Using Standardized Encounters to
Understand Reported Racial/Ethnic
Disparities in Patient Experiences
Robin M. Weinick, Marc N. Elliott, Angelo E. Volandes,
Lenny Lopez, Q Burkhart, and Mark Schlesinger
Objective. To assess the extent to which racial/ethnic differences in ratings of patient
experiences with health care represent true differences versus differences in expecta-
tions,how scalesareused,orhow identicalphysician–patientinteractionsareperceived
by members of different groups.
Study Setting. Primary data collection from a nationally representative online panel
(n5567), including white, African American, and Latino respondents.
Study Design. We administered questions on expectations of care, a series of written
vignettes, a video-depicted doctor–patient interaction, and modified CAHPS Clinician
and Group Doctor Communication items.
Principal Findings. Different groups reported generally similar expectations regard-
ing physicians’ behaviors and provided similar mean responses to CAHPS commu-
nication items in response to standardized encounters.
Conclusions. Preliminary evidence suggests that unlike more subjective global
ratings, reported disparities in more specific and objective CAHPS composites may
primarily reflect differences in experiences, rather than differences in expectations and
scale use, adding to our confidence in using the latter to assess disparities.
Key Words. Racial/ethnic disparities, CAHPS, patient experiences with care,
A growing body of research demonstrates that patients from different racial
and ethnic groups report differing experiences with the health care system
when using well-validated measurement tools such as CAHPS. However,
there are seeming paradoxes within these observed differences. For example,
African American patients provide higher global ratings of their care and
personal clinicians than white patients, despite more objective reports that
rHealth Research and Educational Trust
Health Services Research
their experiences are problematic, including having worse communication
and less responsive providers (Morales et al. 2001; Lurie et al. 2003; Uhrig
et al. 2004; Dayton et al. 2006). Similarly, Latinos are more likely than whites
to report problems getting needed care and with respect, but they simulta-
neously provide higher global ratings of their doctors (Weech-Maldonado
et al. 2003, 2004, 2008).
Understanding these seemingly paradoxical results could help in
describing the role that race and ethnicity play in mediating clinician–patient
differences in scale use for global ratings, such as extreme response tendency
(ERT) (Elliott et al. 2009b), that do not apply to more specific, objective
‘‘report’’ items. This lack of comparability may make global ratings less suit-
able for assessing and reporting racial/ethnic disparities, despite the appeal of
a single measure that is easily interpreted by the public. This issue is of more
than academic interest, as the results of patient experiences with care surveys
are increasingly disseminated to prospective patients. For example, the 2008
expansion of Hospital Compare makes such data from the CAHPS Hospital
Survey publicly available for individual hospitals (http://www.hospitalcom-
pare.hhs.gov), and the Medicare Improvements for Patients and Providers
Action of 2008 mandates public reporting of patient experiences with
Medicare plans by race/ethnicity.
Considering race/ethnicity exacerbates challenges to assessing complex
experiences such as clinical encounters, because the construct embodies a
lifelong set of experiences that can fundamentally transform how medical
episodes are perceived, evaluated, or described. As a result, one potential
scales despite also reporting more frequent problems is that they have
satisfied (Weech-Maldonado et al. 2008), in keeping with evidence that
expectations affect patients’ evaluations of care (Jackson, Chamberlin, and
Kroenke 2001; Noble et al. 2006). Alternatively, various racial/ethnic groups
Address correspondence to Robin M. Weinick, Ph.D., The RAND Corporation, 1200 S. Hayes
Street, Arlington VA 22202; e-mail: firstname.lastname@example.org. Marc N. Elliott, Ph.D., and Q Burkhart,
M.S., are with the The RAND Corporation, Santa Monica, CA. Angelo E. Volandes, M.D.,
M.P.H., is with the Section of General Medicine, Massachusetts General Hospital, Boston, MA.
Lenny Lopez, M.D., M.P.H., is with the Mongan Institute for Health Policy, Boston, MA. Mark
Schlesinger, Ph.D., is with the School of Public Health, Yale University, New Haven, CT.
492 HSR: Health Services Research 46:2 (April 2011)
with care. Previous studies have found that Latino and African American
(Gallagher, Fowler, and Cleary 2004; Weech-Maldonado et al. 2008; Elliott
et al. 2009b). Finally, members of different racial/ethnic groups may have
systematically varying interpretations of identical interactions because they
value particular aspects of these interactions in different ways.
