Article:?Examining EMTALA in the era of the patient protection and Affordable Care Ac

AIMS Public Health Volume 5, Issue 4, 366-377.

AIMS Public Health, 5(4): 366–377

DOI: 10.3934/publichealth.2018.4.366

Received: 24 May 2018

Accepted: 21 September 2018

Published: 08 October 2018

http://www.aimspress.com/journal/aimsph

Research article

Examining EMTALA in the era of the patient protection and

Affordable Care Act

Ryan M. McKenna 1, *, Jonathan Purtle

2 , Katherine L. Nelson

3 , Dylan H. Roby

4 ,

Marsha Regenstein 5 and Alexander N. Ortega

6

1 Department of Health Management and Policy, Dornsife School of Public Health, Drexel

University, 3215 Market Street, Nesbitt Hall, Philadelphia, PA 19104, USA 2 Department of Health Management and Policy, Dornsife School of Public Health, Drexel

University, 3215 Market Street, Nesbitt Hall, Philadelphia, PA 19104, USA 3 Department of Health Management and Policy, Dornsife School of Public Health, Drexel

University, 3215 Market Street, Nesbitt Hall, Philadelphia, PA 19104, USA 4 Department of Health Services Administration, School of Public Health, University of Maryland,

4200 Valley Dr # 2242, College Park, MD 20742, USA 5 Department of Health Policy and Management, Milken Institute School of Public Health, George

Washington University, 950 New Hampshire Ave NW, Washington, DC 20052, USA 6 Department of Health Management and Policy, Dornsife School of Public Health, Drexel

University, 3215 Market Street, Nesbitt Hall, Philadelphia, PA 19104, USA

* Correspondence: Email: [email protected]; Tel: +2673596188.

Abstract: Background: Little is known regarding the characteristics of hospitals that violate the

Emergency Medical Treatment and Labor Act (EMTALA). This study addresses this gap by

examining EMTALA settlements from violating hospitals and places these descriptive results within

the current debate surrounding the Patient Protection and Affordable Care Act (ACA). Methods: We

conducted a content analysis of all EMTALA Violations that resulted in civil monetary penalty

settlements from 2002–2015 and created a dataset describing the nature of each settlement. These

data were then matched with Thomson Healthcare hospital data. We then present descriptive

statistics of each settlement over time, plot settlements by type of violation, and provide the

geographic distribution of settlements. Results: Settlements resulting from EMTALA violations

decreased from a high of 46 in 2002 to a low of 6 in 2015, a decline of 87%. Settlements resulting

from violations most commonly occurred for failure to screen and failure to stabilize patients in need

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of emergency care. Settlements were most common in hospitals in the South (48%) and in urban

areas (74%). Among Disproportionate Share Hospitals (DSH) with a violation, the majority (62%)

were located in the South or in urban areas (65%). Violating hospitals incurred annual

settlements of $31,734 on average, for a total $5,299,500 over the study period. Conclusions:

EMTALA settlements declined prior to and after the implementation of the ACA and were most

common in the South and in urban areas. EMTALA’s status as an unfunded mandate, scheduled cuts

to DSH payments and efforts to repeal the ACA threaten the financial viability of safety-net hospitals

and could result in an increase of EMTALA violations. Policymakers should be cognizant of the

interplay between the ACA and complementary laws, such as EMTALA, when considering changes

to the law.

Keywords: health policy; Affordable Care Act; emergency medicine; health reform; insurance reform

Abbreviations: ACA: Patient Protection and Affordable Care Act; CBO: Congressional Budget

Office; CMS: Centers for Medicare and Medicaid Services; DSH: Disproportionate Share Hospital;

ED: Emergency Department; EMTALA: Emergency Medical Treatment and Active Labor Act; OIG:

Office of the Inspector General

1. Introduction

Since its passage in 1986, the EMTALA has been one of the most comprehensive laws granting

nondiscriminatory access to emergency medical care [22, 32]. EMTALA was originally conceived as

a policy to prevent ―patient dumping‖, the refusal of EDs to treat patients who could not pay for

treatment [22]. EMTALA mandates that a hospital must appropriately screen, stabilize, and, if

necessary, transfer a patient regardless of insurance status or ability to pay. If it is deemed necessary

that the patient needs to be transferred, they must be transferred to a facility with appropriate care

and the receiving facility must accept the patient [31].

