a b s t r a c t

Objectives: The effect of the Affordable Care Act on emergency department (ED) high utilizers has not yet been thoroughly studied. We sought to determine the impact of changes in insurance eligibility following the 2014 Medicaid expansion on ED utilization for ambulatory care sensitive conditions (ACSC) by high ED utilizers in an urban safety net hospital.

Methods: High utilizers were defined as patients with >=4 visits in the 6 months before their most recent visit in

the study period (July-December before and after Maryland’s Medicaid expansion in January 2014). A differ- ences-in-differences approach using logistic regression was used to investigate if differences between high and low utilizer cohorts changed from before and after the expansion.

Results: During the study period, 726 (4.1%) out of 17,795 unique patients in 2013 and 380 (2.4%) of 16,458 dur- ing the same period in 2014 were high utilizers (p-value b 0.001). ACSC-associated visit predicted being a high utilizer in 2013 (OR 1.66 (95% CI [1.37, 2.01])) and 2014 (OR 1.65 (95% CI [1.27, 2.15])) but this was not different between years (OR ratio 0.99, 95% CI [0.72, 1.38], p-value 0.97).

Conclusion: Although the proportion of high utilizers decreased significantly after Maryland’s Medicaid expan- sion, ACSC-associated ED visits by high ED utilizers were unaffected.

(C) 2017

Introduction

A number of recent health Policy changes have aimed to decrease in- appropriate emergency department (ED) use [1,2]. One area of concern has been high utilizers of the ED, the so-called “Super Users”, who alleg- edly contribute to higher hospital costs and insurance premiums owing to Uncompensated care, resource utilization, and ED crowding [2,3]. An- ecdotally this population is often described as uninsured, minority, and using (or misusing) the ED for minor complaints better served in a pri- mary care or non-acute care setting [2,3].

At first glance, increased Primary care access via health insurance expansion may be an attractive policy choice to reduce ED over-utiliza- tion [4]. It has been proposed that insurance expansion may decrease ED visits, specifically those by frequent ED utilizers with ambulatory care sensitive conditions (ACSCs). ACSCs are conditions whose appropriate

* Corresponding author at: Department of Emergency Medicine, University of Maryland School of Medicine, 614 Wyeth St., Baltimore, MD 21230, USA.

E-mail address: [email protected] (D.B. Gingold).

management in a Primary care setting would abrogate need for man- agement in an acute care setting such as the ED [5]. Some policy makers have targeted ED over-utilization for ACSCs as one means to reduce costs [1]. It has been hypothesized that ACSC-associated ED visits from frequent ED utilizers may be more sensitive to the effects of insurance expansion than emergent acute care visits [1].

Despite optimism that insurance expansion could reduce ED visits and therefore cost from both high utilizers and ACSC-related visits in general, emerging research has cast doubt on this notion [4,6-9]. It has been reported that high ED utilizers may actually be adequately insured, and more likely to have significant chronic health conditions requiring more frequent acute ED and chronic primary care management than the general population [2,3,8,10]. Some have suggested that in medical- ly underserved areas, primary care access could become more difficult immediately after Medicaid expansion as an already overextended pri- mary care system receives an influx of new patients [7]. It might also in- crease overall ED use as recently insured patients with health needs but no established primary care access may turn to the ED for evaluation and treatment [7].

http://dx.doi.org/10.1016/j.ajem.2017.01.014

0735-6757/(C) 2017

The Affordable Care Act was signed into law on March 23, 2010, and its associated Medicaid expansion was enacted in Maryland on January 1, 2014, in addition to subsidized privatize insurance ex- changes [11,12]. The effects of the ACA on ED visits by high ED utilizers with ACSCs remain unknown. This study aims to characterize ED visits by high ED utilizers and those for ACSCs both before and after the 2014 Medicaid expansion at a public safety net hospital in Maryland. We hypothesize that Medicaid and health exchange expansion will not decrease overall ED utilization, visits by high ED utilizers, or visits for by high utilizers associated with ACSCs.

