Other Titles
Rising Star Poster/Presentation
Abstract
Background & Significance: Oncology patients often seek emergency room (ER) care for symptom management. Accessing care through the ER can result in increased risks for: nosocomial infections, treatment delays, hospitalization/rehospitalization, and healthcare costs.1,2 Early identification/intervention for symptom management is crucial for improving patient outcomes. Implementing evidence-based symptom management (EBSM) algorithms may reduce ER encounters.
Purpose: To determine: 1) the most common symptoms reported 2) the EBSM algorithm and category of recommendations used, and 3) if EBSM algorithms decreased ER encounters.
Methods: EBSM algorithms were implemented to address the nine most common symptoms (pain, nausea/vomiting, diarrhea, fatigue, hair/skin/nails, constipation, fever, peripheral neuropathy, and mucositis). The EBSM algorithms used a “stop light method” red (severe), yellow (moderate) green (mild) to categorize symptom severity and guide recommendations.
Data Collection/Analysis: Pre-implementation data included the most common symptoms reported and the number of ER encounters (per month). Post implementation data collection included the previous metrics, plus specific EBSM algorithm and category of recommendation. Descriptive statistics and a one-tailed Independent Samples t test (alpha 0.5) were conducted to examine the difference in ER encounters before/after implementation.
Results: Pre-implementation the most common symptoms reported were nausea, vomiting, and diarrhea, with 286 ER encounters. Post-implementation EBSM algorithms were used 193 times for 214 oncology patients, addressing symptoms such as pain (n= 42), nausea/vomiting (n= 38), diarrhea (n= 28), fatigue (n= 27), hair/skin/nails (n= 23), constipation (n=13), fever (n=9), peripheral neuropathy (n=7), and mucositis (n=6). Recommendations fell into red (n=18), yellow (n=39), and green (n=98) categories. Post-implementation ER encounters decreased by 27% from 286 to 208, indicating a significant difference between pre- and post-implementation groups t (2.24), p=.033), with the pre-group having more ER encounters (M =72, SD =10) than the post-group (M =52, SD 14). The effect size between the pre- and post-implementation groups was calculated using Cohen’s d, resulting in a value of 1.64, indicating a large effect.
Notes
References:
1. Gallaway, NM.S., Idaikkadar, N., Tai, E., Momin, B., Rohan, E. A., Townsend, J., Puckett, M., & Stewart, S.(2021). Emergency department visits among people with cancer: Frequency, symptoms and characteristics. Journal of American College of Emergency Physicians, 2(3), e12438. https://doi.org/10.1002/emp2.12438
2. Fleshner, L., Lagree, A., Shiner, A., Alera, M. A., Bielecki, M., Grant, R., Kiss, A., Krzyzanowska, M. K., Cheng, I., Tran, W. T. & Gandhi, S. (2023). Drivers of emergency department use oncology patients in the era of novel cancer therapeutics: A systematic review. The Oncologist, 28(12), 1020-1033. https://doi.org/10.1093/oncolo/oyad161
Sigma Membership
Alpha
Type
Poster
Format Type
Text-based Document
Study Design/Type
Other
Research Approach
Translational Research/Evidence-based Practice
Keywords:
Oncology Patients, Emergency Department Visits, Symptom Management
Recommended Citation
Hums, Ashley; Bernstein, Kimberley; Greenan, Lisa; and Storey, Susan, "Implementation of Evidence-Based Symptom Management Algorithms to Reduce Emergency Room Encounters" (2025). International Nursing Research Congress (INRC). 195.
https://www.sigmarepository.org/inrc/2025/posters_2025/195
Conference Name
36th International Nursing Research Congress
Conference Host
Sigma Theta Tau International
Conference Location
Seattle, Washington, USA
Conference Year
2025
Rights Holder
All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record.
Review Type
Invited Presentation
Acquisition
Proxy-submission
Implementation of Evidence-Based Symptom Management Algorithms to Reduce Emergency Room Encounters
Seattle, Washington, USA
Background & Significance: Oncology patients often seek emergency room (ER) care for symptom management. Accessing care through the ER can result in increased risks for: nosocomial infections, treatment delays, hospitalization/rehospitalization, and healthcare costs.1,2 Early identification/intervention for symptom management is crucial for improving patient outcomes. Implementing evidence-based symptom management (EBSM) algorithms may reduce ER encounters.
Purpose: To determine: 1) the most common symptoms reported 2) the EBSM algorithm and category of recommendations used, and 3) if EBSM algorithms decreased ER encounters.
Methods: EBSM algorithms were implemented to address the nine most common symptoms (pain, nausea/vomiting, diarrhea, fatigue, hair/skin/nails, constipation, fever, peripheral neuropathy, and mucositis). The EBSM algorithms used a “stop light method” red (severe), yellow (moderate) green (mild) to categorize symptom severity and guide recommendations.
Data Collection/Analysis: Pre-implementation data included the most common symptoms reported and the number of ER encounters (per month). Post implementation data collection included the previous metrics, plus specific EBSM algorithm and category of recommendation. Descriptive statistics and a one-tailed Independent Samples t test (alpha 0.5) were conducted to examine the difference in ER encounters before/after implementation.
Results: Pre-implementation the most common symptoms reported were nausea, vomiting, and diarrhea, with 286 ER encounters. Post-implementation EBSM algorithms were used 193 times for 214 oncology patients, addressing symptoms such as pain (n= 42), nausea/vomiting (n= 38), diarrhea (n= 28), fatigue (n= 27), hair/skin/nails (n= 23), constipation (n=13), fever (n=9), peripheral neuropathy (n=7), and mucositis (n=6). Recommendations fell into red (n=18), yellow (n=39), and green (n=98) categories. Post-implementation ER encounters decreased by 27% from 286 to 208, indicating a significant difference between pre- and post-implementation groups t (2.24), p=.033), with the pre-group having more ER encounters (M =72, SD =10) than the post-group (M =52, SD 14). The effect size between the pre- and post-implementation groups was calculated using Cohen’s d, resulting in a value of 1.64, indicating a large effect.
Description
This project implements evidence-based practice symptom management alogorythms to reduce the number of emergency room encounters among oncology patients.