Factors associated with delays in discharge for trauma patients at an urban county hospital - Cai, Christopher (Trauma Surg Acute Care Open, 2020)
BACKGROUND: Discharge delays for non-medical reasons put patients at unnecessary risk for hospital-acquired infections, lead to loss of revenue for hospitals and reduce hospital capacity to treat other patients. The objective of this study was to determine prevalence of, and patient characteristics associated with, delays in discharge at an urban county trauma service. METHODS: We performed a retrospective cohort study with data from Zuckerberg San Francisco General Hospital (ZSFGH), a level-1 trauma center and safety net hospital in San Francisco, California. The study included 1720 patients from the trauma surgery service at ZSFGH. A ‘delay in discharge’ was defined as days in the hospital, including an initial overnight stay, after all medical needs had been met. We used logistic and zero-inflated negative binomial regression models to test whether the following factors were associated with prolonged, non-medical length of stay: age, gender, race/ethnicity, housing, disposition location, type of insurance, having a primary care provider, primary language and zip code. RESULTS: Of the 1720 patients, 15% experienced a delay in discharge, for a total of 1147 days (median 1.5 days/patient). The following were statistically significant (p<0.05) predictors of delays in discharge in a multivariable logistic regression model: older age, unhoused status or disposition to home health or postacute care (compared with home discharge) were associated with increased likelihood of delays. Having private insurance or Medicare (compared with public insurance) and discharge against medical advice or absent without leave (compared with home discharge) were associated with reduced likelihood of delays in discharge after all medical needs were met. DISCUSSION: These results suggest that policymakers interested in reducing non-medical hospital stays should focus on addressing structural determinants of health, such as lack of housing, bottlenecks at postacute care disposition destinations and lack of adequate insurance. LEVEL OF EVIDENCE: Epidemiological, Level III