Methods

Study design

We conducted a retrospective cross-sectional study in adult patients presenting to the emergency department at Prince George’s Hospital Center (PGHC), a public safety-net hospital in Cheverly, Maryland, a suburb of Washington D.C. PGHC is a 300-bed hospital and level 2 trau- ma center with an annual ED volume of approximately 88,000 visits. All parts of the study were reviewed according to the Strengthening the Reporting of Observational Studies in Epidemiology: ‘STROBE’ State- ment [13,14]. Cases occurring between January 1, 2013 and December 31, 2014 were included. The study was approved by the investigative review board at PGHC and the need for patient consent was waived. All patients age >= 18 years were included. Visits that were entered in

error were excluded, and duplicate medical record numbers (MRNs)

were consolidated. Patients were identified as high utilizers if they had >= 4 ED visits in the 6 months immediately preceding the most recent visit for each year [15].

Data collection

The study institution began using the ED information system Picis EDPulseCheck(R) (OptumInsight Inc., Eden Prairie, MN), in February 2013. Cases occurring between February 1, 2013 and December 31, 2014 were identified by querying Picis EDPulseCheck(R). Cases occurring in January 2013 were identified by query of billing software RTI (Reim- bursement Technologies, Inc., Conshohocken, PA). The rollout for ACA Medicaid expansion in Maryland occurred on January 1, 2014. The pre- and post-expansion study periods were July 1-December 31, 2013 and 2014 respectively. This is the 6 months immediately prior to Maryland Medicaid expansion, and then 6-12 months after expansion. The same portion of the calendar year was used to avoid seasonal bias in visits and allow for the initial program enrollment period. Cases were assigned a unique visit number as cross-reference, and then cases were filtered to identify patients with multiple MRNs. Data was then de-identified for the remainder of the analysis.

Information on patient demographics, insurance status, clinical in- formation, final ED diagnosis and ED disposition was collected. Race was collected to assess for race-based disparity in health provision, and was self-identified and then consolidated by the investigators. Missing insurance information was categorized as “Other”. Mean ED length-of-stay was determined by the averaging the length of each ED visits during the 6 months preceding and including the patient’s most recent visit in the calendar year. Likelihood of admission was deter- mined by the number of visits resulting in admission for each patient in the six months preceding and including their most recent visit in the calendar year divided by total number of visits in the same period. Likelihoods of other dispositions (discharge, observation, left prior to treatment complete (LPTC), expired) were calculated similarly. Diagno- sis information was free text and diagnoses indicating an ACSC-related visit were determined by the primary researcher (ICD10 had not yet been implemented), using ACSCs definition similar to Johnson et al.

[1] We grouped diabetic complications together, and also identified psy-

chiatric/behavioral/substance related diagnoses. Angina visits included

those with diagnosis for chest pain as well as ACS. The likelihood for each patient of having an ACSC-related visit was calculated as the num- ber of ACSC-related visits in the study period divided by the total visits in the same period.

Data analysis

All data analysis was performed using SAS University Edition Studio version 3.4 (SAS Institute Inc., Cary, NC). The two tailed Student’s t-test was used to compare continuous variables both be- tween years and between the high and low utilizer groups. A p– value <= 0.05 was considered significant. No Power calculation was

performed as all eligible patients in study period were included in

the convenience sample. p-Value determination incorporated the Satterthwaite approximation to account for unequal variance. Categorical variables were compared using the Pearson ?² test. For selected demographic variables with different measures between high and low utilizers as well as ACSC-visit likelihoods, logistic regression was used to identify odds ratios for high vs. low utilizers within each year using the model high utilizer = variable + year + variable * year. The coefficient for the interaction

term was used to find the ratio of odds ratios comparing 2013 to

2014. This ratio of odds ratios identifies the effect of each variable on the likelihood the patient is a high utilizer changes between be- fore and after Medicaid expansion.

