June 2016 | Issue Brief

Understanding Medicaid Hospital Payments and the
Impact of Recent Policy Changes

Peter Cunningham, Department of Health Behavior and Policy, Virginia Commonwealth University

Robin Rudowitz, Katherine Young, Rachel Garfield, and Julia Foutz, Kaiser Family Foundation

Executive Summary

Medicaid payments to hospitals and other providers play an important role in these providers’ finances, which

can affect beneficiaries’ access to care. Medicaid hospital payments include base payments set by states or

health plans and supplemental payments. Estimates of overall Medicaid payment to hospitals as a share of

costs vary but range from 90% to 107%. While base Medicaid payments are typically below cost, the use of

supplemental payments can increase payments above costs. Changes related to expanded coverage under the

Affordable Care Act (ACA) as well as other changes related to Medicaid supplemental payments could have

important implications for Medicaid payments to hospitals. This brief provides an overview of Medicaid

payments for hospitals and explores the implications of the ACA Medicaid expansion as well as payment policy

changes on hospital finances. Key findings include the following:

 Overall, hospitals have benefitted financially from the ACA coverage expansions and the increase in

Medicaid payments, especially in states that expanded Medicaid coverage. Analysis of the Medicare

Cost Report data for 2013 and 2014 shows overall declines in uncompensated care from $34.9 billion to

$28.9 billion in 2014 nationwide. Nearly all of this decline occurred in expansion states, where

uncompensated care costs were $10.8 billion in 2014, $5.7 billion or 35% less than in 2013.

 While hospitals expect to benefit financially from the Medicaid expansion, they expect some gains from

the reduction in uncompensated care to be offset by volume-generated increases in Medicaid payments

that may be lower than cost. The data is not reliable enough to support nationwide analysis of the

extent to which this has occurred, and the effect would vary across hospitals.

 Despite the decrease in uncompensated care, other changes to Medicaid payment policy (such as

required reductions to disproportionate share hospital (DSH) payments and policy changes to limit the

use of other supplemental payments) are likely to have a more substantial effect on Medicaid hospital

payment and overall hospital financial performance in the future. Ultimately the impact of reductions in

supplemental payments will depend on decisions by state governments to offset reductions with

increases to Medicaid base rates paid to hospitals.

Understanding Medicaid Hospital Payments and the Impact of Recent Policy Changes 2

Introduction

Medicaid payments to hospitals and other providers play an important role in these providers’ finances, which

can affect beneficiaries’ access to care. States have a great deal of discretion to set payment Medicaid rates for

hospitals and other providers. Like other public payers, Medicaid payments have historically been (on average)

below costs, resulting in payment shortfalls.1 However, hospital payment rates are often bolstered by additional

supplemental payments in the form of Disproportionate Share Hospital Payments (DSH) and other

supplemental payments. After accounting for these payments, many hospitals receive Medicaid payments that

may be in excess of cost. Understanding how much Medicaid pays hospitals is difficult because there is no

publicly available data source that provides reliable information to measure this nationally across all hospitals.

Different data sources use different definitions of what counts as payments and costs, so estimates are sensitive

to these data limitations.

Understanding the components of Medicaid payment to hospitals and how much Medicaid pays hospitals is

important given the many policy changes taking place. First, the Affordable Care Act (ACA) is leading to

changes in hospital payer mix, especially in states adopting the Medicaid expansion where studies have shown

a decline in self-pay discharges and a corresponding increase in Medicaid discharges.2,3,4 Second, the ACA calls

for reductions in DSH payments, and other federal policy changes are focused on limiting the use of

supplemental payments. These changes could have important implications for Medicaid payments to hospitals

at the same time that Medicaid is a growing share of hospital payer mix, especially among safety net hospitals

that serve a disproportionately high number of Medicaid and uninsured patients.

This brief provides an overview of how Medicaid pays hospitals and discusses changes related to the ACA and

supplemental payments that will have implications for hospital financing. It draws on existing literature and

published reports as well as information collected from semi-structured interviews with hospital associations

and federal agencies.5 Interviews focused on respondents’ perspectives of how hospitals were likely fare under

the ACA and changes in Medicaid payment policy. In addition, we used data from the 2013 and 2014 Medicare

cost reports to try to measure Medicaid payment and uncompensated care in 2013 and 2014.

Background

HOW DOES MEDICAID PAY HOSPITALS?

Hospital payment for a particular patient or service is usually different than the charge for that service (i.e.,

prices set by the hospital) or the cost to the hospital of providing the service (i.e., actual incurred expenses). In

Medicaid, payment rates, sometimes called the “base rate,” are set by state Medicaid agencies for specific

services used by patients. In addition, Medicaid also may make supplemental payments to hospitals (Figure 1).6

Base Payment. The base payment rates are reimbursed through fee-for-service or managed care

arrangements for services provided to Medicaid beneficiaries. States have wide discretion in setting these rates.

As discussed below, base rates are often not reflective of charges or costs for services.

Supplemental Payments. Supplemental payments are payments beyond the base rate that may or may not

be tied to specific services. States often use Upper Payment Limits (UPL), Intergovernmental Transfers (IGT),

provider taxes, or waivers to finance and direct supplemental payments. UPL rules allow states to make up the

Understanding Medicaid Hospital Payments and the Impact of Recent Policy Changes 3

difference between a reasonable estimate of what Medicare would pay and Medicaid payments (in aggregate

within a type and class of provider). IGTs or provider taxes are often used to generate the non-federal share for

Medicaid payments that are then redistributed to providers as additional Medicaid payments. In addition,

Medicaid Disproportionate Share Hospital (DSH) payments are made to hospitals serving high proportions of

Medicaid or low-income patients.

Nationally, all supplemental Medicaid payments combined amounted to 44 percent of Medicaid fee-for-service

payments to hospitals in 2014.7 Non-DSH supplemental payments (which includes UPL, IGT, and revenue

generated from provider taxes) alone accounted

for 15 percent of Medicaid fee-for-service

payments. Almost all states make Medicaid DSH

payments to hospitals, and most states also use

some other form of supplemental payments,

although both the amount of supplemental

payments and how they are distributed to

hospitals varies considerably across states.8

Supplemental payments as a proportion of total

Medicaid fee-for-service payments to hospitals

varies from a low of about 2 percent in North

Dakota, South Dakota, and Maine to more than

two-thirds in Vermont and Pennsylvania.9

HOW MUCH DOES MEDICAID PAY HOSPITALS?

Since payment rates are either negotiated (with health plans) or set by the federal government for Medicare or

state governments for Medicaid fee-for-service, payments that hospitals receive for patient care do not

necessarily reflect what hospitals charge for those services or the cost of providing those services;10 rather,

hospitals may receive payments above costs or below costs. Payments below costs would result in a “shortfall.”

Due to data challenges and differences in what is counted as a Medicaid cost and payment (see Appendix A),

estimates of Medicaid payments to hospitals vary. The American Hospital Association (AHA) estimated that

Medicaid payments to hospitals amounted to 90 percent of the costs of patient care in 2013, while Medicare

paid 88 percent of costs; by contrast, hospitals received considerable overpayment from private insurers,

amounting to 144 percent of costs.11,12 The most recent Medicaid and CHIP Payment and Access Commission

(MACPAC) report to Congress, based on the 2011 DSH audit reports, shows that DSH hospitals were paid an

average of 93 percent of total Medicaid costs, accounting for base Medicaid payments and non-DSH

supplemental payments. After DSH payments, hospitals received 107% of costs on average nationally but

ranged from 81 percent in the lowest paying state to 130 percent in the highest paying state.13 Our own analysis

of the Medicare Cost Reports finds that Medicaid payments covered 93% of costs in 2014.14 However, we find

great variation at the state or individual hospital level, indicating that hospitals may have very different

experiences of the extent to which Medicaid payment covers Medicaid costs.

Figure 1

*Includes UPL, IGT, provider taxes and 1115 Waiver payments.
Note: Based on fee-for-service payments only. Data is for 2014.
Source: Medicaid and CHIP Payment and Access Commission. MACStats, Section 3, Exhibit 2. Medicaid Supplemental Payments to
Hospital Providers by State, FY 2014, https://www.macpac.gov/publication/medicaid-supplemental-payments-to-hospital-
providers-by-state/.
.

Medicaid payment to hospitals consists of base payments
as well as supplemental payments.

$49.8 Billion

$24.2 Billion

$89.3 Billion

$15.2 Billion

Base Payment DSH Supplemental
Payments

Non-DSH Supplemental
Payments*

Total Hospital
Payment

Understanding Medicaid Hospital Payments and the Impact of Recent Policy Changes 4

How has the Medicaid expansion affected hospital finances?

Expanded health insurance coverage through the ACA (both Medicaid and private insurance) is

having a major impact on hospital payer mix for many hospitals. A number of reports show

increases in Medicaid discharges and declines in uninsured or self-pay discharges for hospitals located in states

that implemented the Medicaid expansion. In contrast, hospitals located in states that did not expand Medicaid

are not seeing these large shifts in payer mix. 15, 16, 17

One report that examined the nation’s largest not-for-profit hospital system (Ascension Health) was able to

examine not only changes in discharges but also changes in hospital revenues and costs. Like other studies,

data from Ascension Health hospitals showed that hospitals in states that expanded Medicaid experienced

larger increases in Medicaid discharge volumes and patient revenue from 2013 to 2014 compared to hospitals

in states that did not expand Medicaid. Ascension hospitals in expansion states also observed a much larger

decrease in uninsured/self-pay volumes as well as charity care.18 Overall, Ascension Health hospitals in

Medicaid expansion states observed a $35 million decrease in charity care between 2013 and 2014, but they

also saw Medicaid shortfall amounts rise by $23 million, resulting in a net decrease of $12 million in the costs

of care to the poor.19 Shortfalls grew as a result of both increases in Medicaid volume and payment rate

changes in some states. Replicating the Ascension analysis for hospitals nationally is difficult due to limited

reliable data.

The cost of uncompensated care has declined among hospitals in Medicaid expansion states,

while such costs have remained flat among hospitals in states that did not expand Medicaid.

Analysis of the Medicare Cost Report data for 2013 and 2014 shows overall declines in uncompensated care. In

2013, total uncompensated care costs for hospitals (including charity care costs and bad debt) were $34.9

billion, with hospitals in expansion states

incurring about $16.7 billion and hospitals in

non-expansion states incurring about $18.1

billion.20 In 2014, uncompensated care fell to

$28.9 billion nationwide, a $6 billion or 17%

drop, with nearly all of the decrease occurring in

expansion states (where uncompensated care

costs were $11 billion in 2014, $5.8 billion or

35% less than the year before). In non-

expansion states, the change in uncompensated

care was nearly flat between 2013 and 2014,

dropping just 1% (or $0.2 billion) to $17.9

billion in 2014 (Figure 2).

Unfortunately, we are not able to quantify how much of the decrease in hospital uncompensated care costs was

offset by increases in Medicaid shortfall amounts, because such data are not reliable in the Medicare Cost

Reports (i.e. supplemental Medicaid payments are likely to be under-reported). While the experience of the

Ascension Health system suggests that rising Medicaid shortfalls are offsetting the potential financial benefit of

lower uncompensated care costs, this outcome is likely to vary substantially across hospitals.

Figure 2

Source: KCMU analysis of the Medicare Cost Reports, 2013 and 2014.

Hospitals in expansion states saw a reduction in
uncompensated care costs from 2013 to 2014.
$ in billions

$34.9

$16.7
$18.1

$28.9

$11.0

$17.9

A l l N o n -F e d eral A cu te C ar e
H o sp i t al s

Ex p an si o n S t at es N o n -Ex p an si o n S t ates

Total Uncompensated Care Cost, 2013 Total Uncompensated Care Cost, 2014

Understanding Medicaid Hospital Payments and the Impact of Recent Policy Changes 5

Whether hospitals come out ahead financially under the ACA will depend on numerous factors

– many of which are unrelated to Medicaid. The ACA included a number of restrictions on Medicare

payments for hospitals and expanded coverage has also resulted in markets shifts and new competition.

Hospitals also may see shifts in patient acuity, Medicaid payment rate changes or other changes in Medicaid

payment policy. In addition, hospitals are constantly implementing strategies to increase revenue (e.g. diversify

payer mix) and reduce the costs of providing services. Many safety net hospitals are trying to diversify their

payer mix by changing their “safety net image” in the community, competing more aggressively for privately

insured patients, retaining the privately insured patients they already have, and expanding services beyond

inner city service areas where they are typically located.21 Thus, Medicaid expansion is just one of many factors

that will influence hospitals’ financial viability in the future.

Given this variation and difficulties with underlying data, better data are needed to capture how hospital

finances are faring under the ACA, and specifically how Medicaid revenues and shortfalls are changing.

Stakeholders interviewed for this project thought that the Medicaid expansion would be a financial benefit to

hospitals, but payment levels were a concern; however, these concerns were secondary to broader concerns

about upcoming and potential changes to Medicaid supplemental payments.

What payment policy changes could affect Medicaid hospital

payments?

Hospitals are facing several policy changes that may affect Medicaid payments. Over time, state budget

pressures have resulted in an increasing reliance on supplemental payments (versus base payments) to finance

Medicaid hospital services. However, a number of upcoming policy changes, including reductions in DSH

payments and limits on other supplemental payments, will restrict the use of supplemental payments. Federal

officials believe that reform of Medicaid supplemental payments is needed to make payment more transparent,

targeted, and consistent with delivery system reforms that reduce health care costs, and increase quality and

access to care. However, these policy changes are causing concern among hospitals that have long been

dependent on Medicaid revenue for their financial viability.22,23,24 In addition, payment changes are occurring

against the backdrop of coverage expansions under the ACA, which are affecting payer mix for some hospitals.

