Currently Viewing:
The American Journal of Managed Care March 2020
Gender Differences in Newly Separated Veterans’ Use of Healthcare
Laurel A. Copeland, PhD; Erin P. Finley, PhD; Dawne Vogt, PhD; Daniel F. Perkins, PhD; and Yael I. Nillni, PhD
Missing Negative Sign in Results Section of Abstract
Looking Back on the ACA, Looking Forward to Bipartisan Solutions: A Q&A With Rep Frank Pallone Jr
Currently Reading
A Revised Classification Algorithm for Assessing Emergency Department Visit Severity of Populations
Klaus W. Lemke, PhD; Kiemanh Pham, MD, MPH; Debra M. Ravert, MD; and Jonathan P. Weiner, DrPH
Medicare Beneficiaries’ Out-of-Pocket Costs for Commonly Used Generic Drugs, 2009-2017
Patrick Liu, AB; Sanket S. Dhruva, MD, MHS; Nilay D. Shah, PhD; and Joseph S. Ross, MD, MHS
Care Management Reduced Infant Mortality for Medicaid Managed Care Enrollees in Ohio
Alex J. Hollingsworth, PhD; Ashley M. Kranz, PhD; and Deborah Freund, PhD
Payer Effects of Personalized Preventive Care for Patients With Diabetes
Brant Morefield, PhD; Lisa Tomai, MS; Vladislav Slanchev, PhD; and Andrea Klemes, DO
Cost-effectiveness of Diabetes Treatment Sequences to Inform Step Therapy Policies
Anna Hung, PharmD, PhD; Bhavna Jois, BS; Amy Lugo, PharmD; and Julia F. Slejko, PhD
Patient Outcomes Associated With Tailored Hospital Programs for Intellectual Disabilities
Jordan Wirtz, MS-HSM; Sarah H. Ailey, PhD, PHNA-BC, CDDN; Samuel Hohmann, PhD, MS-HSM; and Tricia Johnson, PhD
Validated Prediction of Imminent Risk of Fracture for Older Adults
Richard L. Sheer, BA; Richard L. Barron, MS; Lavanya Sudharshan, MS; and Margaret K. Pasquale, PhD
Medication Nonadherence, Mental Health, Opioid Use, and Inpatient and Emergency Department Use in Super-Utilizers
Satya Surbhi, PhD, MS, BPharm; Ilana Graetz, PhD; Jim Y. Wan, PhD, MPhil; Justin Gatwood, PhD, MPH; and James E. Bailey, MD, MPH

A Revised Classification Algorithm for Assessing Emergency Department Visit Severity of Populations

Klaus W. Lemke, PhD; Kiemanh Pham, MD, MPH; Debra M. Ravert, MD; and Jonathan P. Weiner, DrPH
An updated emergency visit classification tool enables managers to make valid inferences about levels of appropriateness of emergency department utilization and healthcare needs within a population.

Analyses of emergency department (ED) use require visit classification algorithms based on administrative data. Our objectives were to present an expanded and revised version of an existing algorithm and to use this tool to characterize patterns of ED use across US hospitals and within a large sample of health plan enrollees.

Study Design: Observational study using National Hospital Ambulatory Medical Care Survey ED public use files and hospital billing data for a health plan cohort.

Methods: Our Johns Hopkins University (JHU) team classified many uncategorized diagnosis codes into existing New York University Emergency Department Algorithm (NYU-EDA) categories and added 3 severity levels to the injury category. We termed this new algorithm the NYU/JHU-EDA. We then compared visit distributions across these 2 algorithms and 2 other previous revised versions of the NYU-EDA using our 2 data sources.

Results: Applying the newly developed NYU/JHU-EDA, we classified 99% of visits. Based on our analyses, it is evident that an even greater number of US ED visits than categorized by the NYU-EDA are nonemergent. For the first time, we provide a more complete picture of the level of severity among patients treated for injuries within US hospital EDs, with about 86% of such visits being nonsevere. Also, both the original and updated classification tools suggest that, of the 38% of ED visits that are clinically emergent, the majority either do not require ED resources or could have been avoided with better primary care.

Conclusions: The updated NYU/JHU-EDA taxonomy appears to offer cogent retrospective inferences about population-level ED utilization.