Results

There were 17,795 unique adult patients with visits during the study period in 2013, and 16,458 in 2014. Distribution of visits in six months from most recent visit for patients in each year is shown in Fig. 1. De- scriptive statistics for the cohort of each year are shown in Table 1. There were 726 (4.1%) high utilizer patients in 2013 and 380 (2.3%) in 2014 (p-value b 0.001). In 2014 a higher proportion of all patients in 2014 identified as Hispanic (8.9% vs. 10%, p-value b 0.001) and lower proportion identified as black (78% vs. 75.9%, p-value b 0.001) compared to 2013. Mean ED length-of-stay increased slightly from 5.5 h to 5.8 h (p-value b 0.001). Uninsured and Medicare rates were similar between years, although in 2014 there was a slight increase in the percentage of patients with Medicaid (25.3% vs. 26.4%, p-value 0.02) and decrease in percentage with private insurance (30.6% vs. 26.4%, p-value b 0.001) compared to 2013. A slightly lower percentage of patients were discharged in 2014 (61.6% vs. 59.4%, p-value b 0.001). Following expan- sion in 2014, the likelihood of patients having an ACSC diagnosis de- creased from 15.7% to 14% (p-value b 0.001).

Tables 2 and 3 compare the descriptive statistics of high and low uti- lizers in 2013 and 2014. In 2013 before Medicaid expansion, frequent ED utilizers were more likely than others to be black (86.6% vs. 77.6%, p-value b 0.001), insured with Medicaid (51.4% vs. 24.2%, p-value b 0.001) or Medicare (12.8% vs. 6.8%, p-value b 0.001), have a longer ED length-of-stay (6.0 vs. 5.5 h, p-value b 0.001), be admitted to the hos- pital (37.1% vs. 31.6%, p-value b 0.001) or psychiatric unit (6.1% vs. 3.5%, p-value b 0.001), and have an ACSC diagnosis (22.2% vs. 15.4%, p-value b 0.001). They were less likely to be male (40.8% vs. 45.5%, p-value 0.013), Hispanic (4.1% vs. 9.1%, p-value b 0.001), privately insured

(11% vs. 31.4%, p-value b 0.001), uninsured (17.2% vs. 30%, p-value b 0.001) or discharged (53.5% vs. 62%, p-value b 0.001) (Table 2). There were similar notable differences between cohorts in race, Medicaid, Medicare, private insurance, uninsured rates, and likelihood of admis- sion and ACSC diagnosis in 2014 after Medicaid expansion (Table 3). Table 4 shows odds ratios (OR) associated with being a high utilizer from logistic regression analysis. It compares high utilizers to low uti- lizers within years, but also compares these ORs as a ratio of odds ratios to identify “differences in differences” between high utilizers and low utilizers before and after Medicaid expansion. The OR of high utilizers having an ACSC diagnosis was 1.66 (95% CI [1.37, 2.01]) in 2013 and

Fig. 1. Frequency of patents with N 1 ED visit within 6 months, 2013 and 2014.

1.65 (95% CI [1.27, 2.15]) in 2014. These ORs do not differ significantly (OR ratio 0.99, 95% CI [0.72, 1.38], p-value 0.97). In 2013 high utilizers were more likely to have diabetes and CHF, and in 2014 high utilizers were more likely to have COPD or asthma. In both years they were more likely to have psychiatric disorders compared to low utilizers, but these odds ratios were not significantly different across years (OR ratios not significantly different from 1.0). Again, high utilizers were slightly

less likely to be male. There were no statistically significant differences between ORs for predictors of high utilization between 2013 and 2014, except that having private insurance more strongly predicted high utili- zation in 2014 compared to 2013 (OR 0.4 vs. 0.27, OR ratio 1.47, 95% CI [1.003, 2.16], p-value 0.05). Tables 2 and 3 also show that high utilizers with insurance increased from 11% in 2013 to 12.9% in 2014, while pri- vate insurance among low utilizers fell (31.4% to 27.2%).

Table 1

Demographics of patient population, before and after Medicaid expansion.

Variable Before expansion 2013 After expansion 2014 p

Table 2

Demographics of high vs. low utilizers, 2013 before Medicaid expansion.

