Article, Emergency Medicine

Distinct subgroups of emergency department frequent users: A latent class analysis

a b s t r a c t

Background: Emergency department (ED) frequent users have high resource utilization and Associated costs. Many interventions have been designed to reduce utilization, but few have proved effective. This may be because this group is more heterogeneous than initially assumed, limiting the effectiveness of targeted interventions. The purpose of this study was to identify and describe distinct subgroups of ED frequent users and to estimate costs to provide hospital-based care to each group.

Methods: Latent class analysis was used to identify homogeneous subgroups of ED frequent users. ED fre- quent users (n = 5731) from a single urban tertiary hospital-based ED and level 1 trauma center in 2014 were included. Descriptive statistics (counts and percentages) are described to characterize subgroups. A cost analysis was performed to examine differences in direct Medical costs between subgroups from the healthcare provider perspective.

Results: Four subgroups were identified and characterized: Short-term ED Frequent Users, Heart-related ED Frequent Users, Long-term ED Frequent Users, and Minor Care ED Frequent Users. The Heart-related group had the largest per person costs and the Long-term group had the largest total group costs.

Conclusion: Distinct subgroups of ED frequent users were identified and described using a statistically objective method. This taxonomy of ED frequent users allows Healthcare organizations to tailor interven- tions to specific subgroups of ED frequent users who can be targeted with tailored interventions. Cost data suggest intervention for long-term ED frequent users offers the greatest cost-avoidance benefit from a hospital perspective.

(C) 2019

Introduction

Emergency department (ED) frequent users, are of great interest to health care organizations, insurers, and researchers alike as they account for a disproportionately large share of overall visits and costs [1]. ED frequent users are traditionally defined as those patients who make four or more ED visits in a 12-month period

Abbreviations: ED, emergency department; LCA, latent class analysis; df, degrees of freedom; G2, G-squared; SES, socioeconomic status; Adm, admission; IP, inpatient; Obs, observation.

q Presentations: Academy Health Annual Research Meeting, 2017, New Orleans, LA, Ohio Public Health Association, 2017, Columbus, OH.

* Corresponding author at: Kent State University, College of Public Health, 800

Hilltop Drive, Moulton Hall, P.O. Box 5190, Kent, OH 44242, USA.

E-mail address: [email protected] (L.E. Birmingham).

[2,3]. Overall, it is estimated that ED frequent users account for approximately 4.5-8% of the ED-using population and make 21- 28% of all ED visits in the United States [2]. Per person yearly expenses for ED frequent users versus non-Frequent ED users vary substantially with an ED frequent user accruing $89,033 in costs on average, compared $5700 for a non-frequent ED user [4]. As a result, impacting the health and utilization behaviors of the ED fre- quent user population poses a significant opportunity to improve health while simultaneously reducing Healthcare costs.

Growing healthcare costs paired with increasing numbers of ED frequent users highlights the critical need to intervene with this population [5]. However, randomized controlled trials have demonstrated limited success in reducing utilization and health- care costs for ED frequent users [6,7]. One reason for the limited success found in prior interventions may be the lack of effective

https://doi.org/10.1016/j.ajem.2019.04.029

0735-6757/(C) 2019

tailoring that can be done when a heterogeneous population is tar- geted with a universal intervention [8]. For example, many patients present with injuries from a fall, but interventions for a 25-year-old with a broken wrist from a fall from alcohol abuse would be different than those for a 75-year-old with a fall due to medication interactions. Program evaluation literature supports the use of targeted interventions over broad-based strategies [9,10]. Furthermore, recent work has demonstrated significant cost-savings associated with tailored interventions based on risk- stratified groups of patients with repeat hospitalizations [11]. We suspect that significant changes in utilization in the entire popula- tion of ED frequent users may require more than one type of inter- vention to create change, however the subgroups to target are not yet well defined [12-14].

