Vol. 48, No. 3, 330–337
Copyright 2008 by The Gerontological Society of America
To What Degree Does Provider Performance
Affect a Quality Indicator? The Case of
Nursing Homes and ADL Change
Charles D. Phillips, PhD, MPH,1Min Chen, MS,2and Michael Sherman2
Purpose: This research investigates what factors
affect the degree to which nursing home performance
explains variance in residents’ change in status of ac-
tivities of daily living (ADL) after admission.
and Methods: The database included all residents
admitted in 2002 to a 10% random sample of nurs-
ing homes in the United States. Longitudinal analyses
of outcomes at 3 months after admission test the
ability of individual characteristics and nursing home
identifiers to explain variance in ADL change for dif-
ferent groups of residents.
the best and worst providers (top 20% vs bottom 20%,
then 10%, then 5%) and we restricted analyses to
more homogeneous groups of residents, we found that
more of the variance in ADL change was attributable
to provider performance. Cognitive function and race
also affected the degree to which home performance
had an impact on outcomes.
results imply that some quality indicators may be
most useful in distinguishing between nursing homes
that provide the best or the worst care. Futhermore, the
degree to which a quality indicator is driven by a
nursing home’s performance may vary considerably,
depending on the characteristics of the consumer.
These findings raise questions about the usefulness of
performance measures that focus on heterogeneous
groups of consumers or entire provider populations.
‘‘How much of the variance in a quality indicator does
provider performance explain?’’ is an issue we think
has not received the attention it deserves in current
discussions of performance-measurement strategies
and pay-for-performance models.
Results: As we compared
Key Words: Long-term care, Nursing homes,
Pay for performance, Performance measurement,
Quality indicators, Quality of care
Quality indicators are becoming more and more
important guides to consumers and payers in all
components of health care (Castle & Lowe, 2005;
Schnelle, 2003). The most useful of these indicators
will contain little measurement error. They will be
affected only by individual characteristics beyond
providers’ control (e.g., age, gender), which will
greatly ease the pain of case-mix or acuity adjust-
ment. In essence, useful indicators should vary al-
(Phillips, Hawes, Leiberman & Koren, 2007). In
other research, investigators focus on such institu-
tions as hospitals as ‘‘the provider,’’ but in this
research the health care provider on which we focus
is the nursing home.
Some of the most commonly used quality
indicators in nursing home research focus on change
in residents’ ability to perform tasks concerning
activities of daily living (ADL; Castle & Lowe, 2005;
Harrington et al., 2003). Such a focus is not sur-
prising, given that, when one is thinking about the
needs of older individuals, functionality often plays
a central role (Fillenbaum, 2006). In essence, it is the
field of gerontology’s approach to summarizing the
‘‘burden’’ of the multiple conditions and ailments
so common among elders. The research using ADLs
is voluminous, and change in ADL function is one
of the quality indicators used in the Centers for
Medicare and Medicaid Services Nursing Home
Compare Web site, which rates the performance of
all Medicare- or Medicaid-certified homes on a
variety of quality indicators (Morris et al., 2002;
Zimmerman et al., 1995).
Unfortunately, some recent research implies that
where individuals receive nursing home care may
have little effect on whether their ADL status in their
first months of care declines or improves (Phillips,
1Department of Health Policy and Management, School of Rural
Public Health, Texas A&M University Health Science Center, College
2Department of Statistics, Texas A&M University, College Station.
Address correspondence to Charles Phillips, PhD, Texas A&M
University Health Science Center, School of Rural Public Health,
Department of Health Policy and Management, 1266 TAMU, College
Station, TX 77843-1266. E-mail. Phillipsed@srph.tamhsc.edu
by guest on October 28, 2015
Chen, Shen, & Sherman, 2007). These results
indicate that facility identity alone (represented as
a set of over 1,300 dummy variables) explains only
8% of the variance in ADL change in residents’ first 3
months in a nursing home. This figure increases to
10% when one looks at ADL change at the end of
residents’ first 6 months in a nursing home. This
figure again increases to 14% when one focuses one’s
analysis on only those residents who declined or
remained stable during their first 3 months (Phillips,
Chen, et al., 2007).
Such results raise the question of how one can
develop a meaningful performance-measurement in-
dicator based on ADL function for nursing homes
when so much of the variation in residents’ func-
tional status is driven by factors other than facility
performance. Although we lack a gold standard for
how much variance the care site should explain in
one’s outcomes, few of us, we think, would feel
sanguine about basing a performance measure on an
indicator in which 85% to 90% of the variance is
beyond the home’s or provider’s control.