Federal enforcement of EMTALA is managed by two agencies, CMS and OIG. EMTALA

investigations are initiated with a complaint being filed with one of the 10 regional CMS offices

and typically submitted by patients, hospitals, or ED staff [1]. If a violation is confirmed by CMS

field investigators, hospitals must submit a plan to correct deficiencies highlighted by CMS within

90 days [2]. Hospitals that fail to implement acceptable corrective actions risk termination of their

Medicare provider agreements, which could result in a significant financial loss and lead to the

closure of the facility. If the plan is accepted by CMS, the investigation ends; however, the OIG may

still levy punitive fines on hospitals and physicians’ offices. Fines have a maximum of $50,000 per

hospital and physician and are not covered by physician malpractice insurance.

Since EMTALA’s implementation, the rate of reported patient dumping has dropped substantially,

with recent estimates from 2005–2014 showing rates as low as 1.7 violations for every 1,000,000 ED

visits [27]. While these rates represent a sharp departure from previous highs in the 1980s, EMTALA

violations suffer from underreporting and hospitals still face compliance issues [12]. Additionally,

while EMTALA represents an important safety net for those without insurance coverage, it does not

guarantee free care to the patient and is not intended as a substitute for routine care. Under EMTALA,

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patients cannot be denied emergency care based on inability to pay, but may still be billed after

receiving care. This could result in bad debt for both the consumer (i.e., bankruptcy) and the provider

(i.e., uncompensated care). EDs already provide more uncompensated care to the uninsured than

hospitals or outpatient clinics combined and nationally this amounted to approximately $50 billion in

2013 (HHS.gov 2015).

The insurance expansion provisions of the 2010 ACA lowered the uninsured rate for individuals

ages 18–64 years from a high of 18.4% in 2013 to 10.2% in 2016, a reduction of 45% [4]. While

findings of the ACA’s impact on ED utilization are mixed, some recent studies have shown that the

ACA is associated with improvements in access to usual sources of care other than the ED and

primary care utilization, especially for low-income groups and racial/ethnic minorities [3,15,25].

The expansion altered the payer-mix of many providers away from self-pay, which resulted in

improved charge capture, reductions in uncompensated care, and potentially served to lower rates

of patient dumping [7,8]. Thus, after the national implementation of the ACA in 2014, we would

expect to observe a decline in patient dumping and settlements arising from EMTALA violations.

Several proposals to repeal and replace the ACA were estimated by the CBO to reverse nearly all

of the gains in coverage attributable to the ACA [5,6,13]. Although those proposals ultimately

failed to become law during the summer of 2017 legislative session, there are still proposals being

circulated to reverse the ACA’s insurance expansion through administrative action and by reducing

Medicaid spending via the federal budget.

Hospitals serving large numbers of Medicaid and uninsured individuals are eligible for federal

DSH payments, to help offset the costs of uncompensated care. Since the insurance expansion has

likely worked to reduce the burden of uncompensated care, the ACA has built-in cuts to DSH

payments to help reduce expenditures. These scheduled reductions to DSH payments will place

greater strain on safety net hospitals. This increased strain could lead to patient dumping in order

to avoid the increased shortfalls in revenue. The impact of DSH payments cuts will be magnified in

Medicaid non-expansion states that have many low-income adults in the ―coverage gap‖ [9,18].

The uninsured low-income population are more likely than their privately insured counterparts to

use EDs and impose a risk of uncompensated care for systems [14,20].

In the face of reform efforts that would increase the number of uninsured patients and the

scheduled cuts to DSH payments, it is important to understand the current prevalence of EMTALA

violations and their distribution across the country. Despite its importance as a federal law

mandating the provision of emergency medicine, little empirical work has been published on

EMTALA violations and virtually none has examined the impact of the law within the context of

the ACA and current debates about health care and insurance reform [1,22,26,30].

This study adds to the current health reform debate by analyzing the content of all settled

EMTALA violations from the OIG between 2002–2015 and identifying the prevalence and

correlates of these cases. While settled fines do not constitute the universe of violations they are

one of the few publically available markers by which to measure EMTALA violations.

Additionally, while our findings are not causal in nature, we offer a descriptive analysis of these

settlements and discuss the implications of results within the broader context of the current health

care reform debate surrounding the ACA.

2. Methods

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AIMS Public Health Volume 5, Issue 4, 366-377.

This study is a retrospective analysis of settled OIG civil monetary penalty settlements related

to EMTALA violations from the OIG. Not every complaint which generates an EMTALA

investigation results in a monetary settlement, thus we do not observe the entire universe of

EMTALA complaints, only settled violations (approximately 7.9% of all violations [32]). In the

event that a violation was found, CMS forwards the case to OIG, where the OIG decides if a

monetary fine is warranted. We rely on settled cases as there are few reliable sources of data to

assess patient dumping and cases settled by OIG have been used in prior work [30]. For brevity,

we hereby refer to settlements that resulted from EMTALA violations as ―settlements‖ in the

manuscript.