The purpose of this research was to identify and describe dis- tinct subgroups of ED frequent users using an objective statistical methodology. The results of this analysis serve as a logical next step in the development of tailored interventions that could be tar- geted to the unique needs of clinically relevant subgroups of fre- quent ED users. Additionally, this analysis presents data to inform decisions about which subgroup would be most optimal to target from a healthcare costs perspective.

Materials and methods

This was a retrospective cross-sectional analysis of ED visitors presenting to a large adult-only Midwestern hospital-based emer- gency department with approximately 100,000 ED visits per year and an associated level I trauma center.

Data sources

Multiple internal data systems were used to conduct the analy- sis including the hospital electronic medical record, a quality improvement database, and the hospital accounting database that contained billing information. Data from calendar year 2014 was used in the primary analysis and data from 2015 was used to iden- tify ED frequent users who demonstrated persistent frequent use in 2015. ED frequent users were identified if they made four or more ED visits in a 12-month period. Individuals younger than 18 years of age were excluded from the analysis.

Approximately 30 variables were included for assessment in the statistical modeling procedure to support the development of a robust model. Variables included Diagnostic categories summariz- ing clinical reasons for ED use, measures of ED and inpatient uti- lization (including indicators of very high-frequency use), 30-day readmissions, making an ED visit for a mental health (ICD-9: 295-299, 300.4, 309.1, 311) or substance use disorder (ICD-9: 291.xx, 292.xx, 303.xx, 304.xx, 305.xx), Charlson Comorbidity Index, demographic indicators (age, sex, race/ethnicity), as well as measures of socioeconomic status (payer, median family income in zip code).

Statistical analysis

Latent class analysis (LCA) was used to identify subgroups of ED frequent users. LCA is an objective statistical methodology that identifies homogeneous subgroups of individuals termed ”classes”. This methodology has been used to create meaningful subgroup- ings of adults in health-related areas of study including alcohol dependency [15], drug adherence [16], and cigarette smoking [17]. SAS version 9.3 (Cary, NC) was used to conduct the data anal- ysis with PROC LCA which was downloaded from The Methodology Center (University of Pennsylvania). The study was approved by the hospital’s Institutional Review Board.

A cost analysis was performed to determine the Total costs of providing care from the hospital perspective. Direct Medical costs for providing care were tabulated for each identified subgroup of ED frequent users. Both fixed and variable direct medical costs incurred during the emergency department visit and subsequent hospital encounter (if admitted) were included in the cost analysis.

Results

A total of 123,594 ED visits were made by 70,959 patients in 2014 to the hospital where the study was conducted. Subse- quently, 5814 people were identified as ED frequent users. Eighty-three (83) patients were excluded because they were

<18 years of age. The remaining 5731 individuals were included for analysis. Table 1 depicts characteristics of the ED frequent user population. Patient demographics showed that most patients were white (61.6%), female (60.0%), and publicly insured (73.3%), which is reflective of the total ED-using population at this site, and is also consistent with prior studies describing ED frequent users [2]. Data indicate that few patients exhibited chronic frequent ED use, defined as being an ED frequent user in both 2014 and 2015 (29.8%).

Latent class analysis models

Selecting variables for inclusion in the LCA model was com- pleted prior to fitting the number of classes, as is a best practice in LCA modeling [18]. Several models were tested iteratively using a diverse array of characteristics including diagnostic categories, rates of healthcare system utilization, chronic Disease burden, as well as demographic and socioeconomic status measures. Vari- ables were kept in the model if they demonstrated latent class sep- aration as denoted by the item-response probabilities. The final model was determined based on several model fit criteria: G- squared (G2) being greater than the degrees of freedom (df), max- imizing entropy, and minimizing the information criterion statis- tics. Additionally, clinical applicability of a variable, or the ability of a clinician to determine which resources would be helpful to a patient based on the variable itself at the time of presentation, was also considered. The models were reviewed and grounded in this fashion by practicing Emergency Medicine physicians. The final model included six variables: having a majority of 2014 ED visits be for a (1) respiratory, (2) mental health, (3) circulatory, or (4) musculoskeletal condition, as well as (5) lower rate of hospi- tal admission following an ED visit (<50% of ED visits resulted in inpatient (IP) admission), and (6) being a ED frequent user in both 2014 and 2015.