CHANGES IN BASE PAYMENT RATES

Changes in state reimbursement rates for hospitals have a big effect on Medicaid hospital financing, especially

for safety net hospitals that serve a large number of Medicaid patients. Each year, states must balance their

budgets, and consideration of Medicaid payment rates for providers and managed care organizations factor in

to these discussions. In general, increases in base rates have lagged behind increases in costs during economic

downturns as states often restrict (freeze or reduce) provider rates. Even as the economy has recovered, in

fiscal years 2015 and 2016, there were more states restricting (freezing or cutting) rates for Medicaid hospital

inpatient care than there were states increasing rates.25 While the economy is improving and resources are not

as scarce as during a recession, states balance the need to increase Medicaid payment rates to ensure provider

participation and access with overall budget decisions.

Understanding Medicaid Hospital Payments and the Impact of Recent Policy Changes 6

CHANGES IN MEDICAID DSH FUNDING

In 2014, federal DSH allotments totaled $11.7 billion.26 Under current law, DSH spending is limited by annual

federal allotments and individual hospital limits (hospitals cannot receive DSH payments in excess of

uncompensated care costs). The ACA calls for reductions in Medicaid DSH payments, originally scheduled to

begin in 2014 but delayed until 2018.27 These reductions will amount to $43 billion between 2018 and 2025;

reductions start at $2 billion in FY 2018 and increase to $8 billion by FY 2025. The ACA requires the Secretary

of HHS to develop a methodology to allocate the reductions that must take into account certain factors that

would allocate larger percentage reductions on states with the lowest percentages of uninsured individuals and

states that do not target DSH payments to hospitals with high levels of uncompensated care. It is unclear if or

how HHS will adjust the DSH reductions to account for the fact that some states may have higher uninsured

rates because they have opted to not implement the Medicaid expansion. MACPAC analysis shows that current

state DSH allocations are not tied to a hospital’s share of Medicaid and other low-income patients, its

uncompensated care burden, and its delivery of essential community services.28

In general, many hospitals and hospital associations are skeptical that the increase in patient revenue under

the ACA will make up for the loss of Medicaid DSH funds, although the impact will vary depending on

hospitals’ prior dependence on Medicaid DSH funding as well as federal and state government decisions on

how the remaining DSH funds will be distributed across states and hospitals. Safety net hospitals are

particularly vulnerable because of their high dependence on Medicaid DSH funds, high numbers of uninsured,

few privately insured or Medicare patients, and generally weaker financial condition.29 An analysis of California

concluded that reductions in DSH payments to the state’s public hospitals would not be fully offset by

increased revenue from paying patients due to the high number of people who would remain uninsured, low

Medicaid reimbursement rates, and the rising costs of care.30 Analyses by the New York City Health and

Hospitals Corporation also estimated that DSH cuts will put a strain on hospitals, possibly leading to

reductions in hospital medical staff and services.31

CHANGES IN OTHER SUPPLEMENTAL PAYMENTS

While states’ reliance on supplemental payments as a source of revenue for hospitals has increased, lack of data

and transparency on state’s use of supplemental payments makes federal oversight of these programs

difficult.32 Federal officials are working to reform how states use supplemental payments in managed care and

waivers, as well as the use of provider taxes.

Managed Care Rules. While UPL payments to hospitals have always been restricted to fee-for-service

payment only, some states have used pass-through mechanisms to direct supplemental payments to selected

hospitals through managed care plans. The Medicaid managed care rules originally proposed by the federal

government would have restricted states’ ability to direct supplemental payments to providers through

managed care plans.33 Under the Final Rule published in May 2016, these supplemental payments to hospitals

would be phased out over 10 years (2017-2027), by 10 percentage points each year. So, while still an area of

concerns, states and hospitals have more time to make rate adjustments over time.

DSRIP. The Delivery System Reform and Incentive Programs (DSRIP) allow states to use supplemental

payments for delivery system reforms in their Medicaid programs. These programs have been implemented

Understanding Medicaid Hospital Payments and the Impact of Recent Policy Changes 7

through Section 1115 waivers in eight states, including California, Texas, Massachusetts, New Jersey, New

Mexico, Kansas, and New York.34,35 Supplemental payments through DSRIP are being used to achieve

particular goals, such as improved quality, outcomes, access to care and population health. In most states with

DSRIP programs, public hospitals are contributing all or most of the non-federal share of funding for these

programs.36 The DSRIP programs are temporary, with the expectation that states and providers can transform

their delivery systems so that they are more efficient, less costly, have lower use of hospital inpatient care, and

more use of primary and preventive care. While these payments are included under broad waivers that are

budget neutral to the federal government, the amount of funding allocated for DSRIP programs is significant

($3.3 billion in California, $6.6 billion in Texas, $6.4 billion in New York)37, and the phase-out of this funding

will have implications for states and providers. In renewing California’s DSRIP program in December 2015,

funding is scheduled to phase down by 10% in year four and by 15% in year five.38

Safety Net Care Pools. Federal policy makers also have been focused on reforming the use of Medicaid

Section 1115 demonstration waivers to fund state uncompensated care pools in nine states. Officials laid out

the principles for which such funds were to be used, including: (1) funds should not pay for the costs that would

be covered in a Medicaid expansion; (2) they should support services provided to Medicaid beneficiaries and

low-income uninsured individuals, and; (3) provider payment should promote provider participation and

access, and should support plans in managing and coordinating care.39 To the extent that this funding has been

used to supplement Medicaid base rates for certain hospitals, changes to these funding streams will affect

hospital finances. The agreement to renew Florida’s Low Income Pool – which reduced funds for the pool –

included a $400 million increase in base rates to providers. In May 2016, Texas received a 15 month extension

of their waiver; the letter states that if CMS and the state cannot reach agreement during this extension period,

CMS expects that the Uncompensated Care pool will not be renewed at the end of 2017 and that DSRIP will

decrease by 25% each year starting in 2018.40

Provider Taxes. Provider taxes are an integral source of Medicaid financing governed by long-standing

regulations. Currently, all but one state (Alaska) reported a provider tax in FY 2015.41 Often provider taxes are

used to bolster Medicaid payment rates for hospitals; however, these taxes can also be used to support state

general revenues. In Connecticut, the state has been retaining more of the provider tax to address state budget

deficits instead of supporting hospitals.42 The state hospital association estimates that this has effectively

decreased overall Medicaid payments from 50 percent of costs to 41 percent.43 In addition to state decisions

about how to use funding from provider taxes, Congress is currently considering proposals to limit the use of

provider taxes. This action would restrict states’ flexibility to finance their share of Medicaid and could

therefore shift additional costs to states or result in program cuts. Since states use provider taxes differently,

limits would have different effects across states.

Understanding Medicaid Hospital Payments and the Impact of Recent Policy Changes 8

Conclusion

At this point, it is unclear how recent and upcoming policy changes in Medicaid will affect the financial viability

of hospitals. Early analysis of the Medicare Care Report data show national declines in uncompensated care,

especially in expansion states, although the data do not permit reliable estimates of trends in Medicaid

payment amounts. However, hospital margins are influenced by numerous factors, the health care and policy

environment is in flux, and some hospitals will be better able to adapt to these changes than others. There is

much concern from hospitals – especially safety net hospitals – about the decrease in Medicaid DSH funds and

other changes in supplemental payments that they have depended on for years. Most stakeholders from the

hospital industry that we talked to thought that even after accounting for increases in Medicaid shortfall, the

Medicaid expansion was a financial benefit, but changes to supplemental payments could have a much larger

negative effect on hospital finances. The overall impact of changes to supplemental payments also will depend

on how much states adjust base payment rates to compensate for changes to supplemental payments. Better

data and monitoring of the effects of coverage changes as well as policy changes related to the supplemental

payments will help to better evaluate hospitals financial well-being and the ability of safety net hospitals to

serve Medicaid and uninsured persons.

Understanding Medicaid Hospital Payments and the Impact of Recent Policy Changes 9

Appendix A: Measuring Medicaid Payments to Hospitals

No data source consistently collects information on Medicaid costs and payment, and different estimates of

Medicaid payment as a share of costs use different definitions of Medicaid costs and payments. Thus, estimates

of Medicaid payment as a percent of costs are sensitive to the specific data source and definitions used to make

the estimates. For example, when measuring Medicaid hospital payments, some data sources include

supplemental payments, while others do not. In some data sources, these payment streams are not identified,

making it difficult to understand what is and is not included. Further, in some cases, Medicaid costs may be

defined to include only costs for Medicaid-covered services, while in others, the definition may include unpaid

costs for services provided to Medicaid patients when Medicaid was not the primary payer—for example, costs

for Medicare-funded services provided to people dually eligible for both Medicaid and Medicare.

Three main sources of estimating Medicaid payment relative to costs are the Medicare cost reports, the DSH

Audit Reports and the American Hospital Association survey. These sources vary in the data they collect and

the definitions of costs and payments that they enable. Each of these data sources may underreport other

Medicaid supplemental payments, which may understate total Medicaid payments, and the data likely does not

net out provider contributions towards the non-federal share, which are necessary to calculate net Medicaid

payments and may contribute to overstating total Medicaid payments.

Medicare Cost Reports. The Medicare Cost Reports (MCR) are annual reports that all Medicare-certified

institutional providers are required to submit to Medicare. It is the only publicly available source of detailed

financial data for most of the acute care hospitals in the U.S. These reports contain provider information such

as facility characteristics, utilization data, cost and charges by cost center (in total and for Medicare), Medicare

settlement data, and financial statement data that are used as part of the annual settlement between the federal

government and the provider.44 These cost reports are designed to collect data necessary for Medicare

reimbursement and thus do not verify or require Medicaid data, leading to questions about how reliable these

data are for Medicaid payment analyses. Hospitals are not required to report DSH payments separately, but

DSH payments are included as Medicaid revenues in these reports, and the reports only include costs for

Medicaid-covered services.

Medicaid DSH Audit Reports. The Medicaid DSH audit reports are required annual reports that states

must submit to the federal government describing DSH payments made to each DSH hospital.45 In these

reports, hospitals explicitly report DSH payments. DSH audits also include unpaid costs for services provided

to Medicaid patients when Medicaid was not the primary payer. The primary limitation of this data source is

that they exclude hospitals that do not receive DSH payments, which are likely to differ substantially from DSH

hospitals in the amount of overpayment or underpayment from Medicaid.

American Hospital Association Reports (AHA). The AHA uses data from their annual hospital survey

to provide an estimate of Medicaid (and Medicare) payments relative to costs. In their survey, AHA obtains

information on each hospitals’ net and gross Medicaid payments, DSH, and supplemental payments. They

calculate a cost-to-charge ratio and use this to determine the rate of underpayment for all hospitals. In their

underpayment calculation, they include all payment adjustments.46 While AHA publishes annual reports on

overall hospital uncompensated care costs, as well as Medicare and Medicaid underpayments, the detailed

financial data are not available on their public use files.

Understanding Medicaid Hospital Payments and the Impact of Recent Policy Changes 10

As a result of these differences, as well as limitations in the underlying data, estimates of Medicaid payment as

a share of cost vary (see Table A1). However, most estimates indicate that Medicaid payments cover most

(more than 90%) costs, with one estimate indicating that some hospitals (those that receive DSH payments)

receive Medicaid reimbursement in excess of costs.

Table A1: Estimates of Medicaid Payments as a Share of Costs

Source
Data Year Estimate of

Medicaid

Payment as a

Share of

Medicaid Cost

Notes

American Hospital

Association (AHA)
47

AHA annual

survey

2013 90%  Includes non-federal, acute care hospitals

 Payments include supplemental payments

and DSH

 Cost data reflect payments for Medicaid

beneficiaries for Medicaid-covered services

Medicaid and CHIP

Payment and

Access Commission

(MACPAC)
48

DSH Audit

Reports

2011 93% excluding

DSH and 107%

including DSH

 Includes only hospitals that receive DSH

payments

 Payments include DSH, but these payments

are reported separately

 Cost data includes cost of services for

Medicaid patients for which Medicaid is not

primary payer

Authors’ analysis Medicare

Cost Reports

2014 93%  Includes non-federal, acute care hospitals

 Payments include DSH

 Cost data reflect payments for Medicaid

beneficiaries for Medicaid-covered services

Understanding Medicaid Hospital Payments and the Impact of Recent Policy Changes 11

Endnotes

1

American Hospital Association, “Table 4.4: Aggregate Hospital Payment-to-cost Ratios for Private Payers, Medicare,” in Trendwatch
Chartbook 2015 (Chicago, IL: American Hospital Association, 2015), http://www.aha.org/research/reports/tw/chartbook/2015/table4-
4.pdf.

2

Deborah Bachrach, Patricia Boozang, and Mindy Lipson, The Impact of Medicaid Expansion on Uncompensated Care Costs: Early
Results and Policy Implications for States (Princeton, NJ: The Robert Wood Johnson Foundation, State Health Reform Assistance
Network, June 2015), http://www.rwjf.org/en/library/research/2015/06/the-impact-of-medicaid-expansion-on-uncompensated-
care-costs.html.

3

Robin Rudowitz and Rachel Garfield, New Analysis Shows States with Medicaid Expansion Experienced Declines in Uninsured
Hospital Discharges (Washington, DC: Kaiser Commission on Medicaid and the Uninsured, September 2015), http://kff.org/health-
reform/issue-brief/new-analysis-shows-states-with-medicaid-expansion-experienced-declines-in-uninsured-hospital-discharges.

