A Quality Improvement Project Using Fall Management Algorithms in Long-Term Care

Abstract

Background: Falls have been an ongoing and reportable problem in long-term care facilities. Moreover, falls can lead to serious physical, psychological and financial consequences for residents, their families and the staff. Each resident has individual risk factors that may lead to falling. Multifactorial interventions, or strategies that target multiple risk factors for falls, have been shown to reduce the number of falls and are recommended for fall prevention and management. The initial step in fall prevention and management includes identifying each resident’s risk factors upon admission into the facility, and after each fall. Local Problem: The medical administrators from a Mid-Atlantic facility expressed a need for a fall prevention and management intervention because of the increased number of falls, despite frequent changes to the facility’s fall management protocol. The latest protocol included fall risk assessment upon admission and fall incident documentation by nurses after each fall. The purpose of this project was to improve fall management in a long-term care unit through implementing the Post Fall Algorithm and reinforcing the Fall Assessment Algorithm with the goals of improving identification of fall risk factors, compliance on post-fall algorithms and overall reducing the number of falls. Interventions: The quality improvement project occurred over a 10-week period in a 33-bed long-term care unit located in a Mid-Atlantic facility. Participants included the certified nursing assistants, certified medicine assistants, registered nurses, nursing administration and providers. The first two weeks included collecting baseline data, recruiting of champions, and training of participants on the algorithms and the fall forms. The Fall Assessment Algorithm provided the staff with a list of intrinsic and extrinsic fall risk factors. The Post Fall Algorithm listed the process to complete forms and assessments within 72 hours after a resident fall. The algorithms were implemented during weeks three through ten, and the impact was monitored by tracking fall rates and compliance with the process of the post-fall algorithm. Descriptive statistics were used to analyze the completion of the Post Fall Algorithm, and determination of trends on fall incidences through the data on the forms. The generated report on fall incidence was analyzed to determine the relationship between the implementation of the algorithm and the fall incidence in the long-term care unit. Results: There was an overall decrease in the average number of falls in the unit from before (𝑥̅=3.33) to after (𝑥̅=2.63) implementation of the Post Fall Algorithm, accompanied by more than 75% staff compliance on documentation of the post fall forms. An inverse relationship was noted between staff compliance and the number of falls. Incidental finding included that the majority of the falls happened in the resident’s room (90%) and during a change in position (86%). Conclusion: Identifying each individual’s risk factors for falls and performing comprehensive evaluation by a proactive multidisciplinary team after a fall are important in developing individualized plans of care and may potentially reduce the number of falls.