Oluwaseun John Adeyemi1, Shelby Veri2. 1. Department of Public Health, University of North Carolina at Charlotte, North Carolina, 28223, USA. 2. Department of Health Services Research, University of North Carolina at Charlotte, North Carolina, 28223, USA.
Abstract
BACKGROUND: Leaving against medical advice (AMA) is associated with increased readmission rates, fragmented patient care, and healthcare litigation. Understanding the factors associated with trauma patients leaving AMA from acute care settings will help guide better communication with trauma patients and improve patient satisfaction. This study aims to assess the sociodemographic and in-hospital care characteristics of trauma patients that leave AMA from acute care centers across the U.S. METHODS: We pooled and analyzed eight years of data (2009-2016) from the National Hospital Ambulatory Medical Care Survey. The outcome variable was whether the patient left AMA or not. The main predictors were the triage class, weekend presentation, health insurance status, the presence of chronic diseases, and the receipt of therapeutic and diagnostic procedures. The sociodemographic characteristics -age, sex, and race/ethnicity, were measured as potential confounders in the developed model. We performed logistic regression and reported the unadjusted and adjusted odds of leaving AMA as well as the 95% confidence intervals. RESULTS: The weighted percent of the trauma patient population that left AMA was 1.8%. The odds of leaving AMA decreased with advancing age, and increased among non-Hispanic Blacks, compared with non-Hispanic Whites. After adjusting for age, race, and gender, the odds of leaving AMA increased among patients that lacked health insurance (AOR: 1.86; 95% CI: 1.51-2.31), and had diagnostic procedures (AOR: 2.79; 95% CI: 2.32-3.36). The odds of leaving AMA reduced among trauma patients who were classified as emergent (AOR: 0.70; 95% CI: 0.50-0.98) and had therapeutic procedures (AOR: 0.39; 95% CI: 0.32-0.47). CONCLUSION: Predicting trauma patients with increased odds of leaving AMA will inform intentional communication that may reduce leaving AMA rates and improve care.
BACKGROUND: Leaving against medical advice (AMA) is associated with increased readmission rates, fragmented patient care, and healthcare litigation. Understanding the factors associated with trauma patients leaving AMA from acute care settings will help guide better communication with trauma patients and improve patient satisfaction. This study aims to assess the sociodemographic and in-hospital care characteristics of trauma patients that leave AMA from acute care centers across the U.S. METHODS: We pooled and analyzed eight years of data (2009-2016) from the National Hospital Ambulatory Medical Care Survey. The outcome variable was whether the patient left AMA or not. The main predictors were the triage class, weekend presentation, health insurance status, the presence of chronic diseases, and the receipt of therapeutic and diagnostic procedures. The sociodemographic characteristics -age, sex, and race/ethnicity, were measured as potential confounders in the developed model. We performed logistic regression and reported the unadjusted and adjusted odds of leaving AMA as well as the 95% confidence intervals. RESULTS: The weighted percent of the trauma patient population that left AMA was 1.8%. The odds of leaving AMA decreased with advancing age, and increased among non-Hispanic Blacks, compared with non-Hispanic Whites. After adjusting for age, race, and gender, the odds of leaving AMA increased among patients that lacked health insurance (AOR: 1.86; 95% CI: 1.51-2.31), and had diagnostic procedures (AOR: 2.79; 95% CI: 2.32-3.36). The odds of leaving AMA reduced among trauma patients who were classified as emergent (AOR: 0.70; 95% CI: 0.50-0.98) and had therapeutic procedures (AOR: 0.39; 95% CI: 0.32-0.47). CONCLUSION: Predicting trauma patients with increased odds of leaving AMA will inform intentional communication that may reduce leaving AMA rates and improve care.
Authors: Krista Haines; Jennifer Freeman; Cory Vastaas; Clay Rust; Christopher Cox; George Kasotakis; Matthew Fuller; Vijay Krishnamoorthy; Michelle Siciliano; Amy Alger; Sean Montgomery; Suresh Agarwal Journal: J Emerg Med Date: 2020-03-12 Impact factor: 1.484
Authors: Allen Kraut; Randy Fransoo; Kendiss Olafson; Clare D Ramsey; Marina Yogendran; Allan Garland Journal: BMC Health Serv Res Date: 2013-10-14 Impact factor: 2.655