Robust, comparable measurement of patient experiences is essential to
designing effective interventions to reduce well-documented racial and ethnic
disparities in health care; little progress can be made if the roles of scale use,
expectations, preferences, and experiences are not clearly understood. This
study was designed to assess the extent to which African American, Latino,
and white respondents provide similar responses to items from the CAHPS
Clinician and Group Survey in response to standardized clinical scenarios in
order to clarify the interpretation of racial/ethnic differences in patient-re-
ported real-world health care experiences. We rely in part on methodology
originally developed by King et al. (2004) to assess the extent of cross-cultural
incomparability in survey responses by presenting an ordered series of short
vignettes and examining the extent to which different groups of individuals
vary in the responses they offer to questions about the vignettes.
This study was conducted using the Knowledge Networks panel, an ongoing
Internet panel based on a random digit dialing sample of the full U.S. adult
population, which is designed to be nationally representative and has a
54.9 percent participation rate (Knowledge Networks No Date a). The panel
provides free Web TV access for those who do not have a home Internet
connection, representing lower Income adults who would otherwise be dis-
proportionately excluded. A variety of health-related studies have used this
panel and support its validity (Schlenger et al. 2002; Silver et al. 2002; Baker et
al. 2003; Wagner et al. 2004). Each individual is contacted by e-mail regarding
generate a response, the individual receives a reminder phone call.
panel, stratified by race/ethnicity to obtain similar numbers of respondents
from each of three racial/ethnic groups, and resulted in 567 responses (44.5
Standardized Encounters to Understand Racial/Ethnic Disparities493
included 204 white (completion rate 52.3 percent), 163 black (completion
rate 36.9 percent), and 200 Latino (completion rate 45.0 percent) adults.
Asians were excluded from the sample, because the panel included too few
Asian members to allow for precise estimation. The study was fielded in
October and November 2008.
Respondents answered questions regarding expectations, assessments of writ-
ten vignettes, and assessments of a videotaped simulated physician–patient
encounter. The written vignettes allowed us to efficiently expose respondents
to multiple scenarios depicting a gradation of physician responsiveness to
patients, which provides insight into differential use of response scales. In
contrast, the video provides greater realism, and previous research has shown
that people respond differently to video descriptions of health care concerns
the video supported detailed measurement and comparison of perceptions of
positive and negative aspects of physician behavior.
In the first part of the study, we asked respondents to answer a series of
questions that have previously been used to assess expectations about phy-
as a shorthand for a complex construct that is based on beliefs and past ex-
periences. These expectations have likely evolved over time for each respon-
questions asked: ‘‘Roughly how many doctors do you think:
? Take the time and effort to learn about the most up-to-date treat-
ments and drugs?
? Don’t take enough time to talk with patients about their medical
? Will speak up for their patients in disputes with health insurance
? Make too many mistakes in taking care of their patients?
? Treat all patients fairly regardless of race?
The response options were no doctors at all, some doctors, most doctors, and
Next, each respondent reads a series of five vignettes describing inter-
494 HSR: Health Services Research 46:2 (April 2011)
description of a patient complaining of headaches. The vignettes differed in
physician responsiveness to the patient’s concerns, and they were reviewed
by multiple team members to ensure that they depicted differential levels
of physician responsiveness to the patient’s concerns and avoided any
appearance of the physician reacting to an escalating clinical situation. This
approach follows King et al.’s (2004) technique of exposing respondents to
characteristic (here, physician responsiveness) and testing the extent to
which respondents differentiate among those vignettes on a fixed response
scale (here, modified items from the CAHPS Clinician and Group survey).
The five vignettes appear in Appendix SA2. Although the vignettes were
presented to respondents in randomized order, we refer to them here as
Vignettes 1 (least responsive) through 5 (most responsive). The vignettes
thus constitute an ordinally scaled measure of physician responsiveness.
Vignettes were constructed so their length was independent of the degree of
After reading each vignette, respondents were asked modified versions
of three of the six items within the Doctor Communication composite of the
CAHPS Clinician and Group Survey, modified for this setting:
? To what extent did this doctor listen carefully to [the patient]?
? To what extent did this doctor spend enough time talking to
[the patient] about his headaches?