4

Sayeh Nikpay, Thomas Buchmueller, and Helen G. Levy, “Affordable Care Act Medicaid Expansion Reduced Uninsured Hospital Stays
In 2014,” Health Affairs. 35, no. 1 (2016): 106-10, http://content.healthaffairs.org/content/35/1/106.abstract.

5

Organizations interviewed for this report included the American Hospital Association, America’s Essential Hospitals, the Connecticut
Hospital Association, the California Public Hospital Association, the Medicaid and CHIP Payment and Access Commission (MACPAC),
and the Center for Medicare and Medicaid Services (CMS).

6

Medicaid and CHIP Payment and Access Commission, “Examining the Policy Implications of Medicaid Non-Disproportionate Share
Hospital Supplemental Payments,” chap. 6 in March 2014 Report to the Congress on Medicaid and CHIP (Washington, DC: March
2014), 183-209, https://www.macpac.gov/publication/report-to-the-congress-on-medicaid-and-chip-314/.

7

Medicaid and CHIP Payment and Access Commission, “Medicaid Supplemental Payments to Hospital Providers by State, FY 2014”
Exhibit 23 in December 2015 MACStats: Medicaid and CHIP Data Book. https://www.macpac.gov/wp-
content/uploads/2015/11/EXHIBIT-23.-Medicaid-Supplemental-Payments-to-Hospital-Providers-by-State-FY-2014-millions.pdf

8

Ibid.

9

Ibid.

10

Uwe Reinhardt, “The pricing of U.S. hospital services: Chaos behind a veil of secrecy,” Health Affairs, 25, no. 1 (2006): 57-69,
http://content.healthaffairs.org/content/25/1/57.full.pdf+html.

11

American Hospital Association, “Table 4.4: Aggregate Hospital Payment-to-cost Ratios for Private Payers, Medicare,” in Trendwatch
Chartbook 2015 (Chicago, IL: American Hospital Association, 2015), http://www.aha.org/research/reports/tw/chartbook/2015/table4-
4.pdf.

12

AHA estimates that in 2014, Medicare paid 89 percent of costs for Medicare patients and Medicaid paid 90 percent of costs ofr
Medicaid patients. See: American Hospital Association, “Underpayment by Medicare and Medicaid Fact Sheet: Underpayment by
Medicare and Medicaid Fact Sheet,” (Chicago, IL: American Hospital Association, 2016)
http://www.aha.org/content/16/medicaremedicaidunderpmt.pdf

13

MACPAC’s analysis showed similar findings using the Medicare Cost Reports (from among a subset of hospitals with complete data
from both sources). Medicaid and CHIP Payment and Access Commission, “Improving Data as the First Step to a More Targeted
Disproportionate Share Hospital Policy,” chap. 3, March 2016 Report to Congress on Medicaid and CHIP, (Washington, DC: March
2016), 56-73, https://www.macpac.gov/publication/improving-data-as-the-first-step-to-a-more-targeted-disproportionate-share-
hospital-policy/.

14

Using data from Worksheet S-10 in the 2013 and 2014 Medicare Costs Reports, we calculated revenue over costs as net Medicaid
revenue divided by the product of Medicaid charges and the cost to charge ratio. We restricted the data to just non-federal acute care
hospitals that had both 2013 and 2014 data. We adjusted spending amounts to reflect the entire year. We treated blanks in the data as
missing data, and did not include them in the rate. Worksheet S-10 (“Hospital Uncompensated and Indigent Care Data”), 2013 and
2014 Medicare Cost Reports, https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/Cost-
Reports/Cost-Reports-by-Fiscal-Year.html.

15

Bachrach et al., op. cit.

16

Rudowitz and Garfield, op. cit.

17

Nikpay, Buchmueller, and Levy, op. cit.

18

Peter Cunningham, Rachel Garfield, and Robin Rudowitz, How are Hospitals Faring under the Affordable Care Act? Early
Experiences from Ascension Health. (Washington DC: Kaiser Commission on Medicaid and the Uninsured, October 2014),
http://kff.org/health-reform/issue-brief/how-are-hospitals-faring-under-the-affordable-care-act-early-experiences-from-ascension-
health/

19

Ibid., Table 5.

Understanding Medicaid Hospital Payments and the Impact of Recent Policy Changes 12

20

Using the Worksheet S-10 data from the 2013 and 2014 Medicare Cost Reports, we calculated uncompensated care by summing bad
debt costs and charity care costs. As we did when calculating the revenue over costs, we restricted the data to non-federal acute care
hospitals that had both 2013 and 2014 data. We also adjusted spending to reflect the entire year. By linking the Medicare Cost Report
data to the American Hospital Association Hospital Data, available through the AHA Data Viewer, we identified the location of each
hospital. We categorized all states that had expanded by December 31, 2014 as “expansion states” and all others as “non-expansion
states.”

21

Teresa Coughlin, Sharon Long, Rebecca Peters, Robin Rudowitz, and Rachel Garfield, Evolving Picture of Nine Safety-Net Hospitals:
Implications of the ACA and Other Strategies (Washington, DC: Kaiser Commission on Medicaid and the Uninsured, April 2015),
http://kff.org/health-reform/issue-brief/evolving-picture-of-nine-safety-net-hospitals-implications-of-the-aca-and-other-strategies/.

22

America’s Essential Hospitals, “Our View: Essential Hospitals Rely on Medicaid Supplemental Payments,” (Washington DC:
America’s Essential Hospitals, March 2016) http://essentialhospitals.org/policy/essential-hospitals-rely-on-supplemental-payments/.

23

Christopher Weaver, “Hospitals Expected More of a Boost From Health Law,” Wall Street Journal, June 3, 2015,
http://www.wsj.com/articles/hospitals-expected-more-of-a-boost-from-health-law-1433304242.

24

Kentucky Hospital Association, “Code Blue: Many Kentucky Hospitals Struggling Financially Due to Health System changes,”
(Louisville, KY: Kentucky Hospital Association, April 2015) http://www.new-
kyha.com/Portals/5/NewsDocs/Code%20Blue%20Report%20Web.pdf.

25

Vernon Smith, Kathleen Gifford, Eileen Ellis, Robin Rudowitz, Laura Snyder, and Elizabeth Hinton, Medicaid Reforms to Expand
Coverage, Control Costs and Improve Care. Results from a 50-State Medicaid Budget Survey for State Fiscal Years 2015 and 2016.
(Kaiser Family Foundation and National Association of Medicaid Directors, October 2015) http://kff.org/medicaid/report/medicaid-
reforms-to-expand-coverage-control-costs-and-improve-care-results-from-a-50-state-medicaid-budget-survey-for-state-fiscal-years-
2015-and-2016/.

26

79 Fed. Reg. 11436 – 11445 (February 28, 2014), available at https://www.federalregister.gov/articles/2014/02/28/2014-
04032/medicaid-program-preliminary-disproportionate-share-hospital-allotments-dsh-for-fiscal-year-fy-2014.

27

42 U.S.C. § 1396r-4(f)(7). See https://www.law.cornell.edu/uscode/text/42/1396r-4 .

28

Medicaid and CHIP Payment and Access Commission, “Analysis of Current and Future Disproportionate Share Hospital Allotments,”
chap. 2, March 2016 Report to Congress on Medicaid and CHIP, (Washington, DC: March 2016), 21-54,
https://www.macpac.gov/publication/analysis-of-current-and-future-disproportionate-share-hospital-allotments/.

29

Evan Cole, Daniel Walker, Arthur Mora, Mark Diana, “Identifying Hospitals That May Be at Most Financial Risk from Medicaid
Disproportionate Share Hospital Payment Cuts,” Health Affairs, 33, no. 11 (2014): 2025-2033,
http://content.healthaffairs.org/content/33/11/2025.abstract.

30

Katherine Neuhausen, Anna Davis, Jack Needleman, Robert Broook, David Zingmond, and Dylan Roby. “Disproportionate-Share
Hospital Payment Reductions May Threaten the Financial Stability of Safety-Net Hospitals.” Health Affairs, 33, no. 6 (2014): 988-996,
http://content.healthaffairs.org/content/33/6/988.abstract.

31

Office of the New York City Comptroller, Holes in the Safety Net: Obamacare and the Future of New York City’s Health and
Hospitals Corporation, (New York, NY: Office of the New York City Comptroller, May 2015), http://comptroller.nyc.gov/wp-
content/uploads/documents/Holes_in_the_Safety_Net.pdf.

32

Government Accountability Office, “Medicaid: Improving Transparency and Accountability of Supplemental Payments and State
Financing Methods,” (Washington, DC: Government Accountability Office, November 2015), http://www.gao.gov/products/GAO-16-
195T.

33

Moira Forbes and Chris Park, “Issues in Medicaid Managed Care Rate Setting,” (Washington DC: Medicaid and CHIP Payment and
Access Commission, May 2015) https://www.macpac.gov/wp-content/uploads/2015/05/Issues-in-Medicaid-Managed-Care-Rate-
Setting.pdf.

34

Alexandra Gates, Robin Rudowitz and Jocelyn Guyer, An Overview of Delivery System Reform Incentive Payment (DSRIP) Waivers
(Washington, DC: Kaiser Commission on Medicaid and the Uninsured, September 2014), http://kff.org/medicaid/issue-brief/an-
overview-of-delivery-system-reform-incentive-payment-waivers/.

35

Jocelyn Guyer, Naomi Shine, Robin Rudowitz, and Alexandra Gates, Key Themes From Delivery System Reform Incentive Payment
(DSRIP) Waivers in 4 States (Washington, DC: Kaiser Commission on Medicaid and the Uninsured, April 2015),
http://kff.org/medicaid/issue-brief/key-themes-from-delivery-system-reform-incentive-payment-dsrip-waivers-in-4-states/.

36

Gates, Rudowitz and Guyer, op. cit.

37

Medicaid and CHIP Payment and Access Commission, June 2015 Report to Congress on Medicaid and CHIP, (Washington, DC: June
2015), https://www.macpac.gov/wp-content/uploads/2015/06/June-2015-Report-to-Congress-on-Medicaid-and-CHIP.pdf

38

Letter from CMS to Mari Cantwell, Chief Deputy Director Department of Health Care Services, California. (Washington, DC: CMS,
December 30, 2015). https://www.medicaid.gov/Medicaid-CHIP-Program-Information/By-Topics/Waivers/1115/downloads/ca/medi-
cal-2020/ca-medi-cal-2020-ca.pdf

The Henry J. Kaiser Family Foundation Headquarters: 2400 Sand Hill Road, Menlo Park, CA 94025 | Phone 650-854-9400
Washington Offices and Barbara Jordan Conference Center: 1330 G Street, NW, Washington, DC 20005 | Phone 202-347-5270

www.kff.org | Email Alerts: kff.org/email | facebook.com/KaiserFamilyFoundation | twitter.com/KaiserFamFound

Filling the need for trusted information on national health issues, the Kaiser Family Foundation is a nonprofit organization based in Menlo Park, California.

39 Letter from CMS to Justin Senior, Deputy Secretary for Medicaid, State of Florida, (Washington, DC: CMS, May 21, 2015),
https://www.medicaid.gov/Medicaid-CHIP-Program-Information/By-Topics/Waivers/1115/downloads/fl/Managed-Medical-
Assistance-MMA/fl-medicaid-reform-lip-ltr-05212015.pdf.

40 Letter from CMS to Gary Jessee, Associate Commissioner for Medicaid/CHIP, State of Texas. (Washington, DC: CMS, May 2016).
https://www.medicaid.gov/Medicaid-CHIP-Program-Information/By-Topics/Waivers/1115/downloads/tx/tx-healthcare-
transformation-ca.pdf

41 Smith, Gifford, Ellis, Rudowitz, Snyder, and Hinton, op. cit.

42 Arielle Levin Becker, “CT Hospitals Say Obamacare Hasn’t Cut Uncompensated Care,” CT Mirror, September 29, 2014,
http://ctmirror.org/2014/09/29/ct-hospitals-say-obamacare-hasnt-cut-uncompensated-care/.

43 Interview with Connecticut Hospital Association.

44 “Cost Reports,” CMS, https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/Cost-Reports/
and “Healthcare Cost Report Information System,” ResDAC, https://www.resdac.org/cms-data/files/hcris.

45 “Medicaid Disproportionate Share Hospital (DSH) Payments,” CMS, https://www.medicaid.gov/medicaid-chip-program-
information/by-topics/financing-and-reimbursement/medicaid-disproportionate-share-hospital-dsh-payments.html.

46 American Hospital Association, “Underpayment by Medicare and Medicaid Fact Sheet,” op. cit.

47 Ibid. Telephone conversation with Caroline Steinberg from the AHA Policy Division provided additional information used in notes.

48 Medicaid and CHIP Payment and Access Commission, March 2016 Report to Congress on Medicaid and CHIP, (Washington, DC:
March 2016), https://www.macpac.gov/wp-content/uploads/2016/03/March-2016-Report-to-Congress-on-Medicaid-and-CHIP.pdf.

NURSING ECONOMIC$/May-June 2018/Vol. 36/No. 3110

H
OSPITALS IN THE U.S. FACE
ongoing challenges as
they strive to achieve
their missions. They are

struggling to operate in a turbulent
healthcare environment, consist-
ing of uninsured and underin-
sured patients (Grant, Colello,
Riehle, & Dende, 2010), changing
reimbursement policies, broaden-
ing regulatory requirements, and
increasing emphasis on quality
care outcomes (Parsons & Cornett,
2011). Despite the challenging
operating environments, hospitals
are trying to survive and maintain
delivery of high-quality healthcare
services. Maintaining financial

viability, hospitals are employing
operational strategies to provide
distinct advantages and differenti-
ate themselves from other com-
petitors, potentially providing
opportunities to increase revenue
either through market share or
reimbursement. One way a hospi-
tal can distinguish itself is by sig-
naling the underlying quality of
its products and services.

