historical underpinnings and approaches. All of these applications are intended to improve the safety of patient care. The interactions of clinicians and patients are influenced by the environment (e.g., legal liability, purchasing and regulatory policies); the education and training of health professionals (e.g., multidisciplinary training); the health literacy and expectations of individuals (e.g., patients’ understanding of chronic condition and the importance of healthy behaviors); and the organizational arrangements or systems that support care delivery (e.g., internal reporting systems, the availability of computer-aided decision support systems). The greatest gains in patient safety will come from aligning incentives and activities in each of these four areas.

This chapter provides an overview of the many applications of clinical performance data and a discussion of their likely impact. A case study is used to illustrate some of the undesirable consequences that can come from the use of various applications if the data, performance measures, and approaches are not chosen carefully, and if too much emphasis is placed on accountability as opposed to learning applications. Finally, the importance of investing more in approaches targeted directly at fundamental system redesign is briefly discussed. Such approaches offer the greatest potential to improve patient safety but require a far more sophisticated data infrastructure than currently exists in most health care settings.

Although much of the discussion in this chapter presumes the availability of computerized clinical information systems, the collection and analysis of clinical performance data can be carried out without computerized clinical information systems. Such collection and analysis would, however, be greatly facilitated by computerized clinical information systems and the national health information infrastructure (NHII).



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