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Risk Stratification for Return Emergency Department Visits Among High-Risk Patients
Katherine E.M. Miller, MSPH; Wei Duan-Porter, MD, PhD; Karen M. Stechuchak, MS; Elizabeth Mahanna, MPH; Cynthia J. Coffman, PhD; Morris Weinberger, PhD; Courtney Harold Van Houtven, PhD; Eugene Z. Oddone, MD, MHSc; Katina Morris, MS; Kenneth E. Schmader, MD; Cristina C. Hendrix, DNS, GNP-BC; Chad Kessler, MD; and Susan Nicole Hastings, MD, MHSc
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Geographic Variation in Medicare and the Military Healthcare System
Taiwo Adesoye, MD, MPH; Linda G. Kimsey, PhD, MSc; Stuart R. Lipsitz, SCD; Louis L. Nguyen, MD, MBA, MPH; Philip Goodney, MD; Samuel Olaiya, PhD; and Joel S. Weissman, PhD

Risk Stratification for Return Emergency Department Visits Among High-Risk Patients

Katherine E.M. Miller, MSPH; Wei Duan-Porter, MD, PhD; Karen M. Stechuchak, MS; Elizabeth Mahanna, MPH; Cynthia J. Coffman, PhD; Morris Weinberger, PhD; Courtney Harold Van Houtven, PhD; Eugene Z. Oddone, MD, MHSc; Katina Morris, MS; Kenneth E. Schmader, MD; Cristina C. Hendrix, DNS, GNP-BC; Chad Kessler, MD; and Susan Nicole Hastings, MD, MHSc
The authors examined 2 high-risk classification methods to compare and contrast the patient populations, and to identify the preferred method for predicting subsequent emergency department visits.
RESULTS

Study Cohort Characteristics

Fifty percent of participants were African American, and 46% were white. The majority (81%) were exempt from co-payments due to financial need. Moreover, 80% had hypertension, 50% had diabetes, and 28% had IHD. Additionally, 66% had 1 or more mental health conditions: 31% were diagnosed with PTSD, 41% with depression, and 19% with anxiety. This cohort demonstrated a high level of engagement with the VHA in the year prior to their index ED visit, with 49% having had 1 or more mental health outpatient encounters and 93% having had 1 or more outpatient specialty service encounters. Veterans with mental health, outpatient specialty, or primary care use in the year prior to the index ED visit had an average of 11 mental health, 9 specialty, and 5 primary care outpatient clinic encounters for the year, respectively (eAppendix A [eAppendices available at ajmc.com]).

Risk Classification by CAN Score Versus Super User Status

Using McNemar’s test, the rate of classification between the 2 methods was discrepant (P <.0001) (Table 1). Based on a CAN score of ≥90, the rate of high-risk classification was 52.8% (n = 814) versus 19.7% (n = 304) for ED Super Users. Of those with a CAN score of 90 or above (n = 814), 76% were not identified as Super Users. Of Super Users (n = 304), 34.9% had a CAN score greater than 90. The 2 methods identified different sets of patients as high risk. 

Characteristics and Utilization Outcomes by Risk Classification of CAN Score and Super User Status

In general, there were few differences in characteristics across the 4 groups based on cross-classification of CAN score of ≥90 and Super User status (eAppendix A). Significant differences included gender (P <.001), primary care utilization in the year prior to the index ED visit (P <.001), specialty care and mental health care utilization in the year prior to the index ED visit (P <.001), and number of ED visits in the year prior to the index ED visit (P <.05). Patients who were identified as Super Users and had a CAN score below 90 had the highest number of primary care visits in the year prior, and those identified as high risk by both methods had the highest number of mental health visits. There were no significant differences in proportion with chronic conditions, with the exception of anxiety (P <.05).

Repeat ED Visits

Overall, 63% (n = 973) of the cohort had 1 or more repeat ED visits within 90 days after the index ED visit (mean = 1.7 repeat ED visits within the observation period); 90% of which were outpatient ED visits. When examining only repeat outpatient ED visits, 59% (n = 906) of the cohort had more than 1 repeat encounter within 90 days. Veterans with a repeat outpatient ED visit had an average of 1.6 repeat ED visits. Six percent (n = 92) of patients died within 90 days of their index ED visit.