The OIG website provides a one paragraph description about every EMTALA settlement

since 2002. We conducted a content analysis of this information for all 191 settlements posted on

the OIG website through 2015. A coding instrument was created in Qualtrics, a web-based survey

tool, and each settlement was coded according to the nature of the violation and the characteristics

of the patients involved [19]. We coded each settlement according to the year it occurred, the total

dollar amount fined, the number of patients involved, and the type of violation that resulted in the

settlement (i.e., failure to provide appropriate screening, failure to accept transfer, failure to

provide appropriate transfer, failure to provide appropriate stabilization, or unknown). Settlements

that occurred but had an unclear cause in the OIG reports were coded as ―unknown‖ in our type of

violation measure.

Hospital financial and geographic data from Thomson Healthcare Profile of US Hospitals

were merged with the OIG EMTALA violation data by hospital name and address using Microsoft

Excel 2016 [27]. The Thomson data include each hospital’s unique Medicare ID, US Census

region (Midwest, Northeast, South, West), geographic status (urban, rural), DSH status, and

number of beds.

The merged dataset was imported into R [20] statistical software version 3.4.1 for analyses.

First, annual trends in EMTALA settlements were plotted and stratified by type of violation which

resulted in a settlement. Second, descriptive statistics were generated to describe hospital and

geographic characteristics of violating hospitals. Third, 2010 Census data were used to calculate

per-capita average fines at the state-level [30]. Results were plotted on a map of the US to visually

explore geographic heterogeneity in EMTALA settlements and fines.

3. Results

We identified 191 EMTALA settlement agreements, which resulted in settlements that

occurred between 2002–2015. A total of 24 hospitals could not be uniquely identified from the

settlement reports or had active data in the Thompson database and were excluded from the

analyses, for a total of 148 unique hospitals. After merging the settlements with the Thompson

data, we had a sample of 167 settlements with associated hospital characteristics, none of which

involved individual physicians.

Figure 1 shows annual trends in settled EMTALA violations from 2002–2015. The Figure is

right skewed, which reflects a decline in the overall number of EMTALA settlements that range

from a high of 46 in 2002 to a low of 6 in 2015, or a decline of 87%. Settlements did increase by

50% in 2013 relative to 2012, driven mostly by failure to appropriately screen. The shaded bar in

Figure 1 indicates the national implementation of the ACA in 2014, which corresponds with a

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continued decline in overall settlements from 16 in 2014 to 6 in 2015. Throughout the 2002–2015

study period, the most common reason for a settlement was failure to appropriately screen

followed by failure to stabilize.

Figure 1. Settled EMTALA Violations by Type, Office of Inspector General 2002–2015.

General (OIG) ―patient dumping‖ settled violation case summaries from 2002–2015. These

violations reflect settled cases, not all alleged EMTALA violations. The shaded bar

represents the national implementation of the Patient Protection and Affordable Care Act.

Table 1 presents descriptive statistics of settled EMTALA settlements, as well as

characteristics of violating hospitals. Nearly half of the settlements (47.9%) occurred at hospitals

in the South, with the fewest settlements occurring in the Northeast (5.39%). Hospitals in urban

areas (74.3%) were more likely than hospitals in rural areas (25.7%) to incur a settlement.

Violating hospitals that had a settlement incurred an annual average fine of $31,734, for a total

$5,299,500 over the study period. No hospital in the sample had its Medicaid provider agreement

terminated as the result of an EMTALA settlement. Information on DSH status was unavailable in

the Thomson data for 70 hospitals in the sample. For the hospitals for which their DSH status

could be determined, sub analyses were conducted. Over a fifth of the hospitals (22.16%) had DSH

status, with 62% of these hospitals located in the South and 65% located in urban areas.

Figure 2 displays distribution of average fines at the state-level adjusted by each state’s

population size. With the exceptions of Vermont and Iowa, per-capita fines are concentrated in the

South and the Western US. As a robustness check, and to assess whether changes in the rate of

EMTALA settlements might have resulted from differences in enforcement arising from changes to

the OIG’s budget, we calculated inflation-adjusted OIG budgets in from 2007–2015. We found that

inflation-adjusted OIG budgets did not substantially decline over the 2007–2015 period and actually

reached their highest levels in 2015.

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Table 1. Descriptive Statistics of Settled EMTALA Violations, Office of Inspector General 2002–2015.

