The JITS system has the potential to improve the treatment outcome of victims of cardiac arrest. The JITS framework can be applied to many situations in which novices deal with urgent tasks without expertise available.

Pharmacokinetics plays an important role in the delivery of anesthesia. Their complex nature creates a challenging control task for anesthesiologists while delivering anesthesia. The anesthesia domain confronts many known control difficulties such as non-linearity, oscillations, and sub-optimal feedback. A clinician's understanding of pharmacokinetics plays an important role in tracking performance. Information about anesthesiologists' knowledge of pharmacokinetics was collected and analyzed. Effects of expertise and considerable variability, even within groups, were observed in the collected data. Reducing variability and supporting the tracking task in the administration of anesthesia could lead to improved patient outcomes.

More than 300,000 people die from sudden cardiac arrest (SCA) annually in the United States. As professional responders such as EMTs make efforts to expedite their arrival, critical minutes pass jeopardizing the victim's health. Providing life-sustaining intervention in the first few minutes greatly contributes to healthier outcomes. Often, there are witnesses to SCA events that could respond immediately, but they are incapable of providing treatment. The just-in-time support (JITS) approach aims to assist novice operators in completing unpracticed tasks such as cardiopulmonary resuscitation (CPR) at the moment of need. By providing naïve users plans, cues, and feedback, JITS systems facilitate goal accomplishment. The results of this work suggest a JITS device could empower novice responders with CPR capabilities. Widespread deployment of such a device could greatly decrease response time and save lives.

Objective The objective of the CARRECT software is to make cutting edge statistical methods for reducing bias in epidemiological studies easy to use and useful for both novice and expert users. Introduction Analyses produced by epidemiologists and public health practitioners are susceptible to bias from a number of sources including missing data, confounding variables, and statistical model selection. It often requires a great deal of expertise to understand and apply the multitude of tests, corrections, and selection rules, and these tasks can be time-consuming and burdensome. To address this challenge, Aptima began development of CARRECT, the Collaborative Automation Reliably Remediating Erroneous Conclusion Threats system. When complete, CARRECT will provide an expert system that can be embedded in an analyst’s workflow. CARRECT will support statistical bias reduction and improved analyses and decision making by engaging the user in a collaborative process in which the technology is transparent to the analyst. Methods Older approaches to imputing missing data, including mean imputation and single imputation regression methods, have steadily given way to a class of methods known as “multiple imputation” (hereafter “MI”; Rubin 1987). Rather than making the restrictive assumption that the data are missing completely at random (MCAR), MI typically assumes the data are missing at random (MAR). There are two key innovations behind MI. First, the observed values can be useful in predicting the missing cells, and thus specifying a joint distribution of the data is the first step in implementing the models. Second, single imputation methods will likely fail not only because of the inherent uncertainty in the missing values but also because of the estimation uncertainty associated with generating the parameters in the imputation procedure itself. By contrast, drawing the missing values multiple times, thereby generating m complete datasets along with the estimated parameters of the model properly accounts for both types of uncertainty (Rubin 1987; King et al. 2001 ). As a result, MI will lead to valid standard errors and confidence intervals along with unbiased point estimates. In order to compute the joint distribution, CARRECT uses a bootstrapping-based algorithm that gives essentially the same answers as the standard Bayesian Markov Chain Monte Carlo (MCMC) or Expectation Maximization (EM) approaches, is usually considerably faster than existing approaches and can handle many more variables. Results Tests were conducted on one of the proposed methods with an epidemiological dataset from the Integrated Health Interview Series (IHIS) producing verifiably unbiased results despite high missingness rates. In addition, mockups ( Figure 1 ) were created of an intuitive data wizard that guides the user through the analysis processes by analyzi...

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