The CAHPS questions were originally designed to ask how frequently
been multiple real-life encounters, with response options of never, sometimes,
usually, or always. Because this format is not consistent with a single encounter
described in a vignette, we modified the stem and response scale to ask about
theextentto which thedoctorexhibitedeachofthese behaviors (notat all,very
little, to some extent, or to a great extent). To minimize respondent burden, the
omitted from this study.
Finally, we prepared a 4-minute video simulating a single physician–
patient encounter, in which a diabetic patient’s lack of success at controlling
have a longstanding, comfortable relationship. The physician indicates how
Standardized Encounters to Understand Racial/Ethnic Disparities495
busy he is and responds to the patient’s lack of progress with significant
frustration, though he also tries to encourage her and discusses alternative
improvement strategies. A transcript of the video is included in Appendix
SA3. The script and final video were reviewed for accuracy and tone by
two physicians (A. E. V. and L. L.) and the principal investigator (R. M. W.),
with the overall tone intended to roughly balance positive and negative
Following the video viewing, each respondent was asked to provide a
3. The video was followed by more extensive follow-up than the written
vignettes for two reasons. First, the written vignettes involved very short
descriptions, making it unlikely that respondents had adequate information on
which to basea response to the global ratingquestion or to a moreextensive set
of follow-up questions. Second, to avoid undue respondent burden, we did not
ask additional questions multiple times for each of the five vignettes.
Finally, each respondent in the full sample was asked a series of ques-
tions to elucidate the rationale for his or her response to the video. These
questions applied an approach drawn from Motivational Interviewing (Miller
why they provided the responses they did to the CAHPS global rating ques-
tion, rather than higher or lower responses. This technique helps elucidate
individuals’ motivations, and here it helps to clarify respondents’ perceptions
their rating. These questions included a set of 10 positive and 10 negative
characteristics, with responses on a four-level ordinal scale, assessing the
extenttowhich eachcharacteristicdescribedthephysician(notatall,very little,
to some extent, or to a great extent). The positive characteristics included the
extent to which the doctor was knowledgeable, motivated the patient, had a
good relationship with the patient, was reassuring, was trusted by the patient,
was kind, was respectful, was understanding, liked the patient, and was
helpful; the negative characteristics were the extent to which the doctor was
rushed, was arrogant, was impatient, was pushy, ignored the patient’s
questions, was disrespectful, was intimidating, was disapproving, interrupted
the patient, and disliked the patient. More detail on the development of these
questions is shown in Appendix SA4.
This study was reviewed by the Partners HealthCare Human Research
Committee (Boston, MA).
496HSR: Health Services Research 46:2 (April 2011)
Our analyses included both bivariate and multivariate statistical models.
For each of the five measures of expectations, means were computed by
race/ethnicity, and African Americans and Latinos were compared with
whites via independent sample t-tests.
For each of the three modified CAHPS communication items and
each of the five vignettes, mean responses were calculated by race/ethnicity.
A series of three multivariate linear regressions predicted responses to
each CAHPS item from indicators of physician responsiveness, case-mix
adjustors (age, gender, education), indicators of African American and Latino
race/ethnicity, and the interaction between physician responsiveness and
race/ethnicity. Additional models parameterized physician responsive-
ness linearly, rather than as categories, for greater power to detect dispari-
ties. These multivariate models adjusted for the correlation of responses to
multiple vignettes for each respondent using the Huber–White sandwich
estimator of variance.
For each of the five modified CAHPS items assessed for the video, as
well as for the indices of perceived positive and negative physician behavior,
by race/ethnicity. The indices of perceived positive and negative behavior
were constructed via an exploratory factor analysis of responses to the 20
attributes described above from the full online sample (n5567). This analysis
identified two factors (regardless of whether Pearson correlations or
polychoric correlations were used), resulting in one positive and one nega-
items. As described above, these two indices help to elucidate respondents’
rationale for their global rating of the physician in the video.
Similar analyses were performed for the 0–10 global rating of the phy-
sician in the video. In addition, ERT by race/ethnicity was assessed in two
ways. First, we compared the standard deviation of the responses by race/
ethnicity using the Levene test. Second, multinomial logistic regression was
used to test the four most extreme responses (0–1 pooled; 9–10 pooled) rel-
ative to themiddle seven responses(2–8 pooled), similar to the approach used
by Weech-Maldonado et al. (2008) and Elliott et al. (2009b). Finally, a multi-
variate model predicted the 0–10 global rating of the physician in the video
from indicators of African American and Latino race/ethnicity, case-mix ad-
justors, indices of perceived positive and negative physician behavior, and
the interaction of race/ethnicity with perceived physician behavior. The
Standardized Encounters to Understand Racial/Ethnic Disparities497
interaction helped assess the extent to which perceptions of physician behav-
ior differentially affect global ratings across racial/ethnic groups.