Once the final model was selected, the number of subgroups, or classes, was identified. A 4-class model optimized the model fit cri- teria outlined above. Patient gender was tested as a grouping vari- able in the final 4-class model to determine if measurement invariance would hold across the latent classes. Gender was evalu- ated as a grouping variable because it has been shown that men and women use the ED at different rates [19], and there are docu- mented gender inequalities in emergency medicine care [20]. The statistical test for evaluating gender as a grouping variable failed to be rejected, and thus, the assumption of the model’s ability to produce similar groupings for both men and women held.

Several covariates were evaluated. Age, socioeconomic status (SES, as measured by the median income in zip code being above 200% of the federal poverty level), and very high ED utilization (defined by having 10+ ED visits in 2014) were tested as covariates. Only SES was found to be significantly associated with class mem- bership. This analysis detected that there were lower odds of mem- bership in Class 2 (Heart-related) and Class 3 (Long-term) (43%

Table 1

ED frequent user characteristics.

Mean

SD

Median

Min

Max

IQR

Number of ED visits

6.24

4.13

5

4

89

4-7

Number of inpatient & observation Visits

1.46

2.07

0

0

18

0-2

Number of 30-day readmissions

0.51

1.04

0

0

12

0-1

Median income for zip code

$40,489

$15,384

$36,270

$13,015

$112,530

$30,958-46,909

Age (years)

46.89

19.69

44

18

101

44-60

Number

Percent

Race

Black

2047

35.7%

White

3531

61.6%

Other

153

2.7%

Sex

Male

2297

40.0%

Female

3434

60.0%

Payer category

Medicare

1696

29.6%

Medicaid

2504

43.7%

Commercial (private)

769

13.4%

Self-pay

762

13.3%

Chronic ED frequent user

Yes

1709

29.8%

No

4022

70.2%

High inpatient (IP) and observation (Obs) visitors

Made 4 + IP/Obs

837

14.6%

Made <4 IP/Obs

4894

85.4%

High frequency user

Made 10 + ED

1073

18.7%

Made <10 ED

4658

81.3%

ED admission (adm) percentage

<50% of ED visits results in IP adm

4370

76.3%

50%+ of ED visits result in IP adm

1361

23.8%

30-day readmission category

0 readmissions

1915

70.6%

1 30-day readmission

479

17.7%

2-3 30-day readmissions

251

9.3%

4+ 30-day readmissions

66

2.4%

Charlson comorbidity index

Score = 0

5556

96.9%

Score >= 1

175

3.1%

ED visit for substance abuse disorder

Yes

407

7.1%

No

5324

92.9%

ED visit for mental health disorder

Yes

506

8.8%

No

5225

91.2%

Chronic disease categories

0 chronic diseases

3069

53.6%

1-2 chronic diseases

1873

32.7%

3-5 chronic diseases

720

12.6%

6+ chronic diseases

69

1.2%

Table 2

Final model, adjusted latent class analysis class membership & item-response probabilities.

Class #

Membership probability (standard error)

Class 1 – Short-term 40.31% (0.1677)

Class 2 – Heart related 4.16% (0.0038)

Class 3 – Long-term 35.46% (0.0581)

Class 4 – Minor care 20.07% (0.1428)

Respiratory diagnosis

9.61% (0.0299)

0.01% (0.0006)

10.57% (0.0105)

0.01% (0.0004)

Mental health diagnosis

4.07% (0.0164)

0.01% (0.0005)

10.65% (0.0105)

0.00% (0.0004)

Musculoskeletal diagnosis

0.01% (0.0004)