Some researchers may, however, be comfortable
with such indicators. For example, a number of
researchers seem relatively comfortable with quality
indicators in which the overwhelming majority of
the variance (80% to 90%) is explained either by
individual characteristics or measurement error
(Degenholtz, Kane, Kane, Bershadshy, & Kling,
2006; Kane et al., 2003; Morris et al., 2002). This
position becomes troublesome to the degree that one
accepts the idea that the best quality indicators are
those heavily affected by provider performance.
However, the idea that care site, home, or
provider makes no important contribution to func-
tional outcomes and that over 90% of the variance in
ADL change is beyond their control is troublesome.
This finding seems contrary to our personal experi-
ence and, we suspect, that of our readers as well.
Many of us have seen homes where residents walk in
alertly, only to be glassy-eyed, slumped to one side,
and pushed about in a wheelchair after a few short
months. Many of us have also seen residents who
were wheeled into homes and walk away unaided
after the same few short months. Most of us know
intuitively and anecdotally that the identity of the
provider from whom a resident receives care must
In this research, we investigate the validity of
nursing home quality indicators or performance
measures across all providers and all consumers.
We seek to determine the circumstances when one
can say that a provider’s or home’s performance does
indeed make a difference in these indicators. To do
this, we evaluate the impact of provider performance
for residents in nursing homes at increasing extremes
in terms of their unique positive or negative effect on
ADL change. Our basic perspective or hypothesis is
as follows: Only when one restricts analyses to homes
at extreme ends of the quality continuum will the
amount of variance in quality attributable to facility
performance be substantial.
We also investigate which individual character-
istics may affect the degree to which provider per-
formance matters. Previous research has shown that
the amount of variance in the direct-care time
explained by site of care is positively correlated
with a consumer’s level of cognitive impairment
(Phillips & Hawes, 1992, 2005). For example, in
supportive housing, provider identity explained
almost 60% of the variance in the frequency with
which a resident received ‘‘cueing,’’ a care strategy
used with cognitively impaired residents, whereas
individual characteristics explained slightly less than
20% of the variance in that measure (Phillips &
Hawes, 2005). The same may be true of variance in
Specifically, the research team investigated the
impact of cognitive impairment, gender, and race on
the importance of provider performance. As Mor
and his colleagues have demonstrated (Mor, Zinn,
Angelelli, Teno, & Miller, 2004), African Americans,
whose occupancy of nursing home beds is increasing
(Sahyoun, Pratt, Lentzner, Dey, & Robinson, 2001),
often reside in poorer quality homes. In addition,
men in nursing homes constitute a definite minority,
and they may cluster in specific types of homes as
well. Whether the homes in which they cluster might
provide poorer or better quality is unclear at this
point. Our basic hypothesis here is as follows: When
analyses are restricted to minority groups or more
homogeneous groups of residents, the amount of
variance explained by facility performance will
The database that we used in this research is
described in somewhat greater detail elsewhere
(Phillips, Chen, et al., 2007). This research uses
Minimum Data Set (MDS) admission data for
calendar year 2002 and quarterly assessment data
for calendar years 2002 and 2003. Quarterly
assessment data in conjunction with admission
data were the source for the dependent variable
(change in ADL function). MDS admission assess-
ment data were the source for the individual-level
covariates in the multivariate models. These data
represent all admissions in a random sample of 10%
of all nursing homes operating in 2002.
We used an admission cohort because it allowed
the research team to develop measures of consumer
baseline status that were free of any provider
influence (Phillips, Hawes, et al. 2007; Phillips,
Chen, et al., 2007). We looked at those individuals
who were admitted to a nursing home and remained
in that nursing home 3 months later and received an
MDS quarterly assessment. We did not exclude
Vol. 48, No. 3, 2008331
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individuals with an explicit terminal prognosis from
our analyses. The MDS item that supposedly
captures that information is of questionable useful-
ness (Finne-Soveri & Tilvis, 1998). This approach
purposely excludes short-stay residents who were
discharged or died prior to the scheduled quarterly
assessment. The focus of this research is on outcomes
of those residents likely to be long-stay residents.
Items on the MDS have been tested for reliability
(Hawes et al., 1995; Morris et al., 1990). Although it
is often assumed that research data are far superior to
administrative or clinical databases, this is notalways
the case. Evidence has shown that MDS ADL data
developed in research studies have reliability similar
to that found in administrative or clinical databases
(Phillips & Morris, 1997). The greatest concern with
these data is that, at times, MDS data do not agree
with observational data gathered during specific time
periods in nursing homes. However, this same re-
search also indicates that MDS data can be used to
differentiate among nursing homes providing either
low- or high-quality care (Bates-Jensen et al., 2004;
Codogan, Schnelle, Yamamoto-Mitani, Cabrere, &
Simmon, 2004; Schnelle et al., 2004).