Signals are used to reduce
information symmetry, which is
defined as an imbalance of infor-
mation between two parties,
where one side has more informa-
tion than another (Connelly,
2011). Signals are used in health

George M. Holmes
Cheryl B. Jones

Elizabeth K. Woodard

Saleema A. Karim
George H. Pink
Kristin L. Reiter

The Effect of the Magnet
Recognition® Signal on Hospital

Reimbursement and Market Share

SALEEMA A. KARIM, PhD, MBA, MHA, is Assistant Professor, Department of Health Policy
and Management, Fay W. Boozman College of Public Health, University of Arkansas for
Medical Sciences, Little Rock, AR.

GEORGE H. PINK, PhD, is Professor, Department of Health Policy and Management,
Gillings School of Global Public Health, The University of North Carolina at Chapel Hill,
Chapel Hill, NC.

KRISTIN L. REITER, PhD, is Professor, Department of Health Policy and Management,
Gillings School of Global Public Health, The University of North Carolina at Chapel Hill,
Chapel Hill, NC.

GEORGE M. HOLMES, PhD, is Associate Professor, Department of Health Policy and
Management, Gillings School of Global Public Health, The University of North Carolina at
Chapel Hill, Chapel Hill, NC.

CHERYL B. JONES, PhD, is Professor and Chair, School of Nursing, The University of North
Carolina at Chapel Hill, Chapel Hill, NC.

ELIZABETH K. WOODARD, PhD, is a Director, Nursing Research and Evidence-Based
Practice, WakeMed Health & Hospitals, Raleigh, NC.

EXECUTIVE SUMMARY
Magnet Recognition® is a quali-
ty designation granted by the
American Nurses Credentialing
Center.
If patients and payers interpret
the Magnet Recognition desig-
nation as a signal of high-
quality care, then demand for
Magnet hospitals should
increase and lead to an
increase in market share and
revenue.
This study examines the effects
of the Magnet Recognition sig-
nal on both hospital and patient
reimbursement.
Using a difference-in-difference
model with hospital fixed-
effects, results indicate Magnet
Recognition signal does not
affect either patient reimburse-
ment or market share of desig-
nated hospitals compared to
non-designated hospitals.
While Magnet Recognition has
been associated with various
positive benefits for patients,
nurses, and the organization,
hospital executives and policy-
makers should carefully consid-
er the financial resources dedi-
cated to publicizing the Magnet
Recognition designation.

111NURSING ECONOMIC$/May-June 2018/Vol. 36/No. 3

care to communicate the underly-
ing quality of a hospital’s products
and services to its stakeholders.
Signals such as corporate name
changes, quality designations,
product branding, advertising
expenditures, and management
quality communicate the commit-
ment of resources to differentiation
and emphasize the organization’s
commitment to quality. The infor-
mation contained in the signal per-
mits consumers to make informed
decisions and distinguish between
high-quality and low-quality prod-
ucts. Some hospitals communicate
directly, using public reporting of
quality of care information (Faber,
Bosch, Wollersheim, Leatherman,
& Grol, 2009), such as Hospital
Compare (Centers for Medicare &
Medicaid Services [CMS], 2015).
Others communicate indirectly, or
signal, unobservable information to
consumers by attaining an expen-
sive quality designation, which the
consumer can interpret as the
firm’s commitment of resources to
quality management (Boulding &
Kirmani, 1993). The quality infor-
mation conveyed by the signal then
leads consumers to update their
perceptions (Connelly, 2011) about
product and service quality within
the context of market conditions.

The Magnet Recognition® (MR)
designation is an example of a sig-
nal employed by hospitals. This
signal communicates to patients,
providers, and payers the hospital’s
dedication and commitment to
both healthcare quality and quality
management via nursing service
excellence (Hader, 2010; O’Neill &
Largey, 1998), which in today’s
competitive marketplace is an
important hospital characteristic
(Bumgarner & Beard, 2003;
Everhart, Neff, Al-Amin, Nogle, &
Weech-Maldonado, 2013). MR is a
quality designation given by the
American Nurses Credentialing
Center (ANCC) to hospitals and
long-term care facilities (Gerhardt
& VanKuiken, 2008) to recognize
organizations as centers of nursing
excellence (Trinkoff et al., 2010).
Pursuing and sustaining MR

requires a commitment of time and
investment of substantial human
and financial resources by the hos-
pital (Parsons & Cornett, 2011; Rich
& Barnsteiner, 2007). The designa-
tion has gained widespread atten-
tion in both research (Hill, 2011)
and practice (Lewis, 2008;
Stimpfel, Rosen, & McHugh, 2014).

MR is considered to be a sym-
bol of distinction (Parsons &
Cornett, 2011) and has been theo-
rized to signal the hospital’s dedica-
tion and commitment to quality
patient care to patients, payers, and
healthcare providers (Jenkins &
Fields, 2011). This in turn leads to
increased volume of patients and
corresponding increases in hospital
market share (Gaguski, 2006;
Stimpfel, Sloane, McHugh, & Aiken,
2016) and revenue (Smith, 2007a).
MR has been associated with better
patient outcomes (Hess, DesRoches,
Donelan, Norman, & Buerhaus,
2011; Ulrich, Buerhaus, Donelan,
Norman, & Dittus, 2009), increases
in quality care (Drenkard, 2010),
and increases in nurse-to-patient
ratios (Kelly, McHugh, & Aiken,
2011). Patients are expected to inter-
pret the MR signal and respond by
seeking care at, or referring family
and friends to designated hospitals.
Patients may also respond by
remaining loyal to the designated
facility through repeated visits.
Providers (physicians) are expected
to interpret the MR signal by refer-
ring patients to designated hospitals
where they will receive quality care.
Payers (government and insurers)
are expected to understand the MR
signal and respond by steering
patients to designated hospitals to
receive quality patient care (Lash &
Munroe, 2005) or adjusting reim-
bursement for health services
accordingly (Smith, 2007b). These
combined actions of patients,
providers, and payers in response to
the signal are expected to increase
the volume of patients to MR-desig-
nated hospitals (Gaguski, 2006;
Stimpfel et al., 2016) and increase
reimbursement (Jayawardhana,
Wel ton, & Lindrooth, 2014; Smith,
2007c).

Given the endorsements and
increasing interest in the MR pro-
gram, despite the lack of evidence
on its ability to be an indicator of
quality distinction, there is a
notable gap in knowledge that is
highly relevant to the hospital
marketplace. This study evaluates
the efficacy of the MR signal by
examining its effect on two
dimensions of hospital financial
performance: reimbursement and
market share. The purpose of this
research study was twofold: (a)
Investigate the impact of the MR
signal on hospital reimbursement;
and (b) Examine the impact of the
MR signal on hospital market
share.

The outcomes of this research
will inform managers and policy-
makers about the effectiveness of
the MR signal on changing hospi-
tal reimbursement and market
share, and thus its utility as a
potential strategy to improve the
hospital’s marketability and finan-
cial health, especially in highly
competitive market areas.

Research Design
The study applied a pre-post

research design to measure the
effect of the MR signal on hospital
reimbursement and market share.
The hospital observations were
divided into two groups. The
treatment group, referred to as MR
hospitals, included hospitals that
achieved MR anytime during the
study period; and the control
group, referred to as never-MR
hospitals, included hospitals that
never achieved MR before, during,
or after the study period.

The MR hospitals were con-
ceptualized as experiencing three
phases: pre-recognition, imple-
mentation, and post-recognition.
The pre-recognition phase was the
period before a hospital was
actively pursuing MR, defined as
the 2 years before the hospital was
seeking MR designation. The
implementation phase was the
period when a hospital was
actively engaged in preparing for
MR, defined as the 2 years before

NURSING ECONOMIC$/May-June 2018/Vol. 36/No. 3112

obtaining initial MR designation.
The post-recognition phase was
the immediate period after the ini-
tial MR designation, described as
2 years after receiving the initial
MR designation.

Data Sources
The hospital data for the analy-

sis were obtained from Medicare’s
Hospital Cost Report Information
System (CMS, 2014), American
Hospital Association (1997) Annual
Survey of Hospitals, Area Resource
File (U.S. Department of Health and
Human Services, 2004), and ANCC
website (2014). These four data sets
were merged using both a year and
a hospital identifier.

Study Sample
The study sample was a longi-

tudinal, unbalanced panel of MR
hospitals and never-MR hospitals
located in urban areas in the
United States covering 2000-2010.
The initial data set consisted of
3,421 hospitals (31,163 hospital-
year observations). Study exclu-
sions included hospital-year
observations with fewer than 330
days in the Medicare cost report
period, hospitals with fewer than
8 hospital-year observations, hos-
pitals that did not have a hospital-
year observation in the year 2000,
and hospitals that received MR
before 2004 or after 2009 were
excluded from the dataset. In
addition, to remain in the final
study sample, each MR hospital
must have had hospital-year
observations for all three phases of
the MR designation process: 4
consecutive years of data prior to
MR designation (2 years for pre-
recognition phase and 2 years for
implementation phase), and 2
consecutive years of data follow-
ing MR designation (post-recogni-
tion phase), for a total of 6 hospi-
tal-year observations. After these
exclusions, 2,199 hospitals were
eligible for the study: 1,968 never-
MR hospitals and 231 MR hospi-
tals.

Since some hospitals have
specific characteristics that pre-

dispose them to become an MR
hospital, propensity score analysis
was used to control for this selec-
tion bias. Using hospital data from
the year 2000, each MR hospital
was matched to a maximum of
four never-MR hospitals in the
year 2000 using propensity scores.
The matched hospitals from the
year 2000 served as the matches
for the remainder of the study
period. The final matched study
sample consisted of 231 hospital
groups for a total of 1,155 hospi-
tals (231 MR hospitals and 924
never-MR hospitals).

Variables and Measurements
Dependent variables. Net pa –

tient revenue per adjusted patient
day was used to measure hospital
reimbursement. The adjusted pa –
tient day was defined as the sum of
inpatient days and the equivalent
patient days attributed to outpatient
services. To account for inflation,
reimbursement was adjusted to
2010 U.S. dollars using the Medical
Care Services Consumer Price
Index. Since net patient revenue per
adjusted patient day is not normally
distributed, the variable was trans-
formed using the natural log to pro-
vide a percentage interpretation of
the coefficients.

Hospital market share, a meas-
ure of the amount of hospital com-
petition in the market area, was
measured as the hospital’s dis-
charges as a percentage of the total
discharges in a hospital’s market
area (McCue, McCall, Hurley,
Wyttenback, & White, 2001). The
hospital’s market area was defined
as the county in which the hospital
is located.

Independent variables: Main
explanatory variable. The MR des-
ignation variable identified hospi-
tals as either MR or never-MR. The
MR status variable identified the
three phases as either pre-recogni-
tion, implementation, or post-
recognition during the 6-year peri-
od each hospital was observed.

Independent variables: Control
variables. Hospital characteristics
are structural factors and processes

that can influence hospital opera-
tions, marketability, and ability to
earn revenues (Whiteis, 1992).
Variables include hospital size
(measured by the total number of
beds), which is known to be associ-
ated with higher economies of
scale, lower cost per unit, and more
successful strategic activity. System
affiliation indicates whether a hos-
pital is owned by a larger system.
Such affiliations can result in
increased efficiency, lower risk, bet-
ter financial outcomes, more seam-
less care, greater control over refer-
rals, and greater economies of scale
(Kim, 2010).

Payer mix for Medicare and
Medicaid were calculated separate-
ly as the percentage of total inpa-
tient days attributed to Medicare
and Medicaid, respectively. These
measures indicated the hospital’s
patient mix (Trussel, Patrick,
DelliFraine, & Davis, 2010) and its
overall payer mix (Bazzoli & Andes,
1995). An increased dependence on
government payers, such as Medi –
care and Medicaid, is likely associ-
ated with lower patient revenue
because these payers typically do
not pay the full average cost of care
(Trussel et al., 2010). Teaching affil-
iation indicated hospitals affiliated
with academic institutions that
train physicians, residents, nurses,
or other health professionals, which
are known to have higher costs than
non-teaching hospitals (Rosko,
2004). Ownership status (for-profit,
not-for-profit, or government) was
included in analyses to account for
related internal pressures aimed at
reducing costs (Nedelea & Fannin,
2012).

Market characteristics. A hos-
pital’s operating environment and
market demand for healthcare
services can also influence hospi-
tal financial performance and mar-
ket share (Whiteis, 1992). Total
population in the market, market
population density, and percent of
the population age 65 and over
describe the demand for hospital
services in the market area (coun-
ty). The average per capita
income, unemployment rate, and

113NURSING ECONOMIC$/May-June 2018/Vol. 36/No. 3

poverty rate (percentage of fami-
lies or persons in poverty) meas-
ure a community’s financial abili-
ty to purchase healthcare services
(NORC Walsh Center for Rural
Health Analysis, 2004). The likeli-
hood of a resident to bypass a hos-
pital and seek services at another
facility is proxied by the average
distance from a patient’s residence
to the next closest hospital, calcu-
lated as the average distance in
miles between residence ZIP code
centroid of each Medicare dis-
charge and hospital (McCue &
Nayar, 2009). Patients may decide
to bypass local facilities due to the
perceived quality of care at desig-
nated hospitals, but may be
deterred due to higher fees and
long distances. Hospital competi-
tion evaluates the number of hos-
pitals physically located in a mar-
ket area and is a measure of sup-
pliers of healthcare services.
There is a large variation in the
location of MR hospitals. Region
controls for the effect of hospital
location. Annual unmeasured fac-
tors affecting hospital reimburse-
ment and market share over time
are accounted for using year indi-
cators.