0.01% (0.0006)

1.12% (0.0070)

33.05% (0.2374)

Circulatory diagnosis

0.67% (0.0051)

97.83% (0.0443)

0.00% (0.0002)

0.00% (0.0003)

Low ED admission (admitted after <50% of ED visits)

93.56% (0.0379)

20.36% (0.0460)

52.17% (0.0532)

95.62% (0.0204)

Chronic ED frequent user

4.26% (0.1097)

50.33% (0.0397)

61.75% (0.0546)

20.49% (0.0278)

(95% confidence interval (CI): 0.40-0.83) and 47% (95% CI: 0.41- 0.67) respectively) compared to Class 1 (Short-term) for an individ- ual with an income below 200% of the federal poverty line. Class 4 (Minor care) did not differ significantly from Class 1 (Short-term). Given the statistical significance of the SES covariate, SES was included in the final model.

The final model is displayed in Table 2. Class 1 (Short-term) members made up 40.31% of the population and were unlikely to be admitted to the hospital for an inpatient stay following an ED visit. Very few were predicted to be chronic ED frequent users (4.26%). Class 2 (Heart-related) members were primarily character- ized by most of their visits being for circulatory disorders. Class 3

Table 3

Descriptive statistics of latent classes.

Short-term (n = 3383)

Heart-related (n = 249)

Long-term (n = 1713)

Minor care (n = 386)

Average age

43

70

52

47

% female

57%

59%

67%

56%

% White

63%

62%

58%

66%

% Medicaid

41%

46%

50%

39%

% Medicare

31%

33%

27%

29%

% Medicare and less than age 65

10%

13%

15%

9%

Median count of chronic diseases

0

1

0

0

Median number of inpatient visits

0

3

1

0

Average percent of ED visits that result in inpatient admission

14%

66%

41%

9%

Chronic ED frequent

0%

47%

89%

19%

Majority of ED visits in 2014 were for Mental disorders

4%

0%

10%

0%

Majority of ED visits in 2014 were for diseases of the circulatory system

0%

100%

0%

0%

Majority of ED visits in 2014 were for diseases of the musculoskeletal system

0%

0%

1.0%

100%

and connective tissue

(Long-term) members were defined by the high probability of being a chronic ED frequent user (61.75%). Class 4 (Minor care) members had a very high probability of not having ED visits result in inpatient admission (95.62%). One-third of their visits were pre- dicted to be for musculoskeletal conditions (33.05%).

Following final model selection and adjustment for SES, individ- uals were assigned to the class where they had the highest proba- bility of class membership, as is standard practice in latent class analysis [18]. Descriptive statistics for the latent classes are pre- sented in Table 3. Each subgroup was assigned a descriptive group name based on the group characteristics. Several Emergency Med- icine physicians assisted with the naming process to ensure the names were clinically relevant and descriptive.

The first group, named ”Short-term Frequent ED Users”, was the youngest group on average (average age 43). This group presented for a variety of conditions without any one diagnostic group being especially prevalent. Most notably, none (0%) of the group mem- bers were chronic frequent ED users.

The second group termed ”Heart-related Frequent ED Users” was primarily defined by the fact that all group members (100%) made most of their visits to the ED for heart-related conditions. Higher inpatient utilization was observed in this group with a median of 3 inpatient visits during 2014, and 66% of ED visits resulting in an inpatient admission.

The third group, ”Long-term Frequent ED Users” was primarily characterized by the high prevalence of chronic ED frequent users (89% of group members). This group had the highest proportion of females (67%) and group members insured by Medicaid (50%) and Medicare while being <65 years of age (15%). On average, 41% of ED visits resulted in an inpatient admission for this group. This group demonstrated the highest percentage of people making a majority of 2014 ED visits for mental health diagnoses (10%).

