An intelligent case-adjustment algorithm for the automated design of population-based quality auditing protocols

We develop a method and algorithm for deciding the optimal approach to creating quality-auditing protocols for guidelinebased clinical performance measures. An important element of the audit protocol design problem is deciding which guideline 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.