A framework for evidence-adaptive quality assessment that unifies guideline-based and performance-indicator approaches.
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 unified model representation and algorithm for evidence-adaptive quality assessment scoring that can: (1) use both complex case-specific guidelines and single-step population-wide performance-indicators as quality measures; (2) score adherence consistently with quantitative population-based medical utilities of the quality measures where available; and (3) give worst-case and best-case scores for variations based on (a) uncertain knowledge of the best practice, (b) guideline customization to an individual patient or particular population, (c) physician practice style variation, or (d) imperfect reliability of the quality measure. Our solution uses fuzzy measure-theoretic scoring to handle the uncertain knowledge about best-practices and the ambiguity from practice variation. We show results of applying our method to retrospective data from a guideline project to improve the quality of hypertension care.
Advani
Aneel
A
Section on Medical Informatics, Stanford School of Medicine, Stanford, CA, USA.
Goldstein
Mary
M
Musen
Mark A
MA
D000465:Algorithms; D019317:Evidence-Based Medicine; D019983:Guideline Adherence; D006801:Humans; D006973:Hypertension / Q000628:therapy*; D008962:Models, Theoretical; D017410:Practice Guidelines as Topic; D011785:Quality Assurance, Health Care / Q000379:methods*; D015203:Reproducibility of Results