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An intelligent case-adjustment algorithm for the automated design of population-based quality auditing protocols.
Advani A, Jones N, Shahar Y, Goldstein MK, Musen MA. An intelligent case-adjustment algorithm for the automated design of population-based quality auditing protocols. Medinfo. 2004 Sep 1; 11(Pt 2):1003-7.
We develop a method and algorithm for deciding the optimal approach to creating quality-auditing protocols for guideline-based clinical performance measures. An important element of the audit protocol design problem is deciding which guide-line elements to audit. Specifically, the problem is how and when to aggregate individual patient case-specific guideline elements into population-based quality measures. The key statistical issue involved is the trade-off between increased reliability with more general population-based quality measures versus increased validity from individually case-adjusted but more restricted measures done at a greater audit cost. Our intelligent algorithm for auditing protocol design is based on hierarchically modeling incrementally case-adjusted quality constraints. We select quality constraints to measure using an optimization criterion based on statistical generalizability coefficients. We present results of the approach from a deployed decision support system for a hypertension guideline.