Module 3 Discussion Reimbursement & Financing Issues
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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.
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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
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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
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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-
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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 ly s ig ni fic an t a t t he 1 % le ve l.
Fi xe d ef fe ct s ar e at th e ho sp ita l l ev el .
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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