Am J Manag Care. 2020;26(3):119-125.
Takeaway Points
  • There is renewed interest in understanding emergency department (ED) use patterns in populations, both because of increased use associated with healthcare reform and as private payers seek to stem their rising ED spending.
  • To assess the appropriateness of ED use at the population level, validated classification methods that use available administrative data will be required.
  • Our analysis using an updated classification suggests that an even greater number of ED visits than previously categorized are nonemergent.
  • Health plans and other organizations might use ED visit classification algorithms to gain an understanding about how populations make use of hospital services.
The New York University Emergency Department Algorithm (NYU-EDA) is widely used to classify emergency department (ED) visits.1,2 This measurement tool’s development occurred in the late 1990s and was based on 5700 ED discharge abstracts from 6 hospitals in the Bronx, a borough of New York City. The NYU-EDA probabilistically classified 659 diagnosis codes from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). The original NYU-EDA mapped only about 5% of all ICD-9-CM diagnosis codes. We propose an algorithm that remedies this shortfall and classifies nearly all ED visits.

The NYU-EDA has been applied in health services research studies to identify emergent visits that required ED care.3,4 Several studies have focused on nonemergent and primary care–treatable ED visits and evaluated emergent and nonemergent utilization patterns to assess the impact of healthcare reforms.5-11 Estimates of proportions of nonemergent visits have ranged between 17% and 49%. One study looked at primary care–sensitive (PCS) visits (ie, emergent visits that are potentially avoidable and nonemergent and primary care–treatable visits) and found that up to 50% of ED visits were PCS in a statewide all-payer claims database with 92% of ED visits classified.12

Evidence for the validity of the NYU-EDA has grown over 2 decades. Emergent visits were associated with total charges and increased likelihood of death and inpatient hospitalization directly from the ED and within 30 days from a previous visit.10,13-15 However, researchers and emergency medicine clinicians have cautioned against using visit classifications based solely on discharge diagnoses for interventions aimed at reducing unnecessary visits or for denying payment. First, underlying differences in morbidity and access to care may, to some degree, account for utilization patterns that would be detected by an ED visit classification algorithm. Second, there are reasons for visits on the individual level that may be appropriate for ED utilization, which can differ from discharge diagnoses that categorize the encounter as nonemergent. For example, patients who are experiencing chest pain and come to the ED for evaluation are not necessarily inappropriately using the ED. ED visit classifications are useful tools for understanding the healthcare needs of populations, not the medical needs of individual patients.16-19

A team of Johns Hopkins University (JHU) emergency medicine physicians and health services researchers has further updated and expanded the NYU-EDA using their best clinical judgment and diagnosis aggregations from the Adjusted Clinical Groups (ACG) System.20 In this revised JHU version of the NYU-EDA (or NYU/JHU-EDA for short) we undertook 3 significant modifications and improvements to the original version and updates undertaken by other teams. First, rather than assigning ICD codes probabilistically, we classify each ED visit into 1 of 11 categories. Second, rather than placing all injuries into 1 category, we subcategorize injuries into 3 severity levels: nonsevere injuries, severe injuries, and severe injuries that are likely to require inpatient admissions. Third, we significantly expand the classification of ICD codes.

In this article, we describe the updated NYU/JHU-EDA, and, using data from a federal survey of US hospital EDs and a large claims database from multiple health plans, we compare results of our revised tool with the original NYU-EDA and 2 earlier modifications developed by Johnston et al and Ballard et al.2,13

The first objective of this article is to offer a description and first-stage assessment of our ED classification algorithm. The second goal is to use this methodology to offer an account of use patterns of American EDs based on a representative sample of patients visiting hospital EDs and a large national sample of health plan enrollees. In addition to describing our new measurement tool, our analysis adds to the literature on how Americans use EDs and will offer insights into how health plans and other organizations might use classification algorithms to gain an understanding of how populations make use of hospital EDs.

Review of Previous Approaches for Classifying ED Visits

The original NYU-EDA first classifies common primary ED discharge diagnoses as having varying probabilities of falling into each of the 4 following categories: (1) nonemergent; (2) emergent, primary care treatable; (3) emergent, ED care needed, and preventable or avoidable with timely and effective ambulatory care; and (4) emergent, ED care needed, and not preventable.1 The original NYU system categorizes certain diagnoses separately and directly into 5 additional categories: injuries, psychiatric conditions, alcohol related, drug related, or unclassified.

The adaptation by Ballard et al sums the NYU-EDA probabilities for nonemergent and emergent primary care–treatable visits and compares this sum with the total probability of the emergent, ED care needed categories.13 Depending on the larger of the 2 resultant likelihoods, visits are classified as nonemergent or emergent, or as intermediate when there is an equal probability of being nonemergent or emergent. The Ballard et al method classifies visits into 1 of 8 categories, which have been shown to be a good predictor of subsequent hospitalization and death within 30 days of an ED visit.13

Copyright AJMC 2006-2020 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
Welcome the the new and improved, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up