To promote the highest quality healthcare, it is necessary to monitor the outcomes of patient care. The
tools to monitor the outcomes of patient care are not optimal. This thesis develops risk adjusted control chart methods for monitoring in-hospital mortality outcomes in Intensive Care Unit (ICU) patients. It is a medical application of statistics and machine learning to measure outcomes for quality management. Three directions of investigation are followed to achieve this.
The first is the assessment methods of ICU models that predict the probability of death. The desirable attributes of model performance are discrimination, the ability to separate survivors and non-survivors, and calibration, a measure of the extent to which model risk prediction represents that patient's actual risk of dying. An independent assessment of the APACHE III model used in the Princess Alexandra Hospital ICU demonstrated good discrimination and calibration, and so the model was validated in this context for prediction of
The second is a study of statistical process control charts for patient mortality rate. Risk adjusted control chart techniques are subsequently developed to incorporate the validated APACHE III probability of death estimate to control for casemix and severity of illness. The design and performance of these control charts are studied.
The third direction is the development of an alternative model for risk adjustment. The results of preparatory experiments using machine learning techniques, artificial neural networks (ANNs) and support vector machines (SVMs), were comparable to those previously obtained with logistic regression. SVM models are further investigated to model 30-day in-hospital mortality, using raw patient data from the equivalent of one year of patient admissions. Model development is successfully guided by the desirable attributes of model performance: discrimination and
The conclusions of this study are: 1) risk adjusted control charting offers an adjunct to current methods of ICU outcome assessment when monitoring the quality of care; 2) SVMs and ANNs are practical approaches to model the probability of in-hospital mortality for ICU patients; 3) model development can be guided by optimization of the model attributes of discrimination and calibration.