Potential Effectiueness of Quality Assurance Screening Using Large but Imperfect Databases

Abstract

To examine the effect of imprecise classification of patient risk (severity of illness) on an otherwise highly accurate quality assurance screening technique, data on clinical outcomes were generated for a simulated hospital system consisting of 108 facilities treating approximately 565,000 patients a year. In these simulations, marked differences in facility size, casemix distribution, and quality of care were combined with random variations in outcome. Pooled data for all 108 facilities were used to create algorithms that combined 468 discrete patient risk classifications into either ten or three groups with broad, overlapping ranges of patient-specific risks of unfavorable clinical results. When derived algorithms were applied to independently generated facility-specific data, the ability to identify hospital systems with and without quality of care problems was maintained with ten, but not with three, risk groups. However, even three moderately heterogeneous risk groups were sufficient to preserve a high degree of sensitivity and specificity in screening for potential quality of care problems within individual facilities. Thus, outcome-based quality assurance screening can be highly accurate in actual health care situations in which only imprecise estimations of patient-specific risk can be achieved.