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The history of performance measurement in health care goes at least as far back as Florence Nightingale, who developed an elaborate data collection and statistical analysis system focusing on in-hospital mortality during the Crimean War era. Her system enabled comparisons from hospital to hospital, from unit to unit within hospitals, and within the same hospitals over time. Graphical presentations highlighted key findings for unsophisticated audiences. This explicit, objective measurement system was the basis of significant breakthroughs in understanding the relationship between sanitary conditions and hospital morbidity and mortality. Nightingale’s methodology also serves as an illustration of three key questions that—according to Drs. Nerenz and Neil—underlie virtually all other considerations about effective performance measures, past or present. What is the entity being measured? Who is using the information? What core organizational processes or skills are the measures designed to reflect?
As the authors move from historical overview to describing the implementation of performance measures in modern health care systems, Nerenz and Neil caution health care executives to approach the implementation process with a clear understanding of today’s health care environment. Among the trends the authors note are on-going:
There is little debate that performance measures in health care should include some mix of clinical quality, patient (or member) satisfaction, efficiency, utilization and financial performance. The authors observe that the real challenge is in the selection and implementation of a balanced set of measures and the effective use of those measures for external accountability and internal quality improvement purposes. Practical strategies for meeting that challenge are included in the study and are demonstrated by specific examples of health care systems engaged in innovative, successful work in the area of performance measurement. Among the systems and performance measurements described are:
Nerenz and Neil conclude the report with several invaluable observations for those seeking to enhance their organizations’ performance measurement programs. First, they note that even as the general concept of a “balanced scorecard” gains favor, the perfect measurement set has not yet been invented. Every measure has one or more flaws, as do data available to support them. However, performance measures need not be perfect in order to be useful. “Good enough” performance measure (i.e., showing evidence of a problem when there really is one or reflecting a trend in the actual direction of change) may be all that is needed to take the organization to the next level of improvement.
Secondly, even accurate data can be useless if there is too much (or too little) “organizational distance” between the unit of analysis for the data and the unit of control for making change. To combat this, several successful organizations have instituted a top-down, “layered” approach to performance measurement in their systems.
And, finally, performance measurement is best supported when it rests on a clear scientific and statistical foundation. While there is no doubt that professional-level research methods and statistics are sometimes useful, it is also true that basic principles and clear thinking around data issues are often all that is necessary to reach meaningful conclusions.
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