Predicting Avoidable Emergency Department Visits Using the NHAMCS Dataset

Yuyang Yang, Jingzhi Yu, Songzi Liu, Hanyin Wang, Scott Dresden, Yuan Luo

Research output: Contribution to journalArticlepeer-review

Abstract

Despite the important role avoidable emergency department (ED) visits play in healthcare costs and quality of care, there has been little work in development of predictive models to identify patients likely to present with an avoidable ED visit. We use a conservative definition of 'avoidable' ED visits defined as visits that do not require diagnostic or screening services, procedures, or medications, and were discharged home to classify visits as avoidable. Models trained using data from emergency departments across the US yielded a training AUC of 0.723 and a testing AUC of 0.703. Models trained using the full dataset were tested on demographic groups (race, gender, insurance status), finding comparable performance between white/black patients and male/female with reductions in performance in Hispanic populations and patients with Medicaid. Predictors strongly associated with non-avoidable ED visits included increased age, increasing number of total chronic diseases, and general as well as digestive symptoms. Reasons for visit stated as injuries and psychiatric symptoms influenced the model to predict an avoidable visit.

Original languageEnglish (US)
Pages (from-to)514-523
Number of pages10
JournalAMIA ... Annual Symposium proceedings. AMIA Symposium
Volume2022
StatePublished - 2022

ASJC Scopus subject areas

  • Medicine(all)

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