Abstract
Abstract
Background
Patients with cancer visit the emergency department often and have a high rate of admission compared to other patients. Admission rates by institution may vary widely, even after accounting for patient and hospital-specific characteristics.
Objectives
To review the variables that affect admission rates among patients with cancer in the emergency department.
Methods
We performed a secondary analysis of a prospective cohort study of patients with cancer at 18 emergency departments between March 1, 2016, and January 30, 2017, to examine differences in patient populations between hospitals with varying admission rates. We calculated the percentage admitted by hospital and used it to categorize hospitals into quartiles. We compared outcomes, patient demographics, and disease characteristics between the admission quartiles using linear or logistic regression.
Results
A total of 1075 patients were included. The median age of our sample was 64, and 51% of patients were female, 84% were white, and 13% were Black. Of the 1075 patients, 615 (57.2%) were admitted as inpatients with a range from 21.2 to 81.7% by hospital. Differences between admission quartiles were found for education, mode of arrival, and recent chemotherapy (p < 0.05). There were no significant differences among quartiles in age, gender, race, or ECOG score. We found significant difference between admission quartiles in 30-day emergency department revisits. Differences in readmission rates and mortality did not appear to be significant between the various quartiles.
Conclusions
In our study, we observed several differences among patients with cancer receiving care at hospitals with different admission rates. These included patients’ education level, mode of arrival, and whether they had received recent chemotherapy. Emergency Severity Index (ESI) score may have also contributed to admission rate variability. Further study into unmeasured factors influencing hospital admissions, such as local culture, resources, and pathways, could identify generalizable findings to reduce avoidable admissions and reduce variation among similar patients in different hospitals.
Publisher
Springer Science and Business Media LLC
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