An expert system for performance-based direct delivery of published clinical evidence.

OBJECTIVE: To develop a system for clinical performance improvement through rule-based analysis of medical practice patterns and individualized distribution of published scientific evidence. METHODS: The Quality Feedback Expert System (QFES) was developed by applying a Level-5 expert system shell to generate clinical direct reports for performance improvement. The system comprises three data and knowledge bases: 1) a knowledge base of measurable clinical practice parameters; 2) a practice pattern database of provider-specific numbers of patients and clinical activities; and 3) a management rule base comprising "redline rules" that identify providers whose practice styles vary significantly. Clinical direct reports consist of a table of practice data highlighting individual utilization vs recommendation and selected pertinent statements from medical literature. RESULTS: The QFES supports integration of recommendations from several guidelines into a comprehensive and measurable quality improvement plan, analysis of actual practice patterns and comparison with accepted recommendations, and generation of a confidential individualized direct report to those who significantly deviate from clinical recommendations. The feasibility of the practice pattern analysis by the QFES was demonstrated in a sample of 182 urinary tract infection cases from a primary care clinic. In a set of clinical activities, four questions/procedures were associated with significant (p < 0.001) and unexplained variation. CONCLUSION: The QFES provides a flexible tool for the implementation of clinical practice guidelines in diverse and changing clinical areas without the need for special program development. Preliminary studies indicate utility in the analysis of clinical practice variation and deviations. Using data obtained through a retrospective chart audit, the QFES was able to detect overutilization, and to identify nonrandom differences in practice patterns.