title: A Bayesian approach to risk-adjusted outcome monitoring in healthcare.
abstract: Clinical outcomes are commonly monitored in healthcare practices to detect changes in care providers' performance. One key challenge in outcome monitoring is the need of adjustment for patient base-line risks. Various control charting methods have been developed to conduct risk-adjusted outcome monitoring, but they all rely on the availability of a large number of historical data. We propose a Bayesian approach to this type of monitoring for cases where historical data are not available. In our approach, detection of change is formulated as a model-selection problem and solved using a popular Bayesian tool for variable selection, the Bayes factor. Issues in decision-making about whether there is a change point in the observed patient outcomes are addressed, including specification of priors and computation of Bayes factors. This approach is applied to a real data set on cardiac surgeries, and its performance under different parameter scenarios is studied through simulations.
authors.affiliation: Department of Industrial and Manufacturing Systems Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA. lzeng@uta.edu
authors.forename: L
authors.initials: L
authors.lastname: Zeng
authors.forename: S
authors.initials: S
authors.lastname: Zhou
mesh_terms: D001499:Bayes Theorem; D006348:Cardiac Surgical Procedures / Q000706:statistics & numerical data; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D011785:Quality Assurance, Health Care / Q000706:statistics & numerical data*; D012306:Risk; D016896:Treatment Outcome