2002

(27)

2003

(25)

2004

(17)

2005

(15)

2006

(11)

2007

(13)

2008

(8)

2009

(5)

2010

(7)

2011

(8)

2012

(6)

2013

(12)

2014

(10)

2015

(3)

Total

(167)

Region

Midwest 3.70

(1)

8.00

(2)

41.18

(7)

20.00

(3)

45.45

(5)

30.77

(4)

25.00

(2)

20.00

(1)

14.29

(1)

25.00

(2)

50.00

(3)

41.67

(5)

30.00

(3)

0.00

(0)

23.35

(39)

Northeast 0.0

(0)

8.00

(2)

5.88

(1)

6.67

(1)

9.09

(1)

0.00

(0)

0.00

(0)

20.00

(1)

0.00

(0)

0.00

(0)

16.67

(1)

0.00

(0)

20.00

(2)

0.00

(0)

5.39

(9)

South 48.15

(13)

52.00

(13)

41.18

(7)

46.67

(7)

27.27

(3)

38.46

(5)

62.50

(5)

40.00

(2)

71.43

(5)

62.50

(5)

33.33

(2)

58.33

(7)

40.00

(4)

66.67

(2)

47.90

(80)

West 48.15

(13)

32.00

(8)

11.76

(2)

26.67

(4)

18.18

(2)

30.77

(4)

12.50

(1)

20.00

(1)

14.29

(1)

12.50

(1)

0.00

(0)

0.00

(0)

10.00

(1)

33.33

(1)

23.35

(39)

Urban 66.67

(18)

76.00

(19)

58.82

(10)

80.00

(12)

81.82

(9)

84.62

(11)

75.00

(6)

100.00

(5)

100.00

(7)

75.00

(6)

66.67

(4)

66.67

(8)

70.00

(7)

66.67

(2)

74.25

(124)

Violations*

Screening 74.07

(20)

84.00

(25)

76.47

(13)

100.00

(15)

90.91

(10)

76.92

(10)

75.00

(6)

80.00

(4)

57.14

(4)

50.00

(4)

66.67

(4)

83.33

(10)

90.00

(9)

100.00

(3)

79.64

(133)

Accept

Transfer

18.52

(5)

4.00

(1)

5.88

(1)

6.67

(1)

9.09

(1)

0.00

(0)

25.00

(2)

20.00

(1)

42.86

(3)

37.50

(3)

0.00

(0)

8.33

(1)

20.00

(2)

0.00

(0)

12.57

(21)

Provide

Transfer

18.52

(5)

16.00

(4)

58.82

(10)

13.33

(2)

9.09

(1)

23.08

(3)

12.50

(1)

40.00

(2)

0.00

(0)

0.00

(0)

0.00

(0)

16.67

(2)

0.00

(0)

0.00

(0)

17.96

(30)

Stabilization 51.85

(14)

60.00

(15)

64.71

(11)

26.67

(4)

27.27

(3)

69.23

(9)

62.50

(5)

60.00

(3)

57.14

(4)

62.50

(5)

83.33

(5)

41.67

(5)

50.00

(5)

100.00

(3)

54.49

(91)

Violation Not

Clear

7.41

(2)

12.00

(3)

0.00

(0)

0.00

(0)

0.00

(0)

0.00

(0)

12.50

(1)

20.00

(1)

0.00

(0)

0.00

(0)

0.00

(0)

0.00

(0)

0.00

(0)

0.00

(0)

4.19

(7)

Teaching

Hospital

51.85

(14)

44.00

(11)

35.29

(6)

20.00

(3)

54.55

(6)

53.85

(7)

62.50

(5)

60.00

(3)

42.86

(3)

75.00

(6)

33.33

(2)

41.67

(5)

60.00

(6)

0.00

(0)

46.11

(77)

Continued on next page

372

AIMS Public Health Volume 5, Issue 4, 366-377.

2002

(27)

2003

(25)

2004

(17)

2005

(15)

2006

(11)

2007

(13)

2008

(8)

2009

(5)

2010

(7)

2011

(8)

2012

(6)

2013

(12)

2014

(10)

2015

(3)

Total

(167)

DSH** 2.40

(4)

5.39

(9)

2.99

(5)

1.80

(3)

1.12

(2)

1.12

(2)

1.12

(2)

0.00

(0)

0.00

(0)

0.60

(1)

0.60

(1)

2.99

(5)

1.20

(2)

0.60

(1)

22.16

(37)

Average Fine 23,815

(27)

23,300

(25)

25,882 </

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