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David H. Smith, PhD, RPh; Jennifer L. Kuntz, PhD; Lynn L. DeBar, PhD, MPH; Jill Mesa; Xiuhai Yang, MS; Jennifer Schneider, MPH; Amanda Petrik, MS; Katherine Reese, PharmD; Lou Ann Thorsness, RPh; David Boardman, MD; and Eric S. Johnson, PhD
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Predicting 30-Day Emergency Department Revisits
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Predicting 30-Day Emergency Department Revisits

Kelly Gao; Gene Pellerin, MD; and Laurence Kaminsky, PhD
“Frequent flyers” significantly contribute to emergency department (ED) crowding. This study developed a predictive model that can be used to identify high-risk patients and reduce ED revisits.

Objectives: To develop a predictive model that hospitals or healthcare systems can use to identify patients at high risk of revisiting the emergency department (ED) within 30 days and thus reduce unnecessary ED use through proactive interventions.

Study Design: A retrospective analysis of fiscal years (FYs) 2013 and 2014 data from 4 Veterans Affairs hospitals in upstate New York.

Methods: This study developed a predictive model based on administrative data, a publicly available patient classification system, and logistic regression. The study data were from 4 Veterans Affairs hospitals in upstate New York; FY 2013 data were used to predict 30-day revisits in FY 2014. All 22,734 patients with ED visits were included in the analysis. The predictive variables were patient demographics, prior-year utilization, and comorbidities. To prevent overfitting, we validated the model by the split-sample method. The predictive power of the model is measured by C statistics.

Results: In the first model using only patient demographics, the C statistics were 0.568 (95% CI, 0.555-0.580) and 0.556 (95% CI, 0.543-0.568) for the development and validation samples, respectively. In the second model with prior-year utilization added, the C statistics were 0.748 (95% CI, 0.737-0.759) for both samples. In the final model with comorbidities added, the C statistics reached 0.773 (95% CI, 0.762-0.784) and 0.763 (95% CI, 0.753-0.774) for the development and validation samples, respectively.

Conclusions: The predictive model we developed in this study is straightforward to implement and offers significantly higher predictive power than other models reported in the literature. Hospitals and healthcare systems can use it to identify high-risk “frequent flyers” for early interventions to reduce ED revisits.

Am J Manag Care. 2018;24(11):e358-e364
Takeaway Points
  • Emergency department (ED) crowding results in treatment delays and excessive mortality.
  • ED revisits constitute a key factor of ED crowding.
  • Given limited resources, an effective predictive model is needed to identify high-risk patients for proactive interventions to reduce ED revisits.
  • The model developed in this study is straightforward to implement by hospitals and payers (eg, health plans, Medicaid, and governmental programs abroad) and offers higher predictive powers compared with other models reported in the literature.
Providing care in the emergency department (ED) is expensive. The average cost of an ED visit is estimated at $1038 compared with $176 for a primary care visit in the United States.1 Moreover, the number of ED visits grew at roughly twice the population growth rate between 2001 and 2008.2 The CDC reported that the number of ED visits increased from 119.2 million (40.5 visits per 100 persons) in 2006 to 136.3 million (44.5 visits per 100 persons) in 2011.3,4 Furthermore, studies have revealed that “frequent-flyer” patients constitute a key factor of ED crowding, which results in treatment delays and excessive mortality.5,6 Thus, given limited resources, reducing repeated visits is warranted to improve ED effectiveness and timeliness for those truly in need.

To develop interventions to reduce ED revisits, reliable predictive modeling that can identify high-risk patients is desired. However, compared with inpatient 30-day readmissions, which have been measured by CMS since 2012 to adjust payments to hospitals,7 ED revisits have received less attention.8 Although substantial published literature has examined factors influencing ED revisits, research on predictive models is limited.9-22 As a result, the predictive models for ED revisits published in the literature have only moderate predictive power: The highest C statistics reached 0.70 for 30-day revisits2,22 and 0.73 for 6-month revisits.23

In the present study, we developed a statistical model that predicts the risk of revisiting the ED within 30 days of discharge. The model can be used to identify high-risk frequent flyers for proactive intervention. With rapid adoption and use of health information technology and especially electronic health records (EHRs), administrative data are coming increasingly closer to real time and offer greater potential for improving ED care. Our model, based on administrative data and a publicly available patient classification system, can be readily implemented in health systems to reduce unnecessary ED revisits.


Data Source and Study Variables

ED visit data from fiscal years (FYs) 2013 and 2014 in Veterans Healthcare Network Upstate New York (VISN 2 Upstate) were analyzed in this study. VISN 2 is 1 of the 18 networks through which the US Department of Veterans Affairs (VA) delivers care to more than 6 million patients annually. VISN 2 Upstate, with 5 medical centers and 31 outpatient clinics across upstate New York, serves approximately 140,000 patients with an annual budget of $1 billion (starting in FY 2016, VISN 2 was restructured to include New York downstate VA hospitals). In FY 2014 for VISN 2 Upstate, a total of 21,141 patients had ED visits in the 4 medical centers that provided ED services.

We used the VA National Patient Care Database (NPCD) hosted at the VA Information and Computing Infrastructure as the primary data source for this study. The Outpatient Care File (OPC) and clinical stop code 130 were used to identify index ED visits and revisits. In addition to encounter information, such as visit dates and International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes, OPC also contains patient demographic and socioeconomic information, such as age, gender, race, and income. Data in NPCD, including OPC, are routinely used in VA operational analysis and research. Most of the data fields, such as visit dates and clinical information like ICD-9-CM codes, are regularly and rigorously validated with strict business rules. The patient income information is means-tested. One exception is that its race information is often incomplete because the VA health system does not mandate veterans to report race. However, for the last several years, the VA has systematically gathered racial and ethnic information from other data sources, such as Medicare and the Department of Defense; as a result, the updated racial and ethnic data are deemed accurate and reliable.24,25

We also used Managerial Cost Accounting (MCA; formerly Decision Support System) files that contain actual patient care costs, rather than amounts claimed or paid as in private health plans. MCA costs are the primary financial data for internal operations and Congressional inquiries. For case mix or comorbidities, we used a publicly available and widely used system, Clinical Classifications Software (CCS), developed by the Agency for Healthcare Research and Quality (AHRQ),26 which classifies patients into 285 homogeneous groups based on ICD-9-CM codes.

The dependent variable in this study was dichotomous (yes = 1, no = 0), indicating whether a patient had any ED revisit(s) within 30 days after being discharged from the ED in FY 2014. The explanatory or predictive variables used in this study were from FY 2013 and can be grouped into 4 categories: (1) demographics (age, sex, marital status, race, disability rating, and period of military service); (2) socioeconomic variables (patient income, homeless [yes = 1, no = 0], and patient insurance status [ie, not covered by any insurance (yes = 1, no = 0), enrolled in Medicare (yes = 1, no = 0), enrolled in Medicaid (yes = 1, no = 0), and covered by private insurance (yes = 1, no = 0)]); (3) prior-year utilization and cost (ED revisit within 30 days [yes = 1, no = 0], number of ED revisits within 30 days, total number of ED visits, number of primary care visits, number of telehealth encounters, total outpatient visits, number of hospitalizations, and total cost); and (4) patient risk or comorbidities (285 clinically homogeneous groups produced by CCS, which is developed by AHRQ26).

The present study did not need or use any identifiable patient private information and therefore had expedited institutional review board review.

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