Lastly, the fourth group was named ”Minor Care Frequent ED Users”. This group had the lowest average percentage of ED visits result in an inpatient admission (9%). Across the groups, this group was the least likely to have members insured by Medicaid (39%) and Medicare while being <65 years of age (9%). All members (100%) had visits that resulted in musculoskeletal discharge diag- noses which are inclusive of many Diagnostic codes for sprains and strains.

Cost analysis

A cost analysis was conducted to determine which group would generate the greatest cost-saving opportunity if offered an effec- tive intervention to reduce utilization. As shown in Table 4, the Long-term Frequent ED Users had the largest total cost ($4,501,810) but the Heart-related Frequent ED Users had the lar- gest per-person cost ($5609). The Short-term ED Frequent User group was the largest in terms of people (3383 members) but had lower total and per-person costs relative to other groups of ED frequent users.

Discussion

This analysis used an objective statistical methodology to iden- tify four unique latent classes, or subgroups, of ED frequent users– a Short-term, Long-term, Heart-related, and Minor Care ED Frequent User groups. The Heart-related group was the smallest group, and tended to be older, frequently discharged for heart-related condi- tions, and had the highest rate of ED visits result in inpatient or observation hospital admissions. Members of this group had the highest average cost per person. This echoes the focus by the Centers for Medicare and Medicaid Services on reducing readmissions for patients with heart failure. Given the small size of the Heart- related group (249 patients), Effective interventions could lead to relatively fast cost-avoidance benefits. However, given the advanced age and medical complexity of this group, some level of ED and inpa- tient hospital utilization will likely be unavoidable.

The Minor Care ED Frequent User group presented most often for musculoskeletal complaints such as sprains and strains and was rarely admitted for inpatient or observation admission. This group has the lowest costs (total and per person) and had a low percentage of chronic ED frequent users (19%) suggesting that most of these patients resolve on their own within 12-months, as is generally true of most ED frequent users [12,21]. As such, these patients may not be an optimal group to target with intervention given their relatively low costs and high prevalence of self- resolution in a Short period of time. This contrasts with recent poli- cies by insurance companies focused on reducing low acuity ED

Table 4

Cost analysis of ED frequent user subgroups.

Short-term frequent ED users

Heart-related frequent ED users

Long-term frequent ED users

Minor care frequent ED users

Count of patients

3383

249

1713

386

Sum of full costs

$3,879,519

$1,273,288

$4,501,810

$344,903

Average full costs

$1196

$5609

$2807

$922

utilization to reduce healthcare expenditures. These data clearly demonstrate that intervening with this group offers the smallest cost-avoidance benefit.

Similarly, the Short-term group exhibited low per person costs, similar to the Minor Care group. However, as the largest group (3383 patients) it had the highest total costs. These individuals were primarily characterized by a complete lack of chronic ED fre- quent users in the group (0%), suggesting that intervention with this group may not be a good use of resources as they appear to naturally resolve on their own within one year. In sum, these data suggest that intervening with groups other than those presenting for minor illness or short-term bouts of frequent ED use will offer greater cost-avoidance benefits.

Conversely the Long-term ED Frequent User group was primar- ily characterized by a high frequency of persistent ED use, and encompassed the highest percentage of ED frequent users who made the majority of their ED visits for mental health disorders. Furthermore, the Long-term group was the highest total cost group, and exhibited the second highest per-person costs. This combination of chronic use and high costs suggests that it would be the optimal subgroup to target with intervention(s). This is con- sistent with conclusions drawn by Smulowitz et al. in their analysis of ED cost-reduction strategies [22].

The present study builds on the body of literature examining characteristics of ED frequent users by determining how character- istics co-occur, and how this information can be used to create tai- lored and targeted interventions. This study takes the next logical step in creating effective interventions for this population. The pre- sent findings can inform future interventions in EDs or elsewhere, by providing information about the unique needs of distinct sub- groups of ED frequent users to assist in the development of tailored and targeted interventions. Recent data shows this strategy is effective. One study demonstrated approximately $26 million in charge avoidance by targeting interventions tailored to distinct needs of super-utilizers [11]. Additionally, this analysis suggests a strategy for determining which group should receive interven- tion to promote cost-effective decision-making. The present analy- sis is the first-step in creating targeted and tailored interventions for ED frequent users.