In addition, it is unclear how much agreement
between observational and clinical data may be
expected. There is always the concern that observa-
tions may be ‘‘reactive’’ and affect the behavior of
those being observed. Some evidence indicates that
care observations in nursing homes may not be
reactive (Schnelle, Osterweil, & Simmons, 2005).
However, there is also evidence that care observa-
tions for regulatory purposes may be reactive in
residential care settings (Reid, Parsons, Green, &
Dependent Variable.—Change in ADL function
is the dependent variable in this research. This
variable is the sum of scores for seven ADL
indicators: bed mobility, transfer, locomotion, dress-
ing, eating, toilet use, and personal hygiene. Each
ADL had five potential response levels (0–4). This
additive ADL scale, which ranges from 0 to 28, had
a Cronbach’s alpha of 0.91. Change in ADL function
was the difference between the ADL scale score for
each resident at admission and at his or her quarterly
assessment. Decline is denoted by positive values,
and improvement is indicated by negative values.
Independent Variables.—We derived the individ-
ual-level covariates included in our analyses from
each resident’s intake MDS assessment. They in-
cluded the ADL scale score just discussed, the MDS
Cognitive Performance Scale (Morris et al., 1994;
Hartmaier et al., 1995), a modified version of the
MDS Changes in Health, End-Stage Disease, and
Signs and Symptoms Scale (Hirdes, Fritjers, &
Table 1. Descriptive Statistics for Variables (n = 36,584)
Change in ADL scale
ADL change at first quarterly
Cognitive Performance Scale
Depression Rating Scale
Mortality Risk Index
American Indian–Alaskan Native
Black, not Hispanic
White, not Hispanic
Living arrangement before admission
In a facility or home
Site admitted from
Private residence, no home health
Private resident, with home health
Assisted living or group setting
Acute care hospital
Residential history in past 5 yearsb
Stay in this nursing home
Stay in other nursing home
Stay in other residential setting
None of the above
Notes: ADL = activity of daily living; MDS = Minimum
Data Set; CHESS = Changes in Health, End-State Disease,
and Signs and Symptoms; MR/DD = mental retardation or
developmental disabilities; SD = standard deviation.
aReferences for these scales appear in the section of the
article where measurement strategies are discussed.
bMultiple response item in which a single resident can
appear in more than one category.
332 The Gerontologist
by guest on October 28, 2015
Teare, 2003), the MDS Mortality Risk Index score
(Flacker & Kiely, 2003), and the resident’s age,
gender, and living arrangement prior to entering the
We measured provider performance by using
a series of dummy variables representing nursing
home identity. After we controlled for individual
characteristics, these variables capture differences in
resident outcomes related to the home in which each
resident resided. It is important to note that this
strategy does not tell one what aspect of a home’s
characteristics or behavior resulted in better or
worse performance. Instead, our approach simply
identifies homes in which the residents fared better
or worse in their ADL function.
We carried out all analyses at the individual level.
For our different groups of residents, we estimated
three models by means of ordinary least squares
(OLS) methodology. We used individual character-
istics at admission alone in the first model. A series
of dummy variables representing home identity
comprised the second model. We included both
individual characteristics and the variables represent-
ing home identity in the third model (the combined
model). In the combined model, the home variables
represented the provider’s impact on the outcome
(performance), over and above the impact of in-
dividual characteristics. We compared the strength
of these models on the basis of the explanatory
power of each model (R2). In all analyses, the con-
tribution of facility performance to explaining vari-
ance over and above individual characteristics is the
difference between the R2for the individual model
and the combined model. We did not adjust the
results for possible intrahome correlation or de-
pendence because our focus is on the overall power
of different models, not individual parameters within
We estimated these models by using all homes and
homes that our results indicated were in the top
20%, 10%, or 5% of homes versus the bottom 20%,
10%, or 5% of homes. We identified these homes at
differing ends of the quality distribution by evaluat-
ing the relative size and direction of their individual
parameters in our combined model. Facility param-
eters in the combined model indicated how well the
home performed on the quality indicator after we
adjusted for all the individual resident characteristics
in the model.
