Goals of Medical Performance Improvement

Understanding Best Clinical Practices

By Brent A. Metfessel; MD, MSbiz-book

The major goals of medical performance improvement are twofold: First, for a particular practice pattern measure, the desire is to narrow the practice variation around the present health care mean. For instance, the spread of the distribution of a cost variance measure should decrease with process improvement.  Second, clinical guideline-based “best practices” can be utilized to move the entire provider population mean toward better cost-efficiency and quality.

Best-Practices

Although best-practices may be guideline-based, they should be adapted to local considerations and evaluated periodically through actual outcomes analysis. Such outcomes measures may include:

  • Cost-efficiency improvement, showing a decrease in resource utilization.
  • An increase in the performance of preventive measures, such as childhood immunizations and various screening tests such as breast and cervical cancer screening.  This may increase costs initially but will more than pay for itself through a decreased illness burden and cost in the future.
  • A decrease in episode length, usually implying a quicker resolution of symptoms.
  • A decrease in emergency room visits and unplanned hospital admissions.
  • A decrease in the rate of “sentinel events” such as status asthmaticus, hemorrhage during pregnancy, diabetic ketoacidosis, and ruptured appendix.

Many of these measures can be obtained using commonly available claims and administrative databases, although supplementation with clinical and functional status data will only increase the reliability and scope of outcomes analysis.

Assessment

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In order to see significant performance improvement in response to quality improvement initiatives, one must be patient.  Two to three years may be needed to see this improvement.  Trending of measures helps analysts to determine whether such improvement is occurring.  Trending of data, however, can be quite resource-intensive since there must be an adequate data set – usually requiring storage of data for several years of experience. 

Conclusion

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