Abstract T P255: QCI-NASCAR - Quality Care Improvement with Nursing-driven Acute Stroke CARe

Objective: To observe the impact on stroke code time metrics after applying a “pit stop” model of bedside nursing for telestroke encounters. Background: Despite the recent push for target treatment times in acute stroke codes, no guidelines exist for optimizing practices specific to stroke care via telemedicine. Effective telestroke is dependent on efficient data gathering by remote staff, and lengthy metrics for real-world telestroke often preclude timely tPA treatment. By co-opting “pit stops” as inspiration, an optimized nursing workflow for telestroke can be created on the following principles: Identification of Shared Goals; Organized Urgency with the Removal of Gatekeepers; Multi-personnel, Non-Sequential Processes; Focus on Defined Staged Roles; and Empowered Engagement/Responsibility. Methods: The QCI-NASCAR protocol was implemented in Oct 2013, and data was collected prospectively on consecutive stroke code activations through Apr 2014 at St. Paul University Hospital (Dallas, TX), a telestroke spoke site. The nurse-driven protocol was reinforced by a paper checklist (i.e. “Driver Sheet”), which doubled as a data collection form. Timestamps were recorded in real time for: door time, MD at bedside, CT arrival, needle time, and/or code cancellation. The primary outcome was Door-to-CT (D2CT) times to reflect the portion of the stroke code most impacted by the nursing protocol. Results: Mean D2CT times were: all cases (n=152, 33.2 min), intervention-eligible cases (n=71, 27.0 min), and thrombolytic-eligible cases (n=57, 22.2 min). A trend for lower D2CT times and standard deviations was noted in comparing the first half of the data (n=76, 38.04 ± 58.1 min) to the second (n=77, 27.8 ± 19.1 min; p Conclusion: QCI-NASCAR demonstrates the feasibility of implementing a nursing-driven protocol for telestroke encounters. A larger, multi-institutional trial will demonstrate if such a protocol can significantly and reproducibly lower stroke code metrics to national guideline parameters.