Analysis
Descriptive statistics were

used to summarize the data. To
mitigate the effect of outliers,
dependent variables were win-
sorized at the 1st and 99th per-
centiles. Bivariate analysis was
used to test for differences between
the subgroup means for MR hospi-
tals versus never-MR hospitals.
The differences between the group
mean on each measure were ana-
lyzed for direction and statistical
significance using t-tests for contin-
uous variables and chi-square tests
for categorical variables. Statistical
significance was set at a=0.05 for
all analyses. Correlation analysis
was completed to identify poten-
tial multicollinearity among the
independent variables. A differ-
ence-in-difference model with hos-
pital fixed effects was used to esti-
mate the effects of MR on both

reimbursement and hospital mar-
ket share. The analysis was con-
ducted using Stata 11.1 (StataCorp,
College Station, TX).

Results
Bivariate statistics. Descriptive

statistics comparing MR hospitals
and never-MR hospitals are shown
in Table 1. Results show MR hospi-
tals received higher reimburse-
ment and have higher market
share than never-MR hospitals.
The net patient revenue per adjust-
ed patient day for MR hospitals
was $3,518 vs. $3,118 for never-
MR hospitals (p=0.000) and the
hospital market share for MR was
19.5% vs. 18% for never MR hos-
pitals (p=0.045).

Multivariate statistics. Differ –
ence-in-difference estimates of the
effect of the MR signal on hospital
reimbursement and market share,
controlling for hospital and market
characteristics and including hos-
pital fixed effects, is shown in
Table 2. The interaction of MR and
the post-recognition phase is a
measure of the net effect of the MR
signal on reimbursement and hos-
pital market share after full imple-
mentation of MR. This effect is
defined as the difference in the
outcome (reimbursement and hos-
pital market share) between MR
and never-MR hospitals and
between post-recognition and pre-
recognition attributed to the MR
signal. Results indicate the rela-
tionship between the MR signal
and both reimbursement and hos-
pital market share is modest (1.7%
increase in revenue and a 0.21 per-
centage point increase in market
share) but neither is statistically
significant. The results also indi-
cate reimbursements are 11%
higher for for-profit hospitals than
for government hospitals and
3.3% lower for teaching-affiliated
hospitals compared to nonteach-
ing-affiliated hospitals. Medicare
payer mix, population density,
percent of population age 65 and
over, hospital competition, and
year variables are significantly
associated with hospital market

share. All the year variables (2001-
2010) are associated with a signifi-
cant increase in hospital market
share compared to the year 2000,
suggesting increasing market share
over the 11-year period. This can
be attributed to an increase in the
total number of discharges per hos-
pital per year potentially due to
population growth in the hospi-
tal’s market area. Hospital-level
factors that are static (some beds
and region) are not included
because hospital fixed effects sub-
sume those factors.

Discussion
Hospitals pursue MR for a vari-

ety of reasons. These reasons may
include, but are not limited to, dis-
tinguishing themselves in the mar-
ketplace (Smith, 2007b; Stimpfel et
al., 2014), increasing market share
(Gaguski, 2006; Stimpfel et al.,
2016), and negotiating better reim-
bursement rates with payers
(Smith, 2007b; Stimpfel et al.,
2014), all of which may result in
potential increases in revenues
(Shetty, 1993). The relationship
between reasons for pursuing MR
and possible increases in revenues
and market share is rationalized
through the reputation effect of
MR, which is conceptualized here
to be a marker of distinction. The
designation provides an opportuni-
ty to promote the institution’s suc-
cess and serves as a signal to the
public that it is recognized as a
place to receive high-quality care
(Aiken, Havens, & Sloane, 2000).
Also, the designation acknowl-
edges that nursing care makes a
positive contribution to patient
outcomes (Grindel & Roman,
2005), thus attracting patients.

Despite these firmly held
beliefs about the reputational
effects of MR, and in contrast to
findings from previous descriptive
studies (Smith, 2007b), the results
of this analysis indicate that the MR
signal does not have an effect on
either hospital reimbursement or
hospital market share. Increases in
patient volume, which increases
hospital market share, are influ-

NURSING ECONOMIC$/May-June 2018/Vol. 36/No. 3114

Ta
bl
e
1.

Su
m
m
ar
y
St
at
is
tic
s
of
D
ep
en
de
nt
a
nd
In
de
pe
nd
en
t V

ar
ia
bl
es
(N

=6
,5
81
H
os
pi
ta
l Y
ea
r O

bs
er
va
tio
ns
) f
ro
m
2
00
0-
20
10

Al
l H

os
pi
ta
ls

(N
=6
,5
81
h
os
pi
ta
l y
ea
r

ob
se
rv
at
io
ns
&

1,
15
5
ho
sp
ita
ls
)

Ne
ve
r-M

ag
ne
t R

ec
og
ni
tio
n

Ho
sp
ita
ls
(N

=5
,2
46
h
os
pi
ta
l

ye
ar
o
bs
er
va
tio
ns
&

92
4
ho
sp
ita
ls
)

M
ag
ne
t R

ec
og
ni
tio
n
Ho

sp
ita
ls

(N
=1
,3
35
h
os
pi
ta
l

ye
ar
o
bs
er
va
tio
ns
&

23
1
ho
sp
ita
ls
)

p-
Va
lu
e

M
ea
n

St
an
da
rd

De
vi
at
io
n

M
ea
n

St
an
da
rd

De
vi
at
io
n

M
ea
n

St
an
da
rd

De
vi
at
io
n

De
pe
nd
en
t V
ar
ia
bl
e

Re
im
bu
rs
em
en
t*

3,
20
3.
72

1,
05
1.
92

3,
11
7.
64

1,
00
6.
67

3,
51
8.
54

1,
14
9.
75

0.
00
0

Ho
sp
ita
l m
ar
ke
t s
ha
re
(%
)

18
.3
0

24
.3
0

18
.0
0

24
.4
0

19
.5
0

23
.9
0

0.
04
5

M
ag
ne
t H

os
pi
ta
l R

ec
og
ni
tio
n
St
at
us

Pr
e-
re
co
gn
itio
n
(%
)

33
.1
0

–
33
.1
0

–
33
.0
0

–
0.
89
5

Im
pl
em
en
ta
tio
n
(%
)

34
.5
0

–
34
.5
0

–
34
.4
0

–
0.
92
4

Po
st
-re
co
gn
itio
n
(%
)

32
.4
0

–
32
.3
0

–
32
.7
0

–
0.
81
8

Ho
sp
ita
l C

ha
ra
ct
er
is
tic
s

Ho
sp
ita
l s
ize
(t
ot
al
b
ed
s)

44
5.
00

33
3.
00

43
9.
00

34
0.
00

46
6.
00

30
2.
00

0.
08
0

Sy
st
em
a
ffil
ia
tio
n
(%
)

29
.7
0

–
29
.7
0

–
29
.6
0

–
0.
94
8

M
ed
ica
re
p
ay
er
m
ix
(%
)

38
.5
0

14
.3
0

38
.3
0

15
.0
0

39
.0
0

11
.3
0

0.
09
4

M
ed
ica
id
p
ay
er
m
ix
(%
)

11
.9
0

9.
70

12
.0
0

10
.1
0

11
.5
0

7.
83

0.
09
0

No
t-f
or
-p
ro
fit
h
os
pi
ta
l (
%
)

86
.1
0

–
86
.0
0

–
86
.2
0

–
0.
84
4

Fo
r-p
ro
fit
h
os
pi
ta
l (
%
)

4.
30

–
4.
40

–
4.
00

–
0.
50
5

G
ov
er
nm
en
t h
os
pi
ta
l (
%
)

9.
60

–
9.
60

–
9.
80

–
0.
82
0

Te
ac
hi
ng
a
ffil
ia
tio
n
(%
)

66
.6
0

–
66
.5
0

–
67
.2
0

–
0.
66
2

M
ar
ke
t C

ha
ra
ct
er
is
tic
s

Po
pu
la
tio
n
(1
,0
00
s)

3,
58
8.
04

4,
78
2.
68

3,
57
1.
45

4,
80
0.
64

3,
65
3.
22

4,
71
2.
57

0.
57
7

Po
pu
la
tio
n
de
ns
ity

72
6.
00

73
7.
00

72
2.
00

74
2.
00

74
2.
00

71
8

0.
37
6

Pe
rc
en
t o
f p
op
ul
at
io
n
65
a
nd
o
ve
r (
%
)

12
.0
0

2.
45

12
.0
0

2.
48

12
.1
0

2.
35

0.
42
2

In
co
m
e

37
,1
92
.0
5

7,
13
7.
81

37
,1
39
.1
8

7,
10
4.
58

37
,3
99
.7
9

7,
26
5.
89

0.
23
4

Un
em
pl
oy
m
en
t r
at
e
(%
)

5.
29

1.
79

5.
30

1.
79

5.
25

1.
79

0.
32
7

Po
ve
rty
ra
te

11
.5
0

2.
52

11
.5
0

2.
55

11
.6
0

2.
43

0.
07
5

Di
st
an
ce
fr
om
re
sid
en
ce
to
h
os
pi
ta
l (
m
ile
s)

16
.6
0

8.
56

16
.7
0

8.
61

16
.2
0

8.
35

0.
04
6

Ho
sp
ita
l c
om
pe
tit
io
n

10
.3
0

14
.5
0

10
.2
0

14
.6
0

10
.9
0

14
.1

0.
13
5

co
nt

in
ue

d
on

n
ex

t p
ag

e

115NURSING ECONOMIC$/May-June 2018/Vol. 36/No. 3

Ta
bl
e
1.
(c
on
tin
ue
d)

Su
m
m
ar
y
St
at
is
tic
s
of
D
ep
en
de
nt
a
nd
In
de
pe
nd
en
t V

ar
ia
bl
es
(N

=6
,5
81
H
os
pi
ta
l Y
ea
r O

bs
er
va
tio
ns
) f
ro
m
2
00
0-
20
10

Al
l H

os
pi
ta
ls

(N
=6
,5
81
h
os
pi
ta
l y
ea
r

ob
se
rv
at
io
ns
&

1,
15
5
ho
sp
ita
ls
)

Ne
ve
r-M

ag
ne
t R

ec
og
ni
tio
n

Ho
sp
ita
ls
(N

=5
,2
46
h
os
pi
ta
l

ye
ar
o
bs
er
va
tio
ns
&

92
4
ho
sp
ita
ls
)

M
ag
ne
t R

ec
og
ni
tio
n
Ho

sp
ita
ls

(N
=1
,3
35
h
os
pi
ta
l

ye
ar
o
bs
er
va
tio
ns
&

23
1
ho
sp
ita
ls
)

p-
Va
lu
e

M
ea
n

St
an
da
rd

De
vi
at
io
n

M
ea
n

St
an
da
rd

De
vi
at
io
n

M
ea
n

St
an
da
rd

De
vi
at
io
n

Re
gi
on
W
es
t

14
.7
0

–
15
.1
0

–
13
.3
0

–
0.
10
5

M
id
we
st

31
.5
0

–
31
.1
0

–
33
.4
0

–
0.
09
8

No
rth
ea
st

19
.4
0

–
19
.0
0

–
21
.0
0

–
0.
08
9

So
ut
h

34
.3
0

–
34
.9
0

–
32
.0

–
0.
06
8

Ye
ar 20
00
(%
)

3.
90

–
3.
90

–
3.
90

–
0.
95
8

20
01
(%
)

6.
60

–
6.
60

–
6.
60

–
0.
99
6

20
02
(%
)

9.
00

–
9.
00

–
9.
00

–
0.
97
5

20
03
(%
)

10
.8
0

–
10
.8
0

–
10
.9
0

–
0.
92
4

20
04
(%
)

12
.7
0

–
12
.7
0

–
12
.6
0

–
0.
91
3

20
05
(%
)

13
.9
0

–
14
.0
0

–
13
.9
0

–
0.
98
4

20
06
(%
)

13
.4
0

–
13
.4
0

–
13
.3
0

–
0.
94
9

20
07
(%
)

10
.7
0

–
10
.7
0

–
10
.7
0

–
0.
98
3

20
08
(%
)

8.
20

–
8.
20

–
8.
30

–
0.
85
3

20
09
(%
)

6.
40

–
6.
40

–
6.
30

–
0.
92
0

20
10
(%
)

4.
40

–
4.
40

–
4.
50

–
0.
86
1

*A
dj
us
te
d
fo
r 2
01
0
do
lla
rs
a
cc
or
di
ng
to
th
e
C
on
su
m
er
P
ric
e
In
de
x.

NURSING ECONOMIC$/May-June 2018/Vol. 36/No. 3116

Ta
bl
e
2.

