Anthony J Scholer1, Omar M Mahmoud2, Debopyria Ghosh3, Jacob Schwartzman1, Mohammed Farooq4, Javier Cabrera5, Robert Wieder6, Nabil R Adam3, Ravi J Chokshi7. 1. Department of Surgery - Rutgers University - New Jersey Medical School, Newark, NJ, United States. 2. Department of Radiation Oncology - Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States; Department of Radiation Oncology - Rutgers University - New Jersey Medical School, Newark, NJ, United States. 3. Rutgers Institute for Data Science, Learning & Applications, and Department of Medicine, New Jersey Medical School, Rutgers University, Newark, NJ, United States. 4. Graduate School of Biomedical Science, Rutgers University, Newark, NJ, United States. 5. Department of Statistics and Biostatistics, Rutgers University, Newark, NJ, United States. 6. Department of Medicine - Rutgers University - New Jersey Medical School, Newark, NJ, United States. 7. Department of Surgery, Division of Surgical Oncology, Rutgers University- New Jersey Medical School, Newark, NJ, United States. Electronic address: chokshrj@njms.rutgers.edu.
Abstract
INTRODUCTION: Due to its increasing incidence and its major contribution to healthcare costs, cancer is a major public health problem in the United States. The impact across different services is not well documented and utilization of emergency departments (ED) by cancer patients is not well characterized. The aim of our study was to identify factors that can be addressed to improve the appropriate delivery of quality cancer care thereby reducing ED utilization, decreasing hospitalizations and reducing the related healthcare costs. METHODS: The New Jersey State Inpatient and Emergency Department Databases were used to identify the primary outcome variables; patient disposition and readmission rates. The independent variables were demographics, payer and clinical characteristics. Multivariable unconditional logistic regression models using clinical and demographic data were used to predict hospital admission or emergency department return. RESULTS: A total of 37,080 emergency department visits were cancer related with the most common diagnosis attributed to lung cancer (30.0%) and the most common presentation was pain. The disposition of patients who visit the ED due to cancer related issues is significantly affected by the factors of race (African American OR=0.6, p value=0.02 and Hispanic OR=0.5, p value=0.02, respectively), age aged 65 to 75years (SNF/ICF OR 2.35, p value=0.00 and Home Healthcare Service OR 5.15, p value=0.01, respectively), number of diagnoses (OR 1.26, p value=0.00), insurance payer (SNF/ICF OR 2.2, p value=0.02 and Home Healthcare Services OR 2.85, p value=0.07, respectively) and type of cancer (breast OR 0.54, p value=0.01, prostate OR 0.56, p value=0.01, uterine OR 0.37, p value=0.02, and other OR 0.62, p value=0.05, respectively). In addition, comorbidities increased the likelihood of death, being transferred to SNF/ICF, or utilization of home healthcare services (OR 1.6, p value=0.00, OR 1.18, p value=0.00, and OR 1.16, p value=0.04, respectively). Readmission is significantly affected by race (American Americans OR 0.41, standard error 0.08, p value=0.001 and Hispanics OR 0.29, standard error 0.11, p value=0.01, respectively), income (Quartile 2 OR 0.98, standard error 0.14, p value 0.01, Quartile 3 OR 1.07, standard error 0.13, p value 0.01, and Quartile 4 OR 0.88, standard error 0.12, p value 0.01, respectively), and type of cancer (prostate OR 0.25, standard error 0.09, p value=0.001). CONCLUSION: Web based symptom questionnaires, patient navigators, end of life nursing and clinical cancer pathways can identify, guide and prompt early initiation of treat before progression of symptoms in cancer patients most likely to visit the ED. Thus, improving cancer patient satisfaction, outcomes and reduce health care costs. Published by Elsevier Ltd.
INTRODUCTION: Due to its increasing incidence and its major contribution to healthcare costs, cancer is a major public health problem in the United States. The impact across different services is not well documented and utilization of emergency departments (ED) by cancerpatients is not well characterized. The aim of our study was to identify factors that can be addressed to improve the appropriate delivery of quality cancer care thereby reducing ED utilization, decreasing hospitalizations and reducing the related healthcare costs. METHODS: The New Jersey State Inpatient and Emergency Department Databases were used to identify the primary outcome variables; patient disposition and readmission rates. The independent variables were demographics, payer and clinical characteristics. Multivariable unconditional logistic regression models using clinical and demographic data were used to predict hospital admission or emergency department return. RESULTS: A total of 37,080 emergency department visits were cancer related with the most common diagnosis attributed to lung cancer (30.0%) and the most common presentation was pain. The disposition of patients who visit the ED due to cancer related issues is significantly affected by the factors of race (African American OR=0.6, p value=0.02 and Hispanic OR=0.5, p value=0.02, respectively), age aged 65 to 75years (SNF/ICF OR 2.35, p value=0.00 and Home Healthcare Service OR 5.15, p value=0.01, respectively), number of diagnoses (OR 1.26, p value=0.00), insurance payer (SNF/ICF OR 2.2, p value=0.02 and Home Healthcare Services OR 2.85, p value=0.07, respectively) and type of cancer (breast OR 0.54, p value=0.01, prostate OR 0.56, p value=0.01, uterine OR 0.37, p value=0.02, and other OR 0.62, p value=0.05, respectively). In addition, comorbidities increased the likelihood of death, being transferred to SNF/ICF, or utilization of home healthcare services (OR 1.6, p value=0.00, OR 1.18, p value=0.00, and OR 1.16, p value=0.04, respectively). Readmission is significantly affected by race (American Americans OR 0.41, standard error 0.08, p value=0.001 and Hispanics OR 0.29, standard error 0.11, p value=0.01, respectively), income (Quartile 2 OR 0.98, standard error 0.14, p value 0.01, Quartile 3 OR 1.07, standard error 0.13, p value 0.01, and Quartile 4 OR 0.88, standard error 0.12, p value 0.01, respectively), and type of cancer (prostate OR 0.25, standard error 0.09, p value=0.001). CONCLUSION: Web based symptom questionnaires, patient navigators, end of life nursing and clinical cancer pathways can identify, guide and prompt early initiation of treat before progression of symptoms in cancerpatients most likely to visit the ED. Thus, improving cancerpatient satisfaction, outcomes and reduce health care costs. Published by Elsevier Ltd.
Authors: Jeffrey M Caterino; David Adler; Danielle D Durham; Sai-Ching Jim Yeung; Matthew F Hudson; Aveh Bastani; Steven L Bernstein; Christopher W Baugh; Christopher J Coyne; Corita R Grudzen; Daniel J Henning; Adam Klotz; Troy E Madsen; Daniel J Pallin; Cielito C Reyes-Gibby; Juan Felipe Rico; Richard J Ryan; Nathan I Shapiro; Robert Swor; Arvind Venkat; Jason Wilson; Charles R Thomas; Jason J Bischof; Gary H Lyman Journal: JAMA Netw Open Date: 2019-03-01
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