OBJECTIVES: The poster presentation purpose is to demonstrate the predicted clinical and economic impacts of bedside peripherally inserted central catheters when using real time navigation and tip confirmation with the Sherlock 3CG^®Tip Confirmation System.  METHODS: This health economic analysis is based on a model that can accommodate actual facility experience and hypothetic hospital system scenarios.  Using this model, clinical and economic outcomes associated with peripherally inserted central catheter placement with traditional “blind” placement and with Sherlock 3CG^® Tip Confirmation System over three years were assessed. Specifically, the current mix assumed an equal distribution of blind bedside placement and tip navigation without tip confirmation. For the current mix, it was assumed that all patients required a confirmatory chest x-ray. The future mix assumed that all eligible patients (~93%) switched to Sherlock 3CG^®Tip Confirmation System and did not require confirmatory chest x-rays. Capital, materials, chest x-rays, and malposition rates were assessed. Inputs were derived from published literature where available. Sensitivity analyses, such as varying base-case inputs ± 20% were conducted to test a range of assumptions (e.g., malposition rates). RESULTS: For an estimated population size of 10,000 annual patients, adoption of Sherlock 3CG^® Tip Confirmation System into a real-world mix of technologies was predicted to avert 30,105 chest x-rays and be cost-saving over 3 years. The cumulative cost-savings were estimated to be $21,132 over the 3 year time horizon. Additionally, Sherlock 3CG^®Tip Confirmation System was predicted to be cost-saving in the majority of sensitivity analyses. These analyses were not based upon head-to-head clinical comparisons.  CONCLUSIONS: These analyses suggest that use of Sherlock 3CG^® Tip Confirmation System may reduce the clinical, resource, and economic limitations associated with historical peripherally inserted central catheter placement methods. Additional analyses, using real-world time and motion data, will help to further elucidate these findings.