Developing quality indicators and auditing protocols from formal guideline models: knowledge representation and transformations.
Automated quality assessment of clinician actions and patient outcomes is a central problem in guideline- or standards-based medical care. In this paper we describe a model representation and algorithm for deriving structured quality indicators and auditing protocols from formalized specifications of guidelines used in decision support systems. We apply the model and algorithm to the assessment of physician concordance with a guideline knowledge model for hypertension used in a decision-support system. The properties of our solution include the ability to derive automatically context-specific and case-mix-adjusted quality indicators that can model global or local levels of detail about the guideline parameterized by defining the reliability of each indicator or element of the guideline.
Advani
Aneel
A
Stanford Medical Informatics, Stanford University School of Medicine, California, USA.
Goldstein
Mary
M
Shahar
Yuval
Y
Musen
Mark A
MA
D000465:Algorithms; D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D003953:Diagnosis-Related Groups; D019983:Guideline Adherence; D006801:Humans; D008485:Medical Audit / Q000379:methods*; D017410:Practice Guidelines as Topic; D019984:Quality Indicators, Health Care; D012985:Software Design