Di
ffe

re
nc
e-
in
-D
iff
er
en
ce
R
eg
re
ss
io
n
w
ith
H
os
pi
ta
l-F
ix
ed
E
ffe

ct
s

De
pe
nd
en
t V

ar
ia
bl
e

In
(R

ei
m
bu
rs
em

en
t)

Ho
sp
ita
l M

ar
ke
t S

ha
re
(%

)
Co

ef
fic
ie
nt

Ro
bu
st
S
Es

Co
ef
fic
ie
nt

Ro
bu
st
S
Es

Ho
sp
ita
l I
nt
er
ve
nt
io
n

M
ag
ne
t R
ec
og
ni
tio
n
H
os
pi
ta
la,
f

–
–

–
–

M
ag
ne
t R

ec
og
ni
tio
n
Ho

sp
ita
l S

ta
tu
s

Im
pl
em
en
ta
tio
nb

-0
.0
11

0.
00
59

-0
.1
8*

0.
08
3

Po
st
-re
co
gn
iti
on

b

-0
.0
10

0.
00
88

-0
.2
4

0.
14

M
ag
ne
t R
ec
og
ni
tio
n
im
pl
em
en
ta
tio
n*

0
.0
03
7

0.
01
0

0
.3
1

0.
17

M
ag
ne
t R
ec
og
ni
tio
n
po
st
-re
co
gn
iti
on
*

0
.0
17

0.
01
1

0
.2
1

0.
22

Ho
sp
ita
l C

ha
ra
ct
er
is
tic
s

H
os
pi
ta
l s
iz
ef

–
–

–
–

Sy
st
em
a
ffi
lia
tio
n

-0
.0
08
4

0.
00
74

-0
.0
31

0.
09
9

M
ed
ic
ar
e
pa
ye
r m
ix

0
.0
02
0

0.
00
12

0
.0
58
**

0.
01
6

M
ed
ic
ai
d
pa
ye
r m
ix

-0
.0
01
8

0.
00
08
4

-0
.0
05
1

0.
01
1

N
ot
-fo
r-p
ro
fit
h
os
pi
ta
le

0
.0
46

0.
03
8

-0
.0
04
2

0.
22

Fo
r-p
ro
fit
h
os
pi
ta
le

0
.1
1*

0.
05
2

0
.3
6

0.
50

Te
ac
hi
ng
a
ffi
lia
tio
n

-0
.0
33
*

0.
01
3

0
.4
8

0.
40

M
ar
ke
t C

ha
ra
ct
er
is
tic
s

Po
pu
la
tio
n
(1
,0
00
s)

0
.0
00
02
1

0.
00
00
75

0
.0
00
53

0.
00
07
9

Po
pu
la
tio
n
de
ns
ity

0
.0
00
23

0.
00
04
4

-0
.0
18
**

0.
00
61

Pe
rc
en
t o
f p
op
ul
at
io
n
65
a
nd
o
ve
r

0
.0
29
5

0.
01
68

-0
.7
0*

0.
29

In
co
m
e

0
.0
00
00
12

0.
00
00
02
3

-0
.0
00
05
3

0.
00
00
52

U
ne
m
pl
oy
m
en
t r
at
e

0
.0
00
20

0.
00
37

-0
.1
1

0.
07
7

Po
ve
rty
ra
te

0
.0
00
17

0.
00
24

0
.0
25

0.
07
3

D
is
ta
nc
e
fro
m
re
si
de
nc
e
to
h
os
pi
ta
l (
m
ile
s)

-0
.0
02
5

0.
00
26

-0
.0
99

0.
05
6

H
os
pi
ta
l c
om
pe
tit
io
n

0
.0
02
7

0.
00
25

-0
.1
3*
*

0.
03
2

117NURSING ECONOMIC$/May-June 2018/Vol. 36/No. 3

Ta
bl
e
2.
(c
on
tin
ue
d)

Di
ffe

re
nc
e-
in
-D
iff
er
en
ce
R
eg
re
ss
io
n
w
ith
H
os
pi
ta
l-F
ix
ed
E
ffe

ct
s

De
pe
nd
en
t V

ar
ia
bl
e

In
(R

ei
m
bu
rs
em

en
t)

Ho
sp
ita
l M

ar
ke
t S

ha
re
(%

)
Co

ef
fic
ie
nt

Ro
bu
st
S
Es

Co
ef
fic
ie
nt

Ro
bu
st
S
Es

Re
gi
on

N
or
th
ea
st
d,
f

–
–

–
–

M
id
w
es
td,
f

–
–

–
–

So
ut
hd
,f

–
–

–
–

Ti
m
e Ye
ar
2
00
1c

-0
.0
09
1

0.
01
2

0.
27
*

0.
13

Ye
ar
2
00
2c

0
.0
03
9

0.
01
5

0.
55
*

0.
25

Ye
ar
2
00
3c

0
.0
21

0.
01
7

0.
71
*

0.
32

Ye
ar
2
00
4c

0
.0
26

0.
02
0

1.
03
*

0.
41

Ye
ar
2
00
5c

0
.0
25

0.
02
2

1.
13
*

0.
51

Ye
ar
2
00
6c

0
.0
28

0.
02
7

1.
41
*

0.
64

Ye
ar
2
00
7c

0
.0
27

0.
03
1

1.
84
*

0.
73

Ye
ar
2
00
8c

0
.0
01
5

0.
03
7

2.
65
**

0.
87

Ye
ar
2
00
9c

0
.0
20

0.
04
4

3.
31
**

0.
97

Ye
ar
2
01
0c

0
.0
38

0.
04
9

3.
81
**

1.
08

C
on
st
an
t

7
.3
0*
*

0.
34

3
9.
48
**

6.
37

N
um
be
r o
f H
os
pi
ta
l Y
ea
r O
bs
er
va
tio
ns

6
,1
54

6,
42
8

N
um
be
r o
f H
os
pi
ta
ls

1
,0
98

1,
13
6

F
St
at
is
tic

(2
8,
1
,0
97
) =
3
.7
2

p=
0.
00
0

(2
8,
1
,1
35
) =
2
.3
8

p=
0.
00
0

a
R
ef
er
en
ce
is
n
ev
er
-M
ag
ne
t h
os
pi
ta
ls
;
b
R
ef
er
en
ce
is
th
e
pr
e-
re
co
gn
iti
on
p
er
io
d;
c
R
ef
er
en
ce
is
y
ea
r 2
00
0;
d
R
ef
er
en
ce
is
w
es
t;
e
R
ef
er
en
ce
is
g
ov
er
nm
en
t h
os
–

pi
ta
ls
;
f T
im
e
in
va
ria
nt
v
ar
ia
bl
es
.

*S
ta
tis
tic
al
ly
s
ig
ni
fic
an
t a
t t
he
5
%
le
ve
l.
**
St
at
is
tic
al
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NURSING ECONOMIC$/May-June 2018/Vol. 36/No. 3118

enced by various intermediate fac-
tors, such as employers, insurers,
managed care organizations, and
referring physicians (Goldstein,
2002). All of these factors may over-
ride the overall effect of the MR sig-
nal on hospital market share.
Regarding hospital reimbursement,
the significant payers include both
the government and private health
insurers. Government payers reim-
burse hospitals using prospective
payment systems and may be less
responsive to adjusting reimburse-
ment rates for MR hospitals. In con-
trast, private health insurers and
managed care organizations may be
more agreeable to negotiating reim-
bursement rates with these hospi-
tals. However, any potential in –
creases in reimbursement rates by
private health insurers may be
diminished by limited increases or
reductions in reimbursement by
government payers.

Implications
Hospital market share. Hos –

pitals publicize their MR designa-
tion to raise community awareness
of their commitment to quality and
to market themselves to patients,
nurses, and the community (Lewis
& Matthews, 1998). Hospitals have
used full-page newspaper adver-
tisements, billboards, websites, and
television spots (Havens & Aiken,
1999) to communicate to the public
that the hospital is recognized as a
place to receive high-quality care
and that nursing services make pos-
itive contributions to patient out-
comes (Grindel & Roman, 2005).
Hospitals incur enormous costs in
the promotion of MR.

Despite the use of costly
advertisements and promotional
materials, the study results indi-
cate the MR signal does not appear
to have an effect on hospital mar-
ket share. This may have numer-
ous implications for both the MR
program and hospitals. From the
perspective of the MR program,
both the validity and interpretabil-
ity of the MR signal may be debat-
able; specifically, the signal’s abil-
ity to attract patients and increase

hospital market share. The ambi-
guity of the MR signal may have
undesirable consequences for the
MR program in terms of promot-
ing the designation as a mecha-
nism to increase hospital market
share.

The results may also prompt
hospital chief financial officers
(CFOs) and chief executive officers
(CEOs) to re-evaluate the resources
allocated to the promotion and
advertising of MR. Furthermore, the
marketing strategy used when pro-
moting the MR signal may need to
be reviewed and perhaps revised.
There may be weaknesses in the MR
signal that may explain the study
results. For instance, the benefits of
MR may not be in the message car-
ried by the MR signal. Although MR
hospitals are acknowledged as cen-
ters of nursing excellence and the
gold standard for nursing care, these
accolades and honors may not be
communicated by the MR signal.
The MR signal may also not be inter-
preted by the stakeholders. Patients
and providers may either not associ-
ate quality or nursing excellence
with the MR designation or under-
stand the importance of nursing
services to the delivery of patient
care. Lastly, the MR signal may not
be eliciting the expected response to
the signal. While the MR signal may
be recognized and interpreted,
patients, payers, and providers may
not be responding for various rea-
sons. These reasons may include a
lack of urgency to respond and lim-
ited control in decision making for a
hospital visit. For instance, in the
case of an emergency, when a
patient needs to visit a hospital, the
patient will have limited decision-
making control; the decision will be
determined either by the physician,
paramedics, or the location of the
nearest facility that meets the needs
of patient and provider.

This study’s findings indicate
the MR signal does not appear to
have an effect on hospital market
share, which suggests the signal-
ing effect of MR may be limited.
These results may prompt hospi-
tal CFOs and CEOs to re-evaluate

the resources allocated to the pro-
motion and advertising of MR.
Alternatively, hospitals may bene-
fit from being more selective in
targeting their marketing efforts to
groups that are likely to receive,
believe, and act on the signal.

Hospital reimbursement. Hos –
pitals and payers try to negotiate
reimbursement rates that are fair to
both parties. To gain leverage when
negotiating reimbursement rates,
hospitals often use quality metrics
that demonstrate the hospital is
achieving high-quality standards.
Quality metrics emphasize the hos-
pital’s commitment to quality man-
agement and may even give hospi-
tals an added advantage in negotia-
tions.

Regardless of the many positive
outcomes for nurses, patients, and
organizations associated with MR
(Hess et al., 2011; Ulrich et al., 2009)
and the emphasis on quality and
safety in patient care (Drenkard,
2010), this study indicates MR sig-
nal does not have an effect on hos-
pital reimbursement. From the per-
spective of hospitals, CFOs and
CEOs may not be leveraging MR sig-
nal as a means to highlight their
quality accomplishments to negoti-
ate better reimbursement rates from
payers.

However, MR may not signify
differential quality because many
hospitals have this designation.
With an increase in the number of
hospitals with MR or an increase
in other signals used by hospitals,
the MR signal may lose its distinc-
tiveness and become weakened in
the presence of other signals. In
this case, hospitals are no longer
able to differentiate themselves as
centers of excellence in nursing
care.

Limitations
There are a few limitations

associated with this study. First,
several variables of interest were
not included in the analysis due to
inaccessibility of the data, poten-
tially resulting in biased parame-
ter estimates. However, use of
fixed effects regression was

119NURSING ECONOMIC$/May-June 2018/Vol. 36/No. 3

intended to control for unmea-
sured fixed hospitals characteris-
tics which may be time invariant.
Second, although the analysis
attempted to match MR hospitals
to never-MR hospitals, no two
hospitals are similar in all aspects
respects. Propensity scores only
control for observed variables and
do not consider the effect of unob-
served variables in the decision of
hospitals to seek MR. This non-
random decision to seek MR could
result in biased parameter esti-
mates of the likelihood of MR.
Lastly, although the ANCC web-
site lists the current MR-designat-
ed hospitals, it does not provide
information on hospitals that
applied for MR but were unsuc-
cessful, hospitals transforming to
become MR, or hospitals that had
their MR status rescinded due to
noncompliance with the pro-
gram’s requirements. As a result,
the never-MR comparators could
include hospitals that had at one
time engaged in the MR program.

Conclusion
MR is not intended to improve

hospital reimbursement or hospital
market share. However, the MR sig-
nal is promoted as a means to
inform payers, patients, and
providers about the hospital’s com-
mitment to quality and patient care,
consequently leading to increases in
market share and increases in reim-
bursement. However, the results
from this study indicate MR signal
has no effect on these outcomes.
Possible explanations may include
the strength of the MR signal, mes-
sage carried by the MR signal, inter-
pretation of the message in the MR
signal, and response or action after
receiving the MR signal. All of these
components that determine a sig-
nal’s effectiveness may have con-
tributed to the results found in this
study.