In other analyses, we used only those homes in the
top or bottom 5%, and we examined the degree to
which resident characteristics affected the impor-
tance of homes’ performance. As the analyses move
to more and more heterogeneous resident popula-
tions (e.g., from all residents to only those in bottom
and top 5% of homes), the R2of the models will
Figure 1. Variance explained by models when estimated for residents in all homes and homes with poorer or better performance.
Vol. 48, No. 3, 2008333
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automatically increase. That progression is inherent
in the way the bottom and top homes were chosen.
When we compare homes at the ‘‘extremes,’’ we
purposely choose groups of homes in which the
variance in outcomes has a larger between-home
variance component than the between-home vari-
ance component for outcomes among the entire
population of homes. What is not inherent in this
process is the levels reached by the R2value. To
investigate this issue, we performed a simulation
with randomly generated data that mimicked the
structure of our database. The value for the model
using the top and bottom 5% of homes in this
simulation was R2= .12.
Table 1 presents descriptive statistics for the data
used in our analyses. In our sample, over 36,000 resi-
dents remained in the home for their first quarterly
assessment. Although a majority of residents re-
mained stable or declined, almost 43% showed
improvement on the scale. The average change was
a decrease of 1.4 points, a minor improvement, on
a scale that averaged 15 at admission. The average
resident was somewhere between mildly and mod-
erately cognitively impaired (Cognitive Performance
Scale score = 2.4). Over 70% of these admissions
were for individuals 75 years of age or older, and two
thirds were women. Four out of five of these
residents were White, non-Hispanic, whereas just
over 1 in 10 were African American. Only one fourth
or 25% of these people lived alone prior to entering
the nursing home, and 58% entered the nursing
home after a stay in an acute care hospital.
As we noted earlier, we used the variables in
Table 1, along with the identity of the home in which
the participants received care, to estimate OLS
equations. The R2statistics for these models are
displayed and compared in Figures 1 through 4.
From this point forward, we use the term home or
facility performance. By this we mean the effect of
facility identity on ADL change over and above the
effect of the residents’ individual characteristics. As
Figure 1 indicates, the importance or variance
explained by individual characteristics increases little
as one analyzes data from homes farther and farther
apart on the scalar made up of the parameters for the
home-identity dummies. The increase in the amount
of variance explained by the homes’ performance
increases by over 100%. For all residents, home
performance alone explains less than 10% of the
variance in ADL change, whereas for residents in the
top and bottom 5% of homes, facility performance
explains almost 25% of the variance in ADL change.
In part, these results are no surprise. The
mathematics of OLS makes this R2progression
inevitable. However, the specific values of this
progression are not inevitable. For all residents, the
Figure 2. Variance explained by models when estimated for residents with differing levels of cognitive performance.
by guest on October 28, 2015
finding that home performance explained only 9% of
the variance in ADL change was not inevitable. The
floor for that progression could have been 25%, but
it was not. The values at the higher end of the
progression are also instructive. When looking only
at residents in the top or bottom 10% of homes, we
found that provider performance explained 20% of
the variance in ADL change. When one goes even
further and uses only the top and bottom 5% of
homes, then home performance explained almost
25% of the variance.
Although the results just presented dealt with the
impact of home performance on ADL change for the
average resident, we also expected home perfor-
mance to affect the outcomes of different types of
residents differently. In Figure 2, we present results
of our decomposition of the variance in ADL change
for residents with differing levels of cognitive
impairment. We ran our three basic models sepa-
rately for residents’ scoring at each of the seven levels
of the MDS Cognitive Performance Scale.
For the most part, provider performance was
more important within these more homogeneous
groups than it seemed for the entire sample of
residents. For those individuals who were cognitively
intact, facility performance explained less than 10%
of the variance in ADL change. These results also
indicated that, for individuals who are relatively
cognitively intact or somewhat mildly impaired,
facility identity became more important as its
proportion of the explained variance increased. Fi-
nally, when one reviews the results for those persons
who suffer from moderately severe, severe, or very
severe impairment, facility performance explained
36% to 51% of the variance in ADL change. In fact,
for those with the severest cognitive impairment,
almost the entirety of the variance explained by the
model containing both individual and facility
indicators was attributable to provider performance.
Figure 3 replicates the information presented in
Figure 2, but Figure 3 includes only those residents in
the 5% worst or 5% best performing providers. The
results were quite similar in form to our earlier
results, but the levels of explained variance were
much higher. Among the poorest and best perform-
ing homes, for the most cognitively impaired
residents, the full model explained over 80% of the
variance in outcomes. For these same residents,
facility performance explained, in isolation, almost
70% of the variance in change in ADLs.