An additional advantage of the development of a standard typology of ED frequent users is the potential for utilization of pre- dictive models. predictive models could prospectively sort patients into preventative interventions to decrease the likelihood of high ED utilization at the first sign of increased use and could poten- tially be incorporated into existing electronic medical record tech- nology. Based on the current understanding of factors that drive frequent ED use, interventions will likely need to address not only healthcare needs, but also basic daily living, behavioral, and psy- chosocial needs [23]. Such preventative interventions could be aimed at addressing the root cause of frequent ED use (such as underlying health issues, social and environmental problems), rather than the symptom (ED visits).

The present research was conducted at a single adult-only emergency department which could potentially limit the general- izability of the findings. ED utilization outside of the health system where this research was conducted could not be accessed and included in the analysis which may result in underestimation of ED and IP utilization rates. Future research should examine the potential for the existence of a standard typology of ED frequent users in a large national emergency department dataset and exam- ine potential differences between typologies for pediatric and adult populations. Additionally, conducting this style of analysis with more recent data may influence the resulting latent classes since many emergency departments have recently seen large increases in rates of ED visits for opioid-related overdoses [24]. Cost data from a more inclusive perspective, such as the societal or payer

perspective may provide more insight into the cost-savings that could be achieved through effective intervention. The costs included in this analysis were limited to only hospital-based costs, thus the few physicians who were consultants billing separately from the hospital were not included due to this data being unavail- able. This may explain why costs per person reported in the pre- sent analysis are slightly lower than some other estimates [4]. While mortality was not included in the present analysis, future research can examine differing mortality rates across latent classes, and may inform opportunities for early palliative care involvement. Inclusion of additional information on unmet psy- chosocial needs, Social determinants of health, or other con- founders related to population health could increase the usefulness of this type of analysis, as these factors are largely believed to influence emergency department utilization [25-29].

The strength of this study is that few analyses have endeavored to create objectively defined distinct subgroups of ED frequent users, and none to our knowledge have utilized LCA. A few recent studies have analyzed groups of super-utilizers (primarily focused on frequent inpatient hospital utilization) using structural equa- tion modeling techniques [30-32]. These studies of super- utilizers provide some validation of the subgroups identified in the present analysis since they also identified distinct groups with high levels of Cardiac conditions [30,32], and another group with high levels of ED utilization and psychiatric and substance- related disorders [31].

A sizable portion of ED revisits may be driven by factors not readily amenable to modification by EDs as they stand today

[33]; instead pointing to well-recognized fragmentations of the medical system through which patients fail to receive continuous or coordinated medical services, and as a result often end up in the ED. Others have pointed to the need for the ED to have greater involvement in population health interventions like care manage- ment, or involvement with community health agencies that pro- vide care at home, pushing the ED to function beyond its traditional scope [22,34]. In this fashion, the ED has become a cen- ter of care for entire communities, connecting patients at high-risk to community health resources and other agencies.

Conclusions

A greater understanding of subgroups of ED frequent users will be indispensable in helping population health practitioners tailor interventions to the distinct needs of subgroups. Tailored interven- tions have demonstrated greater effectiveness than universal, non- targeted interventions [9-11,35]. This analysis identified four homogeneous groups of ED frequent users that have different needs and user patterns and will likely require different interven- tions or resources. Interventions that are tailored and targeted to distinct homogeneous groups of ED frequent users, such as those identified here, may help drive improved results with respect to costs, utilization, and health status in the frequent ED user popula- tion. The present analysis also provides a framework for those pro- viding financial resources for interventions to consider how they will achieve the greatest cost-savings with frequent ED user interventions.

Declarations of interest

None.

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