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The use of mobile and ubiquitous computing devices is advantageous for collecting and sharing patient data at the bedside or in hospital waiting areas. iPhone web applicationsor web apps -combine the power of Internet based solutions with the simplicity of multi-touch and gesture technology, all one portable device. Since many data collection platforms have moved to an online paradigm (or are in the process of doing so), a web app is an ideal solution. In this work, we show the advantages of using a web app for patient data collection, as an imaging engine, and for patient feedback and survey systems. This can be achieved by taking advantage of simple functions available on the iPhone OS when using an online collection platform.

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Sepsis is a significant cause of mortality and morbidity and is often associated with increased hospital resource utilization, prolonged intensive care unit (ICU) and hospital stay. The economic burden associated with sepsis is severe. With advances in medicine, there are now aggressive goal oriented treatments that can be used to help these patients. If we were able to predict which patients may be at risk for sepsis we could start treatment early and potentially reduce the risk of mortality and morbidity. Analytic methods currently used in clinical research to determine the risk of a patient developing sepsis may be further enhanced by using multi-modal analytic methods that together could be used to provide greater precision. Researchers commonly use univariate and multivariate regressions to develop predictive models. We hypothesized that such models could be enhanced by using multi-modal analytic methods that together could be used to provide greater precision. In this paper, we analyze data about patients with and without sepsis using a decision tree approach. A comparison with a regression approach shows strong similarity among variables identified, though not an exact match. We compare the variables identified by the different approaches and draw conclusions about the respective predictive capabilities.