(N = 17,795)

(N = 16,458)

value

Male (%) Median age (95% CI)	8057 (45.3) 40.7 (40.5, 41)	7480 (45.5) 41.1 (40.8, 41.3)	0.75 0.1	Variable	Low utilizers (N = 17,069)	High utilizers (N = 726)	p-Value
Race				Male (%)	7761 (45.5)	296 (40.8)	0.013
Black (%)	13,871 (78)	12,492 (75.9)	b0.001	Median age (95% CI)	40.7 (40.5, 41)	41.2 (39.9, 42.4)	0.49
White (%)	1482 (8.3)	1369 (8.3)	0.97	Race
Hispanic (%)	1584 (8.9)	1651 (10.0)	b0.001	Black (%)	13,242 (77.6)	629 (86.6)	b0.001
Asian (%)	99 (0.6)	96 (0.6)	0.74	White (%)	1431 (8.4)	51 (7)	0.19
Other (%)	416 (2.3)	405 (2.5)	0.46	Hispanic (%)	1554 (9.1)	30 (4.1)	b0.001

Mean length of stay (95% CI)	5.5 (5.5, 5.6)	5.8 (5.7, 5.9)	b0.001	Asian (%) Other (%)	97 (0.6) 403 (2.4)	2 (0.3) 13 (1.8)	0.3 0.32
Insurance (most recent visit) Medicaid (%)	4500 (25.3)	4337 (26.4)	0.02	Mean length of stay (95% CI) Insurance (most recent visit)	5.5 (5.5, 5.6)	6 (5.7, 6.2)	b0.001
Medicare (%)	1259 (7.1)	1147 (7.0)	0.7	Medicaid (%)	4127 (24.2)	373 (51.4)	b0.001
Private (%)	5447 (30.6)	4415 (26.8)	b0.001	Medicare (%)	1166 (6.8)	93 (12.8)	b0.001
Uninsured (%)	5240 (29.5)	4875 (29.6)	0.72	Private (%)	5367 (31.4)	80 (11)	b0.001
Other (%)	1349 (7.6)	1684 (10.2)	b0.001	Uninsured (%)	5115 (30)	125 (17.2)	b0.001
High utilizers (%) % likelihood of disposition (95% CI) Discharge (95% CI)	726 (4.1) 61.6 (60.9, 62.3)	380 (2.3) 59.4 (58.7, 60.1)	b0.001 b0.001	Other (%) % likelihood of disposition (95% CI) Discharge (95% CI)	1294 (7.6) 62 (61.3, 62.7)	55 (7.6) 53.6 (51.1, 56.1)	0.996 b0.001
Obs (95% CI)	0.17 (0.11, 0.23)	0.12 (0.07, 0.17)	0.16	Obs (95% CI)	0.17 (0.11, 0.23)	0.26 (0.09, 0.43)	0.33
Admit (95% CI)	31.8 (31.2, 32.5)	32.8 (32.1, 33.5)	0.05	Admit (95% CI)	31.6 (30.9, 32.3)	37.2 (34.6, 39.7)	b0.001
Psych (95% CI)	3.7 (3.5, 4)	4.2 (3.9, 4.5)	0.01	Psych (95% CI)	3.6 (3.4, 3.9)	6.1 (4.9, 7.4)	b0.001
Floor (95% CI)	21.4 (20.9, 22)	22.6 (22, 23.2)	0.01	Floor (95% CI)	21.2 (20.6, 21.8)	26.6 (24.3, 28.9)	b0.001
ICU (95% CI)	4.5 (4.2, 4.7)	4.2 (3.9, 4.5)	0.21	ICU (95% CI)	4.5 (4.2, 4.8)	3.2 (2.4, 4.1)	0.004
Transfer (95% CI)	1.5 (1.3, 1.7)	1.3 (1.1, 1.4)	0.06	Transfer (95% CI)	1.5 (1.3, 1.7)	0.9 (0.4, 1.5)	0.051
OR (95% CI)	0.7 (8.1, 0.1)	0.5 (0.4, 0.6)	0.03	OR (95% CI)	0.7 (8.2, 0.1)	0.2 (0, 0.3)	b0.001
LPTC (95% CI)	5.8 (5.5, 6.1)	7.1 (24.1, 0.2)	b0.001	LPTC (95% CI)	5.7 (5.4, 6)	8.4 (14.5, 0.5)	b0.001
Expired (95% CI)	0.5 (0.4, 0.6)	0.6 (0.5, 0.7)	0.31	Expired (95% CI)	0.5 (0.4, 0.6)	0.1 (0, 0.2)	b0.001
% likelihood of ACSC diagnosis (95% CI)	15.7 (15.2, 16.2)	14 (13.5, 14.6)	b0.001	Likelihood of ACSC diagnosis (95% CI)	15.4 (14.9, 15.9)	22.2 (20.2, 24.2)	b0.001

Table 3

Demographics of high vs. low utilizers, 2014 after Medicaid expansion.

