Kevin Taaffe

Vishnunarayan Girishan Prabhu, Kevin Taaffe, Marisa Shehan et al.

Emergency Department (ED) overcrowding continues to be a public health issue as well as a patient safety issue. The underlying factors leading to ED crowding are numerous, varied, and complex. Although lack of in-hospital beds is frequently attributed as the primary reason for crowding, ED's dependencies on other ancillary resources, including imaging, consults, and labs, also contribute to crowding. Using retrospective data associated with imaging, including delays, processing time, and the number of image orders, from a large tier 1 trauma center, we developed a discrete event simulation model to identify the impact of the imaging delays and bundling image orders on patient time in the ED. Results from sensitivity analysis show that reducing the delays associated with imaging and bundling as few as 10% of imaging orders for certain patients can significantly (p-value < 0.05) reduce the time a patient spends in the ED.

Majbah Uddin, Nathan Huynh, Jose M Vidal et al.

This study examined the use of voice recognition technology in perioperative services (Periop) to enable Periop staff to record workflow milestones using mobile technology. The use of mobile technology to improve patient flow and quality of care could be facilitated if such voice recognition technology could be made robust. The goal of this experiment was to allow the Periop staff to provide care without being interrupted with data entry and querying tasks. However, the results are generalizable to other situations where an engineering manager attempts to improve communication performance using mobile technology. This study enhanced Google's voice recognition capability by using post-processing classifiers (i.e., bag-of-sentences, support vector machine, and maximum entropy). The experiments investigated three factors (original phrasing, reduced phrasing, and personalized phrasing) at three levels (zero training repetition, 5 training repetitions, and 10 training repetitions). Results indicated that personal phrasing yielded the highest correctness and that training the device to recognize an individual's voice improved correctness as well. Although simplistic, the bag-of-sentences classifier significantly improved voice recognition correctness. The classification efficiency of the maximum entropy and support vector machine algorithms was found to be nearly identical. These results suggest that engineering managers could significantly enhance Google's voice recognition technology by using post-processing techniques, which would facilitate its use in health care and other applications.