Station, TX, USA) and SAS 9.2 (SAS Institute Inc., Cary, NC, USA). Unless
otherwise noted, po.05 using two-sided tests for any differences discussed in
Table 1 displays the demographic characteristics of our sample, overall and by
considerably from that of the U.S. population. Our sample is slightly older than
the U.S. population as a whole and the full Knowledge Networks panel, and
more likely to be in the middle income group than the overall U.S. population
(DeNavas-Walt, Proctor, and Smith 2008; U.S. Census Bureau 2009a; Knowl-
edge Networks No Date b). At the same time, the distribution of our sample by
gender, education, and area of residence looks similar to both the broader U.S.
population and the full Knowledge Networks panel (U.S. Census Bureau
2009a,b; U.S. Census Bureau No Date a; U.S. Census Bureau No Date b).
Racial/ethnic differences in these characteristics are similar to those in the gen-
ethnicity. Average responses tend to fall near the middle of the scale (‘‘some’’
to ‘‘most’’ doctors), regardless of respondent race/ethnicity. Mean responses
range from an overall average of 2.21–2.78 for positive behaviors, and 2.06–
2.40 for negative behaviors, where 1 corresponds to ‘‘no doctors at all’’ and 4
to ‘‘all doctors.’’ Notably, the only expectation for which there were statis-
tically significant differences by race/ethnicity relates to whether physicians
treat all patients fairly regardless of their race, with both African American
(mean 2.53) and Latino (mean 2.78) respondents believing that fewer doctors
do so than white respondents (mean 2.98), equivalent to 45 percent of African
Americans and 20 percent of Latinos shifting responses from one category to
statistically significant racial/ethnic differences (p4.05 in all cases).
Table 3 summarizes CAHPS responses to the vignettes (panel A) and the
panel A shows the case-mix-adjusted mean response to each of the five written
vignettes by race/ethnicity. Responses are increasingly positive with increasing
498 HSR: Health Services Research 46:2 (April 2011)
depicted physician responsiveness to the patient. These findings are replicated
in additional linear regressions (not shown), which found significant positive
coefficients for physician responsiveness (b50.56, 0.63, and 0.60 points per
level of linearly coded responsiveness for listen, respect, and time, respectively;
po.001 for each),confirmingthatthewritten vignetteseffectively conveyed the
intended systematically increasing degree of physician responsiveness.
Table1: Demographic Characteristics of Respondents by Race/Ethnicity
All (n5567) White (n5204) African American (n5163) Latino (n5200)
Less than high school
High school graduate
Bachelors degree or
Lives in Metropolitan Statistical Area
Region of country
nnnpo.001 for test of characteristic mean in the designated racial/ethnic group differing from the
mean among whites.
Standardized Encounters to Understand Racial/Ethnic Disparities 499
At the same time, however, responses are quite similar by race/ethnicity
within a given vignette (p4.05 in all instances). Case-mix-adjusted repeated-
detect racial/ethnic differences confirmed this (not shown). In these same mod-
els, there was no significant association of African American or Latino race/
material. Given the absence of evident disparities here, additional analytic tech-
niques applicable to these vignettes (King et al. 2004) were not pursued.
Panel B of Table 3 shows mean responses to the CAHPS questions that
were asked of each respondent following the video viewing. Notably, the
mean 0–10 rating of the doctor was below 5 for all racial/ethnic groups,
suggesting that the physician in this third-person encounter was perceived
more negatively than is typical of perceptions of one’s own physician in the
real world, given that means near 9 are more typical for such ratings of one’s
own physician (e.g., Elliott et al. 2009b). Coefficients in case-mix-adjusted
regressions showed no evidence of racial/ethnic differences in responses to
any of the five modified Doctor Communication items or in responses to the
global rating. A repeated-measures multiple regression (similar to the model
Table2: Expectations Regarding Physician Behavior by Race/Ethnicity
Roughly how many doctors do you think:
MeanSE MeanSE Mean SEMean SE
Take the time and effort to learn about the
most up-to-date treatments and drugs?
