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Emergency Department Visit Classification Using the NYU Algorithm | Page 3

Published Online: April 21, 2014
Sabina Ohri Gandhi, PhD; and Lindsay Sabik, PhD
There were a few limitations to our approach. In categorizing visits using the EDA, there may have been some measurement error in determining if a visit is truly emergent. For example, some diagnoses may have been appropriately categorized as emergent, but were not associated with death or hospital admission (eg, a broken leg). We attempted to address this possible issue by excluding ED admissions for injury. Another limitation to our research was that we were only able to observe mortality and hospital admission as a direct admit from the ED, but not able to observe subsequent mortality or hospital admission within a limited time frame after the ED visit. Further, we used a blunt measure of hospital admission in order to directly compare our results with Ballard et al,9 which may not necessarily reflect severity in cases where hospitalizations resulted in shorter lengths of stay or were less acute. Finally, thresholds for hospital admission may have differed by payer type or other subjective patient characteristics. EDs may serve as a gateway through which to admit patients who do not have access to care through other channels due to their insurance status or other socioeconomic factors. This question is beyond the scope of this paper and requires further research.

This study demonstrated that the EDA can be used to identify ED visits associated with mortality and hospitalization. Classifying ED visits as emergent or nonemergent has been a shortcoming of the literature on ED use. The EDA is increasingly being used at the state and local levels20- 23 and, despite its limitations, the EDA has the potential to be a useful tool for understanding patterns of use and assessing the effects of policies and programs aimed at reducing nonemergent ED use. While the developers of the EDA have cautioned that the algorithm would not be appropriate to use for making individual reimbursementbased decisions, and recent research has supported this assessment,24 it can be applied to assess overall trends in ED use and to study how interventions and policies may affect these trends. For example, it has been used by researchers studying how new programs providing primary care for the uninsured affected ED use among these particular patient populations.20,21 In such contexts, where administrative data are available to assess how a program or policy change affected utilization, the EDA can be useful.

With the implementation of health reform, there will be major changes in the number of uninsured, the distribution of insurance coverage types, the payment and organization of healthcare providers, and other aspects of the healthcare system that could affect the way various patient populations utilize the ED. It will be important to have tools to classify ED visits and to test the success of interventions and policies designed to alter ED utilization and improve access to alternative sources of care. We have shown that the conclusions of earlier research validating the EDA in the context of managed care also hold when a nationally representative sample of ED visits is examined, suggesting that the EDA is a useful tool for health services and policy researchers.

Take-Away Points

The New York University Emergency Department (ED) Algorithm is a powerful tool that can be used to classify ED visits.
  • Researchers are in need of methods to categorize ED visits as emergent and nonemergent.

  • Public and private decision makers may use the algorithm to evaluate the impact of policies that alter ED utilization when information on diagnosis codes is available.
Author Affiliations: RTI International, Washington, DC (SOG); Department of Healthcare Policy and Research, Virginia Commonwealth University School of Medicine (LS).

Source of Funding: None reported.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (SOG, LS); acquisition of data (SOG, LS); analysis and interpretation of data (SOG, LS); drafting of the manuscript (SOG, LS); critical revision of the manuscript for important intellectual content (SOG, LS); statistical analysis (SOG, LS).

Address correspondence to: Sabina Ohri Gandhi, PhD, RTI International, 701 13th St NW, Ste 750, Washington, DC 20005-3967. E-mail: sgandhi@rti.org.
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Issue: April 2014
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