There were no statistically significant differences in the proportion of patients with 1 or more repeat ED visits within 90 days of the index encounter, the number of repeat ED visits, or outpatient ED visits across the 4 risk groups (eAppendix B).

Risk classification of the 4 groups was not associated with repeat ED visits within 90 days of the index encounter (P = .28). Adjustment for covariates yielded similar results. When limiting the outcomes to outpatient ED visits within 90 days, we found similar results (Table 2).

DISCUSSION

In our cohort of VHA ED patients with multimorbidity (defined as 2 or more chronic conditions) and history of a prior ED visit or hospitalization, nearly two-thirds had at least 1 repeat ED visit within 90 days. We found that the CAN score and ED Super User status identified different groups of patients. When we examined 4 risk stratification groups cross classifying by CAN score and Super User status, we found no association between the risk classification groups and repeat ED visits within 90 days of the index date.

Our findings of high repeat ED use in the cohort overall are consistent with previous studies of older veterans, which found that high rates of chronic conditions and prior ED and hospital use were independent predictors of repeat ED use.6,21,22 Compared with the general VHA ED population, of which 15% of patients had a repeat ED visit within 30 days, our cohort had much higher repeat ED utilization.1 Considering these past studies, our cohort was more racially diverse6,23,24 and had more mental health conditions.25 

The fact that these 2 methods of risk stratification did not improve prediction of ED returns in this already high-risk population has important clinical and research implications. First, new applications of existing risk prediction tools should be validated before being put into practice. CAN scores have been available to primary care providers throughout the VA system; however, more data about their clinical utility outside of recognizing patients at high risk for hospitalization and mortality are needed before they are repurposed. Second, although the CAN model, along with other EHR-based methods of risk stratification, incorporates diverse information on patient demographics, medical conditions, and previous utilization, it excludes potentially important data, such as socioeconomic, cultural, and other contextual factors that often play significant roles.26 Third, considering the CAN score is a comprehensive model of health status predictors, improvements in prediction may be achieved through the use of alternative models more frequently seen in other disciplines, such as models based on machine learning techniques.27-30

Although we did not identify subgroups of patients at higher risk of ED returns based on CAN scores, ED Super User status, or a combination of these variables, an examination of the cohort characteristics reveals possibilities for future study into clinical populations of interest and potential ways to improve care. First, we observed high engagement with VHA services, with an average of 5 primary care clinic encounters in the year prior to the index ED visit. Previously, lack of access and low engagement with PCPs and specialists have been highlighted as associated with increased ED use.31-35 Our results suggest that high repeat ED visit rates may not be due exclusively to access barriers; other issues, such as inadequate care coordination, may also play a role.36 If this is the case, increased use of strategies, such as telehealth, may be essential to reducing repeat ED visits. Moreover, the prevalence of mental health diagnoses in our cohort (49%; mean = 11 mental health outpatient encounters in the year prior) was much higher than other studies examining recurring ED use25 and may have contributed to the higher rates of repeat ED visits. Future research should consider the high rates of mental health conditions explicitly. It is notable that the VHA has more extensive and available mental health services compared with many non-VHA systems,36 and interventions directed at non-VHA patients may need to surmount additional barriers to access for mental health services.

Limitations

There are several limitations of this study. First, we identified VHA ED utilization and diagnoses of interest to veterans using VHA administrative claims data, which are subject to coding bias, errors in record keeping, and delayed records of utilization. Second, the single-site cohort potentially limited generalization. Third, we had limited information about other potentially relevant variables related to ED use, such as socioeconomic status data. Fourth, no gold-standard definition of the term "Super User" exists. We examined ED visits over a 9-month period, which does not address the issue of seasonality. We also only examined repeat ED visits at a single time point (within 90 days); findings may have been somewhat different with a longer time horizon (ie, 6, 9, and 12 months). To address these limitations, we extracted data after allowing sufficient time for records to be updated and relied on clinical expertise from senior researchers regarding best practices to determine diagnoses using claims data. We also relied on the literature, clinical expertise, and prior work on the distribution of ED utilization to define Super User status.12 As an exploratory analysis of associations using secondary data, there was no power calculation for this study. However, the percentages of repeat ED use across the different categorizations were similar and generally above 60%. Thus, there were no indications of significant differences we were not powered to detect; the narrow widths of the confidence intervals are reasonable from the logistic models.