The pursuit of MR is an essen-
tial organizational decision that
results in substantial modifications
to the organization’s structures and
processes and requires a consider-
able investment of time and

resources, initially and ongoing as
the process continues. Knowledge
of the limited benefit of the MR sig-
nal on hospital reimbursement and
hospital market share may per-
suade CEOs and CFOs of MR hos-
pitals to consider being more
strategic, cautious, and reserved in
their allocation of advertising and
marketing resources to other areas
that may be positively impacted by
the MR signal. The MR signal may
be better positioned to increase the
reputational benefits by continuing
to educate payers, patients, and
providers about the positive
impacts of MR on hospital quality
and patient outcomes. Knowledge
of the limited benefit of the MR sig-
nal on hospital reimbursement and
hospital market share may cause
CEOs and CFOs of MR hospitals to
re-evaluate the advertising and
market resources dedicated to pro-
moting and signaling the designa-
tion to patients, providers, and
payers. Resources may be allocated
to other pathways that are positive-
ly impacted by the MR signal. $

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January/February 2020 | Volume 38 Number 1 7

Nursing Economic$

Presently, the United States is in the midst of a major, unprecedented
public health opioid epidemic
that traverses race, ethnicity,
gender, age, health status, and
socioeconomic level (Brinkley-
Rubinstein et al., 2018; Chimbar
& Moleta, 2018; Cox & Naegle,
2019). The opioid epidemic,
now a crisis in America, reflects
the deadliest in history with pre-
dictions the death toll will con-
tinue to escalate in years to
come (Bennet et al., 2018).
Despite billions of dollars allo-
cated to address the opioid epi-
demic, the crisis has worsened,
with more deadly outcomes
(Johnson, 2018). More opioid-
related deaths occur per year
than mortality from recent wars,
motor vehicle accidents, gun
violence, and human immuno –
deficiency virus (Centers for
Disease Control and Prevention
[CDC], 2019a; Siegel, 2018;
Velander, 2018). In 2016, the
national death rate from opioid
overdose was 21.7 deaths per
100,000, reflecting a dramatic
increase since 2013 when the
U.S. rate was 7.6 deaths per
100,000 (National Institute of
Drug Abuse, 2019).

Recognizing that nurses are
the most trusted of health pro-
fessionals, account for the
largest number of healthcare
providers, and have the most
frequent interpersonal contact
with patients and families, the
important role of nurses in com-
batting the opioid crisis has
been recognized (American
Association of Colleges of
Nursing [AACN], 2019a;
American Nurses Association,
2016). The purpose of this inte-
grative review is to examine the
economic burden of the opioid
epidemic based on published
evidence to inform practice,
education, research, and policy
development in nursing to com-
bat this escalating crisis.

Method

In this integrative review,
the authors used interprofession-
al academic and federal analysis
literature published in English
between 2013 and 2019. Seven
electronic databases were used
to identify relevant published
articles and included Directory
of Open Access Journals,
EBSCOhost, Elsevier, Google
Scholar, ProQuest Document,

The Economic Impact of the Opioid
Use Disorder Epidemic in America:
Nurses’ Call to Action
Kathleen Neville
Marie Foley

The unprecedented public health
opioid epidemic in America has
created a tremendous economic
burden. Exorbitant costs from
premature mortality, criminal
justice, childcare and family
assistance, lost productivity, and
healthcare services are
skyrocketing. Given the
escalating economic burden of
this national crisis, nurses as
frontline providers are called to
action to combat the opioid
epidemic through the provision
of comprehensive, cost-effective,
humanistic levels of prevention,
including primary, secondary,
and tertiary care.

January/February 2020 | Volume 38 Number 18

PubMed, and Science Direct.
Key terms for these sites includ-
ed cost opioid, cost opioid crisis,
history opioid cost, opioid epi-
demic cost, opioid cost individ-
ual, and opioid cost family.

The last search identified
approximately 70 potential arti-
cles; 60 abstracts were reviewed
based on inclusion criteria, and
eight were excluded because
they were not fully relevant to
the research topic of economics
and the opioid epidemic. This
literature review resulted in 52
articles that had the most recent
data related to the cost of opi-
oid use disorder (OUD) in the
United States.

Background

OUDs are so prevalent in
the United States that a national
emergency to address this pub-
lic health crisis was declared in
2017 (Broglio & Matzo, 2018;
Hedegaard, Warner, & Minino,
2017). According to CDC data,
there were 42,249 opioid-related
deaths in 2016 (CDC, 2019b),
accounting for more than 66%
of all overdose deaths (CDC,
2018). Every day 115 Americans
die from opioid overdoses
(CDC, 2019a). The mortality rate
has increased to 130 deaths per
day (AACN, 2019b). More than
1,000 people are treated in
emergency departments for mis-
using prescription opioids (CDC,
2017). “From 1999 to 2016,
greater than 630,000 individuals
died from a drug overdose, with
opioid-related overdoses
increasing five times since 1999”
(Fornili, 2018, p. 215).

The current opioid epidemic
involves the misuse and abuse

of both prescription drugs and
illegal drugs such as heroin and
fentanyl. While it is not the first
drug crisis in America, it is the
deadliest and most costly in
terms of lives lost, decreased life
expectancy, lost productivity,
crime, violence, and the devas-
tating impact of addiction on
families and communities. In
earlier epidemics, in the 19th
and 20th centuries, the liberal
use of laudanum to treat pain
and the influx of opium dens
from Chinese immigrants in
America created a state of alarm,
and physicians began addiction
management with the use of
opioids (Velander, 2018). It was
not until the Harrison Act of
1914 that restrictions of opioids
for pain management existed.
Similar to current, albeit hope-
fully changing views, the
Harrison Act depicted opioid
dependence as criminal activity,
representing a moral weakness,
and was not viewed as a med-
ical condition (Velander, 2018).
Under this act, the use of opi-
oids for addiction treatment was
prohibited and 30,000 physi-
cians were then prosecuted for
unlawful use of prescribing opi-
oids.

It was not until the 1970s
that methadone became a legal
opioid to treat addiction man-
agement in the United States
and was authorized to be dis-
pensed in federally designated
clinics (Velander, 2018).
Buprenorphine, an opioid par-
tial agonist, was developed in
the 1970s as an alternative to
methadone. Buprenorphine pre-
vents withdrawal symptoms and
cravings, prevents abuse of
other opioids, and requires less

federal regulations for dispens-
ing (can be prescribed in office
settings) than methadone. In
2000, the Drug Addiction
Treatment Act (DATA) author-
ized physicians via a DATA
waiver to prescribe medication-
assisted treatment (MAT) for
OUDs. Upon obtaining a DATA
waiver, legislation has expanded
buprenorphine prescribing prac-
tices for nurse practitioners and
physician assistants for initial
treatment of 30 patients, fol-
lowed by 100 patients annually
(Cadet & Tucker, 2019). In sum-
mary, from a historical perspec-
tive, “opioid addiction has been
recognized as a difficult prob-
lem to treat with low recovery
rates” (Velander, 2018, p. 1), and
remains so today.

This current escalating epi-
demic has its origins beginning
in the 1980s when pharmaceuti-
cal companies misinformed
physicians that addiction from
narcotic use was unlikely. A let-
ter on the risk of opioid addic-
tion published in 1980 in the
New England Journal of
Medicine concluded that addic-
tion was indeed rare when
long-term opioids were pre-
scribed for pain management
(Leung et al., 2017).
Additionally, Purdue Pharma
extensively marketed
Oxycontin® (oxycodone) to
physicians, providing lucrative
incentives for increased pre-
scription use (Macy, 2018). “In
2000, throughout the pharma-
ceutical industry, $4.04 billion
was spent on direct marketing
to physicians” (Macy, 2018, p.
32). However, in 2007, “the
manufacturers of Oxycontin,
along with senior executives

Nursing Economic$

January/February 2020 | Volume 38 Number 1 9

pled guilty to misleading regula-
tors, physicians and patients
regarding the risks of addiction
with Oxycontin” (Leung et al.,
2017, p. 2194).

Other major factors respon-
sible for this escalating crisis
relate to practice changes that
occurred in the 1990s. In
response to the need to more
effectively treat pain, pain
assessment as a fifth vital sign
became a standard nursing prac-
tice initiative in acute care set-
tings, followed by the liberal
use of prescribing narcotics to
treat patient-reported pain. What
followed was a dramatic
increase in opioid prescription
use, accompanied by highly
publicized false security and the
myth opioid addiction was not
probable and highly unlikely
when used for pain manage-
ment. It is now well known that
addiction can occur quickly,
even with short-term use; the
possibility exists that some indi-
viduals can become addicted
with only one opioid prescrip-
tion (Barnett et al., 2017).
Consequently, along with
increased prescription use came
significantly increased mortality
affecting children, teens, adults,
and even newborns, who were
exposed to opioids in utero and
born suffering from neonatal
abstinence syndrome.

Especially vulnerable to the
risks of opioid use are the elder-
ly, who due to opioid-sedating
effects, may succumb to falls,
fractures, and other potentially
life-threatening events as well as
addiction (Barnett et al., 2017).
Young adults, equally prevalent
among males and females, rep-
resent the largest numbers of

heroin users (Cicero et al., 2014;
Fogger & McGuiness, 2015).
However, the greatest mortality
due to opioid-analgesia has
occurred in the 55-64 age group
(Chen et al., 2014). Between the
years 1999 and 2013, the mortal-
ity rate from opioid use for anal-
gesia resulted in a nearly
quadrupled overdose rate (Sofer,
2019; Substance Abuse and
Mental Health Services
Administration [SAMHSA],
2019a). Over the last 3 years,
there has been a decline in the
rate of opioid prescriptions, a
19% reduction since 2006 (Sofer,
2019). However, this decrease in
prescribing has not significantly
impacted opioid use and related
overdoses.

Additional factors fueling the
epidemic are illicit drugs. These
drugs include the influx of hero-
in from Mexico, as well as the
rise of extremely potent synthet-
ic opioids such as fentanyl and
carfentanil, which is 10,000
times more potent than mor-
phine, and tramadol (CDC,
2019c; Velander, 2018). Once
prescription drugs became unat-
tainable, a typical pattern result-
ed in illicit street drug use, con-
sisting of predominantly heroin,
but frequently combined with
additional potentially lethal sub-
stances resulting in an increased
mortality rate from overdose
since 2010 (CDC, 2017).

The Economic
Consequences of the
Opioid Epidemic

The opioid crisis in America
has created a tremendous eco-
nomic burden. According to the
Council of Economic Advisors
(CEA, 2017), “in 2015, the eco-
nomic cost of the opioid crisis
was $504.0 billion or 2.8% of
the gross domestic product” (p.
1) and has risen substantially
(see Table 1). Since 2001, fig-
ures reflect the costs exceeding
$1 trillion (Rhyan, 2017). These
figures may reasonably be
underestimated, predominantly
due to underreporting of fatali-
ties due to heroin and other
illicit drug use, as well as the
associated incidence of suicide.

In 2018, the estimated costs
to the U.S. economy from the
opioid epidemic rose to $631
billion (Siegel, 2019). Critical
components of this financial
burden and estimates of per-
centage of specific expenditures
are as follows: health care
(33%), premature death (40%),
criminal justice (6%), child and
family assistance and education-
al programs (6%), and lost pro-
ductivity (15%) (see Table 2).

The cost of premature fatali-
ties is due to lost potential earn-
ings. It is estimated by the
“value of a statistical life” (CEA,
2017, p. 3), which is age-depen-

Nursing Economic$

Table 1.
U.S. Economic Cost of the Opioid Epidemic

Year

2015 2018

Cost $504 billion $631 billion

Source: Siegel, 2019.

January/February 2020 | Volume 38 Number 110

dent and can range from $221.6
billion to $549.8 billion. It was
estimated the total cost of non-
fatal opioid use as a result of
lost productivity, health care,
and criminal justice system costs
were $72.7 billion in 2015 (CEA,
2017) and an estimated overall
societal cost of $78.5 billion in
2016 (Leslie et al., 2019). Lost
productivity, specifically, absen-
teeism and work impairment,
involves not only the abuser,
but also family members, close
friends, and associates.

Criminal Justice Costs
A substantial cost related to

nonfatal consequences involves
the criminal justice system,
which consists of the following
components: police protection,
legal and adjudication, correc-
tional facilities, and property
loss due to crimes (Florence et
al., 2016; Rhyan, 2017). Crime
and violence as a sequela to
opioid use is a significant cost
and involves both the abusers
and the victims. It is estimated
the opioid epidemic has
increased criminal justice costs
in America by $7.8 billion
(Florence et al., 2016), and a
more recent finding reveals the
current cost to be $8 billion
(Ropero-Miller & Speaker, 2019).

Healthcare Costs
The opioid epidemic has

fueled an excessive financial
burden to the nation, including
federal, state, and local govern-
ments as well as private health-
care plans and society at large.
Between the years of 2001 and
2017, U.S. healthcare expendi-
tures topped $215.7 billion
(Litton, 2018). Federal costs
(Medicare, Medicaid, SAMHSA,
and CHAMPVA) accounted for
14% of the financial burden
related to the epidemic.
Combined with the criminal jus-
tice costs, this accounts for 25%
of the total economic weight
funded by society (Florence et
al., 2016). Additionally, health-
care plans have endured signifi-
cant financial burdens. Two fed-
eral laws, the Affordable Care
Act and the Mental Health Parity
and Addiction Equity Act,
expanded behavioral health
plans and provision of services,
and eliminated lifetime mone-
tary limits, substantially increas-
ing the use of services.

Concomitantly with the opi-
oid epidemic, new drug treat-
ment programs developed
nationwide, some of which
engaged in unscrupulous and
unethical practices to increase
revenue (Johnson, 2018). In

many cases, these private treat-
ment programs were out of net-
work, and individuals and fami-
lies incurred excessively high
financial costs. Similar to the
pharmaceutical industry, these
private treatment programs uti-
lized skillful marketing tech-
niques to attract vulnerable
patients with financial resources.

Rates and costs of opioid-
related admissions have
increased dramatically and the
escalation in numbers, and costs
of hospitalizations indicate a
threat to the financial solvency
of U.S. hospitals (Hsu et al.,
2017). These costs stem primari-
ly from emergency services,
emergency room visits to man-
age overdoses, hospital admis-
sions, and the increased costs of
associated illnesses (Litton,
2018). In comparison to treat-
ment costs for other illnesses
such as diabetes or renal disease
(range $3,560-$5,624), MAT for
OUDs reflects substantially high-
er costs ($5,980-$14,112) per
year (Agency for Healthcare
Research and Quality, 2016).