Medicaid expansion across entire states [4,6,7]. We observed a slight in- crease in the percentage of patients insured by Medicaid, a decline in

Variable

Male (%)	7325 (45.6)	155 (40.8)	0.065
Median age (95% CI)	41.1 (40.8, 41.3)	40.3 (38.5, 42.1)	0.39
Race
Black (%)	12,155 (75.6)	337 (88.7)	b 0.001
White (%)	1348 (8.4)	21 (5.5)	0.05
Hispanic (%)	1638 (10.2)	13 (3.4)	b 0.001
Asian (%)	93 (0.6)	3 (0.8)	0.59
Other (%)	15,678 (97.5)	375 (98.7)	0.14
Mean length of stay (95% CI) Insurance (most recent visit)	5.8 (5.7, 5.8)	5.8 (5.5, 6.2)	0.72
Medicaid (%)	4143 (25.8)	194 (51.1)	b 0.001

Low utilizers (N = 16,078)

High utilizers

(N = 380) p-Value

private insurance rates, and a no significant change in uninsured rates. national level data comparing 2013 to 2014 showed modest increases in Medicaid and private coverage, and a decrease in uninsured rates [16,17]. Our study was performed in the year immediately after expan- sion; it is possible future data looking further after Medicaid expansion may show similarly improved trends in uninsured rates at our site.

The percentage of high utilizers within our population was consis- tent with prior reports [2,10,15,18]. Interestingly, both overall number of unique patient visits and the percentage of high utilizer patients de- creased in 2014 following Medicaid expansion. This could be attribut- able to the ACA or due to more local shifts in visits between other area

Medicare (%)	1109 (6.9)	38 (10)	0.02	hospitals. Similar to prior descriptions of high ED utilizers, we found
Private (%)	4366 (27.2)	49 (12.9)	b 0.001	that when compared to low ED utilizers, high utilizers are more likely
Uninsured (%)	4821 (30)	54 (14.2)	b 0.001	to be a racial minority, insured by Medicare or Medicaid, higher acuity

Other (%) 1639 (10.2) 45 (11.8) 0.29

% likelihood of disposition (95% CI)

Discharge (95% CI) 59.6 (58.8, 60.3) 52.1 (48.6, 55.5) b 0.001

Obs (95% CI) 0.12 (0.06, 0.17) 0.15 (-0.03, 0.32) 0.73

Admit (95% CI) 32.7 (32, 33.4) 37.6 (34, 41.1) 0.01

Psych (95% CI) 4.2 (3.9, 4.5) 5.8 (4.1, 7.5) 0.07

Floor (95% CI) 22.4 (21.8, 23.1) 28.1 (24.9, 31.4) b 0.001

ICU (95% CI) 4.2 (3.9, 4.5) 2.8 (1.8, 3.9) 0.01

Transfer (95% CI) 1.3 (1.1, 1.4) 0.7 (0.1, 1.4) 0.1

OR (95% CI) 0.5 (7, 0.1) 0.1 (0, 0.2) b 0.001

LPTC (95% CI) 7.1 (6.7, 7.4) 10.2 (15.8, 0.8) b 0.001

Expired (95% CI) 0.6 (0.5, 0.7) 0.1 (-0.1, 0.2) b 0.001

of complaint, higher severity of diagnosis, and more often require ad- mission [2,3,10,18]. In our population high utilizers were less likely to be male and more likely to have a psychiatric diagnosis than low uti- lizers, although these differences did not reach statistical significance in 2014 due to a smaller cohort. psychiatric visits in our population were approximately 30% substance related, 30% for depression/suicidal ideation, and 20% for psychosis symptoms, with the remainder being non-specific behavioral issues (data not shown). Additionally, high uti- lizers were more likely to visit the ED for an ACSC-related diagnosis

compared to low utilizers, although these findings did not change in the first year following Medicaid expansion.