In conclusion, among DVAMC users with multimorbidity and more than 2 prior ED visits or hospitalizations, repeat ED use within 90 days was very high. Applying 2 methods of risk stratification in this population identified discrepant groups of patients, and classification of risk by these 2 measures was not associated with repeat ED use within 90 days. Identifying clinically relevant subgroups is important for future interventions to improve care and provide high-value services for high-risk groups defined by multimorbidity and utilization.

Acknowledgments

The study was funded by the Department of Veterans Affairs, Health Services Research and Development Service (IIR 12-052) and was also supported by the Durham VA Center for Health Services Research in Primary Care and Geriatrics Research, Education and Clinical Center. Dr Weinberger is supported by the Research Career Scientist Program (RCS 91-408). The VA Office of Academic Affiliations provided fellowship support for Dr Duan-Porter (No. TPP 21-022). The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official views of the Department of Veterans Affairs.

This article has been corrected in Am J Manag Care. 2019;25(3):140.

Author Affiliations: Health Services Research and Development Service (KEMM, WD-P, KMS, EM, CJC, MW, CHVH, EZO, KM, KES, CCH, SNH), and Geriatric Research, Education, and Clinical Center (KES, CCH, SNH), Durham VA Medical Center, Durham, NC; Department of Medicine, Duke University Medical Center (WD-P, CHVH, EZO, KES, SNH), Durham, NC; Department of Biostatistics and Bioinformatics (CJC), and Center for the Study of Human Aging and Development (KES, CCH, SNH), Duke University, Durham, NC; Department of Health Policy and Management, University of North Carolina at Chapel Hill (MW), Chapel Hill, NC; Duke University School of Nursing (CCH), Durham, NC; Ambulatory Care Service, Durham VA Medical Center (CK), Durham, NC. 

Source of Funding: The study was funded by the Department of Veterans Affairs, Health Services Research and Development Service (IIR 12-052) and was also supported by the Durham VA Center for Health Services Research in Primary Care and Geriatrics Research, Education and Clinical Center. Dr Weinberger is supported by the Research Career Scientist Program (RCS 91-408). The VA Office of Academic Affiliations provided fellowship support for Dr Duan-Porter (No. TPP 21-022).

Author Disclosures: Dr Hastings is a VA employee, and has received grants from the VA Health Services Research and Development Service. 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 (KEMM, WD-P, CJC, MW, EZO, KES, SNH, CH); acquisition of data (KEMM, KM, SNH, EM); analysis and interpretation of data (KEMM, WD-P, KMS, CJC, MW, CHVH, EZO, KES, SNH, CCH); drafting of the manuscript (KEMM, WD-P, CJC, MW, KES, SNH, CCH); critical revision of the manuscript for important intellectual content (KEMM,WD-P, KMS, CJC, MW, CHVH, EZO, KES, CK, SNH, EM, CH); statistical analysis (KEMM, KMS); provision of patients or study materials (KM); obtaining funding (EZO, CK, SNH); administrative, technical, or logistic support (EZO, KM, CK, EM); and supervision (CHVH, EZO). 

Address Correspondence to: Katherine E.M. Miller, VA Medical Center (152), 508 Fulton St, Durham, NC 27705. E-mail: Katherine.miller9@va.gov.
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35. Byrne M, Murphy AW, Plunkett PK, McGee HM, Murray A, Bury G. Frequent attenders to an emergency department: a study of primary health care use, medical profile, and psychosocial characteristics. Ann Emerg Med. 2003;41(3):309-318.

36. Shekelle PG, Asch S, Glassman P, Matula S, Trivedi A, Miake-Lye I. Comparison of quality of care in VA and non-VA settings: a systematic review. Department of Veterans Affairs website. https://www.hsrd.research.va.gov/publications/esp/quality.pdf. Published September, 2010. Access date: February 2, 2016.
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