International Opioid
Epidemic

While OUDs exist world-
wide, the United States is facing
a substantially larger epidemic.
While the United States repre-
sents 4% of the world’s popula-
tion, 27% of the world’s mortali-
ty from drug overdose occurs in
the United States (United
Nations Office on Drugs and
Crime, 2016). Residents in the
United States consume more
opioids than any other popula-
tion in the world. For example,
in France and Italy, the inci-

Nursing Economic$

Table 2.
Financial Burden of Opioid Epidemic

Expenditures Costs from 2015-2018

Health care $205 billion
Premature deaths $253 billion
Criminal justice $39 billion
Childcare/Family assistance $39 billion
Lost productivity $96 billion

Source: Managed Healthcare Executive Staff, 2019; Siegel, 2019.

January/February 2020 | Volume 38 Number 1 11

dence of chronic pain is similar
to the per capita rate in
America. Yet, consumption of
opioids in the United States is
six to eight times greater
(Humphreys, 2018). Nearly
100% of hydrocodone and 81%
of oxycontin are consumed by
Americans and are prescribed

for pain management. Another
contributing factor related to
over-prescription in the United
States is that fewer regulations
exist for drug manufacturers and
distributors, as compared to
other developing nations
(Humphreys, 2018).

An additional factor con-

tributing to the increase in opi-
oid use in the United States is
related to the healthcare indus-
try’s focus on addressing patient
needs and satisfaction, which
has resulted in health profes-
sionals’ liberal overprescribing
and often resulting in unused
medications including opioids.

Nursing Economic$

Table 3.
Call to Action: Advocacy in Nursing

Policy 1. Endorse organizational initiatives and advocate for policy development to
address the opioid epidemic through local, regional, state, and national
development of OUD programs.

Education 1. Develop and implement curricula based on best practice for treatment of OUDs
in undergraduate and graduate nursing programs nationwide.

2. Introduce a new paradigm of OUD as a chronic neuropsychobiological disease
capable of recovery to decrease stigma.

3. Increase the number of nurse practitioners to prescribe medication-assisted
treatment.

Nurse leaders 1. Design and provide continuing education programs for practicing nurses to treat
individuals with OUDs with best evidence interventions.

2. Foster assimilation of the new paradigm of OUD as a disease with recovery,
rather than moral defect.

3. Establish health economics competencies to manage exorbitant costs of OUD
treatment.

Research 1. Engage in the conduct of diverse research methodologies to expand broad body
of nursing knowledge in humanistic treatment of OUDs, including psychosocial
aspects of treatment.

2. Conduct research investigations on stigma, and development of interventions to
mitigate stigma among individuals, families, communities, societal, and
healthcare professionals.

Practice: Levels of prevention 1. Primary Prevention
a. Protect the public through education on risks, challenges, and need for

support/community services for those in recovery from OUDs.
b. Provide education to mitigate stigma to improve access to treatment for

individuals with OUDs.

2. Secondary Prevention

a. Facilitate early detection and intervention for those at risk (biophysical,
psychological, or social determinants) for all subgroups of population in all
healthcare settings.

3. Tertiary Prevention

a. Provision of nursing services to maximize health states, despite living with a
chronic illness.

b. Provide supportive services to reduce long-term sequelae of OUDs.

OUD = opioid use disorder

January/February 2020 | Volume 38 Number 112

These unused opioids, available
in household medicine cabinets
nationwide, have facilitated
increased recreational use of
opioids by patients, family
members, and friends (diver-
sion); thereby, furthering the
escalation of opioid addiction in
the United States.

Nurses’ Call to Action

Because nurses represent
the largest number of healthcare
providers, are the most trusted
among health professionals,
have the greatest interaction
with patients and families, and
deliver comprehensive, excel-
lent, cost-effective care, nurses
are ideally suited to engage in
action to address the opioid epi-
demic in America. To combat
the opioid epidemic, there is a
call to action for nurses in all
settings, including academia and
practice at all levels of preven-
tion, to engage and advocate for
change to advance science, poli-
cy, education, and practice (see
Table 3).

Policy
Recognizing the severity and

magnitude of the opioid epi-
demic, national healthcare
organizations from diverse disci-
plines have joined forces to
identify solutions for this public
health crisis. The National
Academy of Medicine’s Action
Collaborative on Countering the
U.S. Opioid Epidemic along
with the AACN and 55 private
and federal participating net-
work organizations have
engaged in partnerships to
develop solutions to the opioid
crisis and to ultimately improve

individual, family, and commu-
nity outcomes for those impact-
ed by the opioid crisis (AACN,
2019b). Through policy, AACN
has focused on workforce
development and further access
to MAT for individuals with
OUD. Goals include increasing
healthcare professional educa-
tion and training; advancing
research and adoption of evi-
dence-based substance use dis-
order (SUD) treatment, provid-
ing safe guidelines for prescrib-
ing opioids for acute and chron-
ic pain, and increasing outreach
to disseminate the urgent need
to address this growing epidem-
ic (AACN, 2019b).

The American Nurses
Association (ANA) has
communicated similar goals as
AACN, including expanded
access to MAT and prescriber
education and training.
Additionally, ANA (2016) has
called for further prevention
research, increased utilization of
prescription drug monitoring
programs, and increased
availability of naloxone
(Narcan®), an opioid antagonist
used to counter the effects of
opioid overdose, for first
responders, family, friends, and
caregivers of individuals with
OUD. All nurses in practice and
academia need to endorse these
initiatives and advocate for
policies to support these
services at the local, regional,
state, and national levels to
improve outcomes for
individuals with OUD.

Education
The need to educate nurses

in practice and academia to treat
individuals with OUD with inter-

ventions based on best evidence
is well documented (ANA, 2016;
American Psychiatric Nurses
Association, 2016; Klimas, 2017;
Livingston et al., 2011; Martello
et al., 2018; Neville & Roan,
2014). The American Society of
Addiction Medicine (ASAM,
2015) has advocated for a
change in perspective in how
society and healthcare providers
view individuals with OUD;
transitioning from the negative
perception of moral weakness
to a treatable chronic neuropsy-
chobiological disease. In
essence, ASAM addresses the
need to decrease stigma and use
best scientific evidence to treat
patients with OUD using the
most humanistic approach to
confront this disease. Through
this changed perspective, educa-
tion focusing on treating SUDs
and OUD as a disease will ulti-
mately result in lessening stig-
ma, more positive healthcare
professional attitudes, and
improved recovery rates (van
Boekel et al., 2013).

In academia, the call to
action is to impart knowledge of
best practice for treatment of
individuals with OUD. This
encompasses knowledge acqui-
sition of OUD risk factors, etiol-
ogy, psychosocial components,
and treatment modalities, using
the framework of OUD as a
chronic condition, characterized
by exacerbation, remission, and
recovery. Nurse faculty need to
embrace this new paradigm
supportive of recovery, and
develop and implement curricu-
la in undergraduate and gradu-
ate nursing programs. An imper-
ative call to action is for nurse
faculty nationwide to compre-

Nursing Economic$

January/February 2020 | Volume 38 Number 1 13

hensively integrate OUD content
into nursing curricula.

Research
To advance science, nurses

should engage in research using
diverse methodologies to
expand the body of knowledge
in OUD. A shortage of literature
exists related to the role of nurs-
es in the care and treatment of
individuals with OUD. Further
inquiry regarding barriers, inter-
ventions to reduce stigma, and
the design and efficacy of com-
prehensive nursing interven-
tions, including the use of MAT,
along with psychosocial support
services, is needed.

Practice
Nurses, as frontline practi-

tioners, are ideally situated to
address the opioid epidemic
through their close interactions
with patients and families in
community and healthcare set-
tings. However, consistent with
public stigma, many healthcare
professionals have negative atti-
tudes toward working with
patients with SUDs, including
perceptions of aggression,
manipulation, and lack of moti-
vation as factors that impede
effective care delivery (van
Boekel et al., 2013).
Furthermore, van Boekel and
colleagues substantiate that
healthcare professionals are
unable to empathize, and report
dissatisfaction when caring for
patients with SUDs, ultimately
resulting in suboptimal care.
Literature supports that many
nurses have condemnatory atti-
tudes and negative beliefs such
as distrust, powerlessness, anger,
futility, and intolerance when

working with patients with
SUDs and OUD (Tierney, 2016).
Neville and Roan (2014) report-
ed nurses perceived patients
with addictions as “manipula-
tive, rude, aggressive and
unsafe” (p. 344). However, these
practicing nurses also identified
being unprepared to care for
this population and a need for
increased education.

In healthcare agencies, there
is a call to action for nurse lead-
ers to address this epidemic.
Based on the identified factors
of stigma and need for further
education, nurse leaders should
advocate for continuing educa-
tion, providing state of the art
best evidence to guide practice
to improve outcomes for indi-
viduals with OUD. Additionally,
the creation and monitoring of a
therapeutic milieu to support
nurses during the transition
toward the adoption of this par-
adigm of recovery is vital.

In addition to the need for
nurses to provide excellent care
to individuals with OUD, nurse
leaders need to be aware of
costs related to OUD treatment.
Recently, Platt and colleagues
(2019) identified the importance
for nurses to gain understanding
of economics in health care to
promote cost-effective delivery
of care. Given the tremendous
economic burden the opioid epi-
demic has created nationwide,
health economics competency is
of paramount importance for
nurses, especially nurse leaders.

Levels of Prevention
Nurses promote and maxi-

mize health states across the
lifespan and health and well-
being continuum, including pri-

mary, secondary, and tertiary
prevention. Nurses in primary
care, community health, and
school settings can be key advo-
cates in protecting the public by
education on the risks of sub-
stance abuse, as well as the
challenges and need for support
of individuals in recovery, the
changing perception of individu-
als with OUD, and the impor-
tance of mitigating public and
health professionals stigma to
facilitate individuals with OUD
to seek and receive adequate
treatment to prevent relapse.

Nurse educators in the com-
munity can serve as highly valu-
able resources in the prevention
of drug misuse, as well as pro-
vide naloxone training and edu-
cation to first responders, teach-
ers, family members, and care-
givers. Additionally, nurses can
provide extensive education to
teachers, counselors, coaches,
and students on prevention and
identification of risk and individ-
uals confronting this disease.

In secondary prevention,
knowledge and recognition of
persons with OUD should not
be confined to mental health
professionals but should include
education for all healthcare pro-
fessionals. Early detection and
intervention must be available to
identify those at risk (biophysi-
cal, psychological, or social
determinants) for all subgroups
within the population across all
ages and in all types of health-
care settings. Increased efforts to
promote safe prescribing prac-
tices are underway, and pre-
scriber and clinician education
on safe opioid use and detec-
tion of potential misuse is essen-
tial (Salmond & Allread, 2019).

Nursing Economic$

January/February 2020 | Volume 38 Number 114

In tertiary prevention, nurses
have a vital role in helping indi-
viduals with OUD maximize
health despite living with a
chronic disease. In the past,
drug dependence was character-
ized by individuals from lower
socioeconomic classes and was
considered a sign of moral
weakness. It is now imperative
that a change in perception by
society and, importantly, by
health professionals must occur.
Stigma, associated with negative
attitudes, can dramatically
impact treatment, recovery, and
ultimately relapse. Recognizing
that addiction is a brain disease,
individuals with OUD must be
treated with respect and com-
passion as a patient with a dis-
ease, without judgment and not
viewed as morally deficient.

Although multiple treatments
are available, evidence reveals
that only an approximate 11%
receive specialty care in treat-
ment centers (Busch et al.,
2017). Based on the escalating
opioid misuse crisis, there is a
dire need to educate practition-
ers on MAT, the safest and most
effective treatment for OUD.
MAT efficacy is increased with a
holistic approach, including
therapeutic counseling and
other behavioral therapies
(Moore, 2018; SAMHSA, 2019b).
According to Moore (2018), sur-
vival rates are improved,
patients remain in treatment
longer, have increased levels of
employment, and have
improved overall quality of life
with MAT. While deemed the
most effective treatment for
OUD, MAT underutilization con-
tinues to exist predominantly
due to the inadequate number

of providers and lack of OUD
education among healthcare
professionals.

To address the need for
increased providers, in 2016 the
U.S. Congress, through the
Comprehensive Addiction and
Recovery Act, facilitated expan-
sion of prescriptive privileges
for nurse practitioners to pro-
vide MAT (SAMHSA, 2019b).
Through education, increased
numbers of nurse practitioners
who can treat individuals with
OUD will dramatically assist in
combatting the opioid epidemic.

Conclusion

The need to address this
unprecedented opioid epidemic
is increasingly being recognized.
Finding ways to treat OUD has
become a national priority
(Mumba et al., 2018). OUD
affects individuals across the
lifespan, including those of
diverse ethnic, racial, and
socioeconomic backgrounds, in
rural, urban, and suburban set-
tings throughout the country. As
frontline providers of care, it is
imperative nurses take compre-
hensive action to combat this
epidemic to improve outcomes
as well as to mitigate the rising
healthcare costs associated with
this crisis. $

Kathleen Neville, PhD, RN, FAAN
Associate Dean of Graduate Studies and

Research
Seton Hall University College of Nursing
Nutley, NJ

Marie Foley, PhD, RN
Dean and Professor
Seton Hall University College of Nursing
Nutley, NJ

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Nurses Call to Action
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