Likelihood of ACSC diagnosis

(95% CI)

Discussion

Summary

13.9 (13.4, 14.4) 20.4 (17.6, 23.1) b 0.001

Although the overall share of privately insured patients decreased moderately in 2014, the percentage of high utilizer patients with private insurance actually increased (Tables 2, 3 and 4). It is possible that high uti- lizers in our population had a lot to gain by accessing private insurance on subsidized exchanges, also started in Maryland January 2014, and were more likely to interface with healthcare access through frequent healthcare visits. Multiple visits during insurance exchange enrollment periods may have made it more likely that high utilizers of health care

In the immediate aftermath of ACA-associated health insurance ex- pansion, we observed a 7.5% decrease in the number of unique patients visiting the ED in the 6-month study period (Table 1). Total visits be- tween years were essentially the same (51,895 in 2013 and 51,705 in 2014). Other reports have reported an increase in ED visits after

were identified as candidates for private insurance through the new sub- sidized exchanges. Also, high utilizers with multiple co-morbidities may have been more likely to benefit from the ACA’s elimination of pre- existing condition restrictions from private insurers, which was also rolled out in January 2014 [11].

Table 4

Single variable logistic regression: Adjusted Odds Ratios of predictors of being a high utilizer.

2013 2014 Year interaction

Variable

	OR	95% CI			OR	95% CI			OR ratio	95% CI		p value
ACSC visit likelihood	1.66	1.37	2.01		1.65	1.27	2.15		0.99	0.72	1.38	0.97
Dehydration	0.78	0.22	2.79		0.65	0.12	3.49		0.83	0.1	6.81	0.86
PNA	0.91	0.24	3.43		1.9	0.46	7.78		2.1	0.3	14.58	0.45
UTI	1.11	0.59	2.08		0.9	0.33	2.48		0.81	0.25	2.69	0.73
Diabetes	2.47	1.21	5.04		1.44	0.46	4.51		0.58	0.15	2.24	0.43
Hypertension	1.08	0.47	2.49		0.66	0.14	3.14		0.61	0.1	3.57	0.59
CHF	2.88	1.52	5.45		2.16	0.81	5.79		0.75	0.23	2.43	0.63
Angina	1.18	0.79	1.77		0.99	0.51	1.93		0.84	0.39	1.84	0.67
COPD	1.81	0.7	4.71		4.88	1.86	12.83		2.69	0.69	10.49	0.67
Asthma	1.67	0.96	2.9		3.3	1.69	6.42		1.97	0.83	4.69	0.15
Psych/substance abuse	1.8	1.37	2.37		1.68	1.16	2.43		0.932	0.59	1.48	0.12
Other variables
Male	0.83	0.71	0.96	0.82		0.67	1.01	0.997		0.77	1.29	0.98
Black	1.69	1.34	2.13	2.42		1.77	3.31	1.43		0.97	2.12	0.07
Hispanic	0.45	0.3	0.68	0.27		0.15	0.49	0.59		0.28	1.21	0.15
Medicaid	3.31	2.85	3.85	3.01		2.45	3.69	0.91		0.7	1.17	0.45
Medicare	2	1.6	2.51	1.5		1.07	2.11	0.75		0.5	1.13	0.16
Private	0.27	0.21	0.34	0.4		0.29	0.54	1.47		1.003	2.16	0.05
Admit	1.31	1.12	1.54	1.26		1.01	1.57	0.96		0.73	1.26	0.77
Discharge	0.68	0.58	0.8	0.72		0.58	0.89	1.05		0.81	1.37	0.71
LPTC	1.62	1.22	2.17	1.57		1.1	2.24	0.96		0.61	1.53	0.88

ACSC-related visits overall decreased only slightly in the year follow- ing Medicaid expansion. It is possible that six months after Medicaid ex- pansion is not sufficient for outpatient capacity and availability to change appreciably to alter acute care seeking behaviors. High utilizers were more likely than low utilizers to have ACSC-associated visits both before and after Medicaid expansion. They were also more likely to have a psychiatric-related primary diagnosis, highlighting a potentially un- derserved need for effective outpatient behavioral health resources within the community (Table 4). We did not stratify by super-utilizers (N 20 ED visits per year) and high utilizers as in Doupe et al. [15] and Doran et al. [18], as absolute numbers of super-utilizers was relatively low (Fig. 1). Previous literature showed that super-utilizer patients are very likely to have behavioral health or substance abuse issues, and have a low likelihood of admission [15,18].

