Multiple data sources fusion for enterprise quality improvement by a multilevel latent response model

Abstract

Quality improvement of an enterprise needs a model to link multiple data sources, including the independent and interdependent activities of individuals in the enterprise, enterprise infrastructure, climate, and administration strategies, as well as the quality outcomes of the enterprise. This is a challenging problem because the data are at two levels—i.e., the individual and enterprise levels—and each individual's contribution to the enterprise quality outcome is usually not explicitly known. These challenges make general regression analysis and conventional multilevel models non-applicable to the problem. This article a new multilevel model that treats each individual's contribution to the enterprise quality outcome as a latent variable. Under this new formulation, an algorithm is developed to estimate the model parameters, which integrates the Fisher scoring algorithm and generalized least squares estimation. Extensive simulation studies are performed that demonstrate the superiority of the proposed model over the competing approach in terms of the statistical properties in parameter estimation. The proposed model is applied to a real-world application of nursing quality improvement and helps identify key nursing activities and unit (a hospital unit is an enterprise in this context) quality-improving measures that help reduce patient falls.