Each year approximately five million people are treated in the nation's intensive care units making intensive care one of the most expensive components of the U.S. healthcare system. Of these patients, 400,000-500,000 will die annually giving the ICU the distinction of having the highest rate of mortality and complications in the hospital setting. Studies have demonstrated that one in ten patients who die each day in ICUs might survive if intensivists were present to manage clinical care and direct treatment plans (Randolph & Pronovost, 2002; Dimick, Pronovost, Heitmiller & Lipsett, 2001; Pronovost et al., 2002). The utilization of supplemental remote telemedicine has been investigated as a means of compensating for the limited resource of intensivists (Breslow et al., 2004; Rosenfeld et al., 2000). One specific use of this technology, the electronic intensive care unit or eICU®, has previously demonstrated the potential to improve physiological and economic outcomes in ICU patients through the use of integrated decision-support and patient data systems. The present study focuses on the eICU® as a 21st century technology capable of improving the quality of patient care and identifies those factors most likely to impact the success of this clinical intervention. This research utilizes a non-experimental pre-and post-intervention study design and examines patient data collected on all admissions to five ICUs managed by two regional tertiary care hospitals during a 36-month time period. Each ICU is equipped with eICU® software systems that allow intensivist surveillance of patients from a remote facility. The data is analyzed using both structural equation modeling and decision tree regression modeling to statistically evaluate the effects of patient, hospital and unit characteristics on proximal and distal outcomes in ICU patients. As the development of clinical complications subsequently affects patient length of stay, cost of stay, and mortality, it becomes increasingly imperative to seek interventions capable of reducing the risk of unfavorable patient outcomes. This study closely examines one such intervention, the eICU®.