Will speak up for their patients in disputes
with health insurance plans?
Treat all patients fairly regardless of race?
Don’t take enough time to talk with
patients about their medical care?
Make too many mistakes in taking care
of their patients?
2.560.032.63 0.04 2.52 0.05 2.520.05
2.21 0.032.220.05 2.25 0.05 2.180.05
2.78 0.03 2.980.04 2.53n0.05 2.78n0.05
2.400.03 2.44 0.04 2.330.05 2.420.05
2.060.02 2.090.03 2.03 0.03 2.050.04
Response options are 15no doctors at all; 25some doctors; 35most doctors; 45all doctors.
npo.05 versus white.
500HSR: Health Services Research 46:2 (April 2011)
and Video Interaction by Race/Ethnicity (Adjusted for Age, Education,
Case-Mix-Adjusted Mean CAHPS Responses to Written Vignettes
(A) Written Vignettes
All White African AmericanLatino
MeanSE MeanSEMean SEMean SE
. . . listen carefully to the patient?w
. . . show respect for what the patient had to say?w
. . . spend enough time talking to the patient about his headaches?w
4 2.98 0.04
(B) Video Interaction
CAHPS Communication Composite Itemsw
To what extent did this doctor:
Show respect for what the patient had to say?
Spend enough time with the patient?
Explain things in a way that was easy to understand?
Seem to know the important information about the
patient’s medical history?
CAHPS Global Rating
Using any number from 0 to 10, where 0 is the worst
doctor possible and 10 is the best doctor possible, what
number would you use to rate this doctor?
4.510.10 4.370.17 4.620.194.560.17
nMean responses did not differ by race/ethnicity (p4.05 for all comparisons of African American
and Latino to White).
wResponses are 15not at all; 25very little; 35to some extent; 45to a great extent.
z1 = lowest physician responsiveness; 5 = highest physician responsiveness.
Standardized Encounters to Understand Racial/Ethnic Disparities501
used for responses to the vignettes), which attempted to maximize power to
detect racial/ethnic differences by pooling across outcomes, also failed to find
significant evidence of differences (p4.05 in all instances).
In addition, while white, African American, and Latino respondents
p4.05) to the doctor in the video, the standard deviations for African Amer-
icans (2.63) and Latinos (2.59) were significantly greater than for whites (2.19,
po.05 in each case, by the Levene test). Similarly, African American and
Latino respondents were more likely to use responses at both ends of the scale
than white respondents. In particular, African Americans and Latinos were
more likely than whites to use both the bottom two response options (14 and
versus 3 percent); ORs51.90–2.81, po.05 for all four contrasts from mul-
tinomial logistic regression. This reflects greater use of the extremes of the
scales, or greater ERT, among African American and Latino respondents.
This has also been observed in real-world CAHPS data, particularly for La-
in ERT for the more specific report items (data not shown).
With respect to the index of perceived positive physician behaviors in
the video, mean responses fell between 2 (very little) and 3 (to some extent).
Mean perceptions of positive behaviors did not differ significantly by race/
ethnicity (2.62 for African American, 2.56 for Latino, and 2.52 for white
respondents; p4.2 for each comparison versus white, not shown). Mean re-
sponses for perceptions of negative physician behaviors occurred somewhat
more often, nearly corresponding to a value of 3 (to some extent). The mean
frequency of negative behaviors perceived by African Americans (2.73) was
significantly lower than that for whites (2.93; p5.01); Latinos (2.83) did not
significantly differ from whites (p4.2). These patterns are also consistent with
respondents generally perceiving the video interaction as having at least as
many negative as positive behaviors.
