Abstract Hospitalized patients are at risk for acquiring an infection they did not previously have, increasing their risk of harm, extending their stay, and raising the cost of their care. Two types of healthcare-associated infections (HAIs), catheter-associated urinary tract infections (CAUTI) and catheter-associated blood stream infections (CLASBI), can come from the introduction of a foreign object (a catheter) in to the body or from improper care and maintenance of the catheter. Systematic efforts have shown improvement in the ability of organizations to reduce HAIs, but they remain a devastating and costly problem that poses a serious risk to a hospitalized patient's life. A new use of telemedicine, applying telemedicine technology in acute intensive care units (tele- ICU), has the potential to reduce HAI rates in hospitals. The tele-ICU model combines innovative technology with provider expertise (physicians trained in caring for critically ill patients in ICU environments [intensivists] and registered nurses) to implement a systematic care model, deliver evidence-based practice, and potentially improve the quality of care. At a remote location, the tele-ICU team uses specialized software to analyze data entered by the bedside team, monitor for trends in data, capture highly sensitive alerts, and recognize patterns with symptoms. The purpose of this study is to undertake a secondary analysis of national data to determine if the presence of a tele-ICU system influences CAUTI and CLASBI rates. The specific aims of this study include examining differences in hospital-associated CAUTI and CLASBI rates between hospitals with tele-ICU systems and hospitals that do not have tele-ICU, and whether differences are dependent on patient and/or hospital characteristics. We will control for patient (i.e., age, etc.) and hospital-level covariates (i.e., bed size, teaching status, location, etc.) and examine the impact of tele-ICU status and interactions between tele-ICU status and patient and hospital characteristics on the probability of acquiring a CAUTI or CLABSI. This study is innovative because we will be the first, to our knowledge, to look at the potential benefit of tele-ICU healthcare delivery systems in relation to HAI rates by examining large national datasets. To accurately describe our population and sample, we will utilize propensity score matching techniques to compare patients in approximately 250 tele-ICU hospitals to a similarly matched sample of non-tele-ICU hospitals. Our analysis is the first step in determining the impact of tele-ICUs on the probability of being diagnosed with a CAUTI and CLASBI, leading to future research and dissemination on best practices associated with reducing HAIs through hospitals with tele-ICU systems, potentially saving thousands of lives and millions of dollars in healthcare costs.