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 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.
Advani
Aneel
A
Stanford Medical Informatics, Stanford University, CA 94025, USA.
Jones
Neil
N
Shahar
Yuval
Y
Goldstein
Mary K
MK
Musen
Mark A
MA
D000465:Algorithms; D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D006801:Humans; D006973:Hypertension / Q000188:drug therapy*; D008485:Medical Audit; D017410:Practice Guidelines as Topic; D019984:Quality Indicators, Health Care; D015203:Reproducibility of Results