Figure 4 provides similar information on residents
divided into more homogeneous groups based on
gender and on race. In each of those instances, the
models as a whole operated better (had a higher
R2value) when estimated with more homogeneous
groups. In addition, the power of the models differed
Figure 3. Variance explained by models when estimated for residents with differing levels of cognitive performance in the homes
considered among the 5% best and 5% worst homes.
Vol. 48, No. 3, 2008335
by guest on October 28, 2015
among the groups. Provider performance seemed, in
this sample, to be most important for African
Americans, explaining over 40% of the variance in
outcomes for this minority group, in contrast to
explaining approximately 25% of the variance in
outcomes for Whites. The differences between the
model results for men and women were not as large
as the differences between racial groups.
This research indicates that nursing home perfor-
mance explains relatively little of the variance in our
dependent variable, change in ADL function, for the
entire population of nursing home residents. Instead,
the variance attributable to performance differences
among homes only reaches substantial levels when
one compares differences in resident outcomes for
those individuals in what seem to be the poorest and
best performing homes. In addition, this research
indicates that the variance attributable to perfor-
mance differences among homes is not consistent
across outcomes for different types of residents.
Our research has a number of limitations. Our
analyses deal with only one measure of change in
ADL function. These results may not generalize to
other nursing home quality indicators, including the
specific indicators used on the Center for Medicare
and Medicaid Services Nursing Home Compare Web
site. In addition, we used data from only the first
3 months of the residence in a nursing home. Any
cumulative effect of facility performance over
a longer time period than 3 months was not captured
in our analyses. In addition, our approach gives us
no insight into what aspects of a home’s operations
or characteristics may have led to its position as
a poor or excellent performer. Finally, our model
may be poorly specified and some of the error
variance may be attributable to unobserved factors
that might affect our results.
Nonetheless, as we noted in our introduction,
a good nursing home quality indicator largely varies
only with differences in the quality of care provided
by homes. However, the degree to which quality
indicators are driven by variance in home perfor-
mance or identity has not been a major research issue
or a major concern. Only a few researchers in long-
term care have given this issue the attention it
deserves. One potential result of this attitude is that
we may unknowingly have developed indicators for
nursing homes in which the provider performance
plays little role.
Our results concerning the importance of home
performance imply that when one looks at homes
that score the poorest or the best on our chosen
outcome, then one sees a relatively strong effect of
home performance. Beyond that, home performance
has a rather minimal effect. This makes a provider’s
Figure 4. Variance explained by models when estimated for different types of residents in the homes considered among the 5% best
and 5% worst homes.
by guest on October 28, 2015
score on this indicator relatively meaningless to those Download full-text
trying to identify anything other than the best and
worst homes, in terms of maintaining ADL function.
As we noted earlier, the results in Figures 2 and 4,
indicating that the importance of facility perfor-
mance varied by the resident’s degree of cognitive
impairment, were not wholly unexpected. Previous
research on residential long-term care indicates that
as individuals become more cognitively impaired,
they became more dependent for the content of their
care on provider policies (Phillips & Hawes, 1992,
2005). When a resident is too cognitively impaired to
clearly express her or his needs, then general insti-
tutional procedures and practices, rather than indi-
vidual needs, may design and dominate the care
The implications of the findings concerning the
impact of race and gender are less clear. The only
clear conclusion is that facility performance is more
important for the care of African Americans than
it is for the care of other residents. This seems
consistent with the finding by Mor and his colleagues
(2004) that minority residents are most often found
in ‘‘second-tier’’ homes with poorer staffing and
outcomes. The more general implication of these
results may be that if we allow the segregation of
specific groups of residents into different types of
homes, then the identity of the home and its
performance may become more important determi-
nants of these residents’ outcomes.
Though this research focused only on ADL
change, this research may indicate that researchers
need to move toward analyses of outcomes for more
homogeneous populations or look at only the best or
worst providers, or both. In these instances, provider
performance may explain enough variance in an
outcome for one to consider using the indicator to
help consumers choose among providers or for
choosing which providers to reward in pay-for-
performance reimbursement model.
As we noted herein, this research has significant
limitations. Nevertheless,it may serveas a cautionary
tale for those committed to the development of a
rational system for rewarding provider performance
and offering consumers meaningful information
about providers. Depending on the results of future
research, how much variance in a quality measure is
attributable to provider performance, and how that
level of determination varies across different types of
consumers, may be matters of primary importance to
those interested in the development of performance-
measurement systems for nursing homes.
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Received June 25, 2007
Accepted January 15, 2008
Decision Editor: William J. McAuley PhD
Vol. 48, No. 3, 2008 337
by guest on October 28, 2015