Graphical model-based O/E control chart for monitoring multiple outcomes from a multi-stage healthcare procedure.
Most statistical process control programmes in healthcare focus on surveillance of outcomes at the final stage of a procedure, such as mortality or failure rates. Such an approach ignores the multi-stage nature of these procedures, in which a patient progresses through several stages prior to the final stage. In this paper, we introduce a novel approach to statistical process control programmes in healthcare. Our proposed approach is based on the regression adjustment and multi-stage control charts that have been in use in industrial applications for decades. Three advantages of the approach are: better understanding of how outcomes at different stages relate to each other, explicit monitoring of upstream stage outcomes may help curtail trends that lead to poorer end-stage outcomes and understanding the impact of each stage can help determine the most effective allocation of quality improvement resources. A test statistic for the control charts is proposed. Simulations are performed to test the control charts, and the results are summarised using an empirical probability of true detection. An illustrative example using data from a maternity unit is included. A main result from the simulation study is that taking a multi-stage approach makes it easer to explicitly identify shifts in upstream stage outcomes that might otherwise be signalled in final stage outcomes if dependence between stages is ignored.
School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, New Zealand nokuthaba.sibanda@vuw.ac.nz.
Nokuthaba
N
Sibanda
D003196:Computer Graphics; D003198:Computer Simulation; D003695:Delivery of Health Care; D005260:Female; D006771:Hospitals, Maternity; D006801:Humans; D017063:Outcome Assessment, Health Care / Q000379:methods*; D011247:Pregnancy; D012044:Regression Analysis
O/E; directed acyclic graph; healthcare performance monitoring; multi-stage control chart