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Implementation of Reminders to Improve Reassessment Rate of Specialty Beds

Authors
Lin, Qingting
Date
2024-05
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DNP Project
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Problem: In 2021, a large academic hospital allocated $1.8 million for specialty beds to reduce hospital-acquired pressure ulcers (HAPUs) in high-risk patients, such as those with trauma. The facility employed an online bed request system/ bed algorithm (BRS-BA) for initial bed selection to reduce HAPUs. However, insufficient reassessments, especially during patient transfers from intensive care units (ICUs), have led to prolonged specialty bed use and increased financial costs. Data from a 24-bed Neurotrauma Intermediate Care Unit (NTIMC) showed that patients remain in specialty beds until discharge. Purpose: The quality improvement (QI) project was to implement reminders for nurses to reevaluate specialty bed need via the hospital's BRS-BA during ICU patient transfers. Methods: Over a 15-week period in the fall of 2023, pre-survey and post-surveys were administered. The first week emphasized education, distributed reminder materials, and asked charge nurses to get shift updates. Starting the second week, monthly email reminders were sent. The QI-PL audited charts weekly, logged eligible patients into Research Electronic Data Capture (REDCap), created run charts, and updated stakeholders. Strategies to enhance compliance include designating charge nurses as champions, staff recognition, and interdisciplinary teamwork. Results: Results demonstrated of 55 nurses, 87% (n = 48) received the education, and 69% (n= 38) responded to the pre-survey and 45% (n=25) completed the postsurvey. Weekly specialty bed reassessment rates varied from 0% to 100%. Out of 52 patients, 35% (n=18) had a primary diagnosis of both head and spinal cord injuries. Notably, one patient moved from a specialty bed to a standard bed after reassessment. Exact bed savings were unclear due to vague nursing documentation on specialty bed types. Conclusions: Findings suggest that employing reminders can effectively foster clinical changes and optimize resource use.

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