This article outlines a decision support system that seeks to help community nurses monitor the well-being of their chronically ill patients. It is designed for nurses to stay in contact with their patients without spending unnecessary time on less productive aspects of community nursing, such as avoidable driving to and from patients' houses and taking measurements of vital signs to assess their health condition. It therefore allows the nurse to spend more time on managing the factors that could lead to a healthier patient. The decision support system is developed for two levels of mathematical capability. Nurses with a statistical background are provided with in-depth information allowing them to detect changes in mean, mean square error (and hence variation), and correlations using a variation on dynamic principle components. Less mathematically inclined nurses are offered information about trends, change points, and a simpler multivariate view of a patient's well-being involving parallel coordinate plots.

The vital signs of chronically ill patients are monitored daily. The record flags when a specific vital sign is stable or when it trends into dangerous territory. Patients also self-assess their current state of well-being, i.e. whether they are feeling worse than usual, neither unwell nor very well compared to usual, or are feeling better than usual. This paper examines whether past vital sign data can be used to forecast how well a patient is going to feel the next day. Reliable forecasting of a chronically sick patient’s likely state of health would be useful in regulating the care provided by a community nurse, scheduling care when the patient needs it most. The hypothesis is that the vital signs indicate a trend before a person feels unwell and, therefore, are lead indicators of a patient going to feel unwell. Time series and classification or regression tree methods are used to simplify the process of observing multiple measurements such as body temperature, heart rate, etc., by selecting the vital sign measures, which best forecast well-being. We use machine learning techniques to automatically find the best combination of these vital sign measurements and their rules that forecast the wellness of individual patients. The machine learning models provide rules that can be used to monitor the future wellness of a patient and regulate their care plans.

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Spatio-temporal surveillance methods for detecting outbreaks are common with the SCAN statistic setting the benchmark. If the shape and size of the outbreaks are known, then the SCAN statistic can be trained to efficiently detect these, however this is seldom the case. Therefore devising a plan that is efficient at detecting a range of outbreaks that vary in size and shape is important in practical applications. So this paper introduces a method called EWMA Surveillance Trees that uses a binary recursive partitioning approach to locate and detect outbreaks. This approach is explained and then its performance is compared to that of the SCAN statistic in a series of simulation studies. While the SCAN statistic is shown to remain the most effective at detecting outbreaks of a known shape and size, the EWMA Surveillance Trees are shown to be more robust. The method is also applied to an example of actual data from motor vehicle crashes in an area of Sydney Australia from 2000 to 2004 in order to detect dates and geographic regions with outbreaks of crashes above the expected

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