Date of Graduation
Fall 12-10-2020
Document Access
Project/Capstone - Global access
Degree Name
Master of Science in Nursing (MSN)
College/School
School of Nursing and Health Professions
First Advisor
Erica Hooper-Arana
Abstract
This paper summarizes the quality improvement falls prevention project on a Medical-Surgical unit with an Enhanced Fall Algorithm incorporated in the Electronic Medical Record conducted by two Clinical Nurse Leader students of the University of San Francisco.
This project addresses the patient falls events in a San Francisco-Bay Area Hospital on a Medical-Surgical unit. The aim is to decrease the rate of falls on a Medical-Surgical microsystem unit by 50% with an Enhanced Fall Algorithm incorporated into the Electronic Medical Record by June 2021. The patient population consists of general medical surgical patients where many of the patients have an increased risk of falls. Efforts at patient fall prevention will be planted at identifying patient risk with an Enhanced Fall Algorithm as a way to improve patient safety.
Utilizing Kotter’s Eight Step Process for Leading Change, the literature review revealed that to prevent patient falls, evidence-based practice must be implemented. Incorporating the Enhanced Fall Algorithm tool in the EMR was presented to the frontline staff for development, appropriateness, evaluation, and sustainability. The outcome measure of patient falls reduction with a target goal of preventing one patient fall from a baseline of two falls per quarter will be measured by tracking patient falls trend. The goal of this implementation is the prevention of one patient fall per quarter from a baseline of 2.04 by June 2021 and will be tracked with a run chart.
The results of this implementation are expected to show a decrease in patient falls relative to the median rate of falls within the hospital from 2 falls per quarter to 1 fall per quarter until June 2021. The importance of the CNL role with this improvement project is to understand how the microsystem works, gaining support from stakeholders, collaborate with frontline staff and incorporating evidence-based practices which are parts of the CNL competencies and training.
Recommended Citation
Sokolovski, Natali, "Decreasing the Fall Rate on a Medical Surgical Unit with an Enhanced Fall Algorithm in the Electronic Medical Record" (2020). Master's Projects and Capstones. 1114.
https://repository.usfca.edu/capstone/1114
