Citation

Attanayake PM, Reither J. BMJ Open Qual. 2023; 12(4): e002450.

Copyright

(Copyright © 2023, BMJ Publishing Group)

DOI

10.1136/bmjoq-2023-002450

PMID

38105241

Abstract

INTRODUCTION: As the leading cause of fatal injuries in geriatric populations, falls are a serious health concern with a predicted rate of seven fall-related deaths per hour by 2030. The Timed Up and Go (TUG) test is a screening tool proposed by the Center for Disease Control for evaluating patients' risk of falling ('fall risk'). However, there exist no current protocols on how to use the test results to decrease fall risk. The Fall Prevention Protocol (FPP) is a new comprehensive fall prevention programme created to address the lack of standardised TUG test follow-up in an Advanced Primary Care (APC) clinical setting. The programme provides a comprehensive approach for identifying fall risk and creating an individualised intervention plan to reduce the likelihood of falls. Due to the recent creation and implementation of FPP, there have been no efforts made to quantitatively prove that the FPP is more effective at reducing falls than the use of the TUG test alone without an established protocol for intervention.

METHODS: This quality improvement project focuses on creating a user-friendly statistical analysis software for determining the effectiveness of the FPP compared with just using the TUG test without a standardised post-test protocol in reducing the number of falls in geriatric patients in an APC setting. The software-created using MATLAB R2022b and finalised as a stand-alone computer application-takes in data sets of patient fall history, determines the best statistical test for comparing the data, then analyses and provides users with a conclusion regarding which protocol is more beneficial for reducing falls.

RESULTS: The developed software was proven to be user-friendly, able to be used in a healthcare setting with minimal necessary training, and deemed appropriate for data analysis of future fall risk protocol effectiveness testing.

Language: en

Keywords

Statistics; Geriatrics; Healthcare quality improvement; Performance measures; Quality improvement