Public health policy implications

In this study, ACSC-related visits among high utilizers were not de- creased in the period immediately following Maryland’s Medicaid ex- pansion. It is likely that additional time is needed for usage patterns to adjust to new insurance coverage. In order to decrease ED visits for ACSCs, increased availability of well-coordinated and accessible outpa- tient care as well as increased numbers of primary care providers is nec- essary. Simply improved insurance access is likely necessary but not sufficient solution to reduce non-emergent ED utilization and over- crowding, as prior studies have also stated [8,10,18]. This highlights the need for policy-makers to recognize that insurance is a small part of healthcare access, and the importance of developing comprehensive delivery systems, increased primary care access, Improved care coordi- nation, and Alternative Payment Models. Interestingly, in January 2014 the Centers for Medicaid and Medicare Services (CMS) granted Mary- land a waiver for hospital Medicare payments, creating global budgets for hospitals and financial penalties for spending over these budgets [19]. While the success of this model remains to be seen, it is intended to create significant financial incentives for hospitals and health systems to avoid unnecessary ED visits and hospitalizations by investing in Care coordination and robust outpatient medical and social services [19].

Future research

Further investigations are needed to characterize the Long-term effects of insurance expansion on ED utilization patterns. Given fewer Uninsured patients, Outpatient services may expand to reduce the burden of non-emergent ACSC visits in EDs. Increased access to preventative care services may reduce the number of emergent visits as well, but this time frame is years if not decades in the future. In- creased testing of innovative healthcare models that take an integra- tive approach including social services, welfare, housing, behavioral health, public health, criminal justice, and other systems that impact vulnerable populations will be important to further elimination of healthcare barriers.

This study is meant to be descriptive and hypothesis generating with respect to the barriers of reducing high ED utilization in this population. Previous literature defining the direction of utilization research calls for qualitative research to identify barriers to care in the high utilizer co- hort, especially while stratifying by disease [3]. Qualitative studies will inform future research as well as community-based interventions to im- prove access to primary care, reduce avoidable ED utilization, and im- prove health outcomes.

Limitations

This was a single center study, and changes in ED utilization out- side the single center are unable to be accounted for. Large, regional multicenter studies would be ideal to answer this question [3]. Sam- ple size may have limited the power to detect small differences,

especially using logistic regression technique, although such small differences may not be significant from a policy perspective. Diagno- sis data was from a drop-down menu as well as free-text, not ICD codes, which may result in categorization of diagnosis not consistent with other literature. The time frame from this study was only short- ly after the insurance expansion, and it may take longer than six months for the insurance expansion to have an effect on utilization patterns. We were also not able to follow individual patients before and after the utilization, which could perhaps elucidate bigger changes when focusing on patients whose insurance status changed as a result of the policy. This is an appealing line of questioning for future research, but this technique was not possible with this dataset.

Conclusion

In the year after Medicaid expansion, there was an overall increase in ED utilization at our center, while there was a small but statistically significant reduction in the proportion of ED patients that were high uti- lizers or visited for ACSC related diagnoses. Insurance expansion did not correlate with a change in Medicaid coverage of high utilizers, although it did correlate with a reduction in the disparity in private insurance coverage between high and low ED utilizers. ACSC-associated ED visits by high ED utilizers remained stable following the state’s Medicaid expansion.

Contributions

Substantial contributions to conception and design, or acquisition or analysis of data, are credited to DBG, RPM, CB, ACM, JK. manuscript drafting and revision was performed by DBG, RPM, CB, ACM, JK.

Disclosure

Dr. Khaldun also worked for the Baltimore City Health Department, and currently is Medical Director for City of Detroit Health Department. Any opinions expressed herein are those of the authors and not official policy of Baltimore City or City of Detroit Governments. No other au- thors have sources of funding or conflicts of interest to disclose.

Acknowledgements

Marcus Mitchell, for research assistance. Stephanie Gaboda for help with data abstraction from billing software.

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