Finally, Table 4 includes the results of regressing the 0–10 CAHPS
global rating of the physician on race/ethnicity, the positive and negative
perception scales, and interactions between these. Because of the presence of
interaction terms with race/ethnicity, the ‘‘positive behavior’’ and ‘‘negative
non-Hispanic whites. As expected, perceptions of positive behavior were
positively associated with the global ratings and perceptions of negative be-
haviors were negatively associated with this rating (po.0001 for each). The
magnitude of this coefficient was twice as large for positive perceptions as for
502HSR: Health Services Research 46:2 (April 2011)
negative perceptions, perhaps suggesting that the absence of positive percep-
tions may more strongly drive poor overall assessments of physicians than the
presence of negative perceptions. Both the main effects of race/ethnicity and
of physician behavior having a similar influence on 0–10 ratings of physicians
across racial/ethnic groups. The nonsignificant interactions also suggest that
the larger role of positive than negative perceptions is consistent across racial/
We describe an experimental approach using standardized written and video
vignette medical encounters to learn how differences in expectations, per-
ceptions, and scale use may explain observed racial/ethnic differences in re-
ported patient experiences in the real world. As has been demonstrated in
other substantive areas (King et al. 2004), a vignette-based approach proved
helpful for studying the extent to which racial/ethnic groups use response
scales differently for assessing similar experiences; this approach may be
Quality from Race/Ethnicity and Positive and Negative Perceptions of
Physician Behavior in the Video Interactionn
Multivariate Regression Predicting Global Rating of Physician
Mean (1–4) perceived behaviorsz
African American ? positive
African American ? negative
Latino ? positive
Latino ? negative
nCase-mix-adjusted for age, education, and gender; coefficients not shown.
wWhite is the reference category.
z15not at all, 25very little, 35to some extent, 45to a great extent.
Standardized Encounters to Understand Racial/Ethnic Disparities503
useful in further research that examines underlying causes of racial/ethnic
disparities in care.
Our results suggest that different racial/ethnic groups have generally
extent to which they treat all patients fairly regardless of race. This indicates
that the differences previously observed in real-world CAHPS data are not
likely to be caused by some racial/ethnic groups having lower expectations of
physicians, and therefore being more easily satisfied.
Using written vignettes that depicted a range of physician responsive-
ness to patient concerns, we find no evidence that CAHPS Clinician and
Group report items are used differently by African Americans, Latinos, and
whites in response to the same encounter stimuli. To increase the verisimil-
itude of the depicted encounter, we subsequently employed a single video
portraying poorer-than-average physician behavior. In response to this video,
all racial/ethnic groups provided similar responses on CAHPS Clinician and
Group report items. These groups also had similar mean responses on 0–10
global ratings of the physician, and these ratings were similarly responsive to
perceptions of positive and negative physician behavior. At the same time,
however, African American respondents perceived fewer negative behaviors
on the part of the physician than white and Latino respondents. This suggests
behaviors in varying ways or may differentially value some behaviors and
may warrant further exploration.
In the present study, Latinos and African Americans used both the
positive and negative extremes of the response scale more often than whites
for a standardized encounter. This extends previous findings of greater ERT
by Latinos relative to whites in real-world encounters (Weech-Maldonado
et al. 2008; Elliott et al. 2009b). Here, this tendency did not result in higher
mean 0–10 ratings for Latinos and African Americans than for whites because
the portrayal of poorer-than-typical physician behavior resulted in an atypical
symmetric distribution of ratings (with ERT boosting both ends), rather than
the more typical asymmetric distribution of ratings around a mean of 8 or 9
(with ERT boosting the positive end more than the negative and shifting the
mean upward). Perhaps as a result, we find similar mean 0–10 ratings across
racial/ethnic groups in response to this particular simulated encounter, unlike
Elliott et al. (2009b) and Weech-Maldonado et al. (2008). These ERT differ-
Americans if more positive physician behavior had been portrayed in the
video encounter. Taken together, these findings reinforce the interpretation
504 HSR: Health Services Research 46:2 (April 2011)
that previously observed differences in 0–10 global ratings are most likely
an artifact of scale use rather than an indication that Latinos and others with
high ERT have more variable health care experiences. While it remains
possible that minority patients are more likely than whites to have both the
This study has several limitations. First, despite efforts to achieve a rep-
resentative sample and a demographic profile of respondents that reflects
national distributions, respondents who agree to participate in an ongoing
online panel may differ in unmeasured ways from the general public; our
sample also differed in minor known ways from the full Knowledge Networks
panel andthe U.S.population asawhole.Second,ourstudywasadministered
only in English, limiting our ability to comment on Latino patients who
respond to CAHPS in Spanish, whose ERT and other differences from
non-Latino whites are particularly large (Weech-Maldonado et al. 2008).
Third, due to sample size limitations, we were unable to study Asians, a group
for whom some of the strongest differences in CAHPS information have
been observed (Weech-Maldonado et al. 2001, 2004). Fourth, the present
study was administered via the Internet, rather than the mail and telephone
modes currently recommended for the CAHPS Clinician and Group Survey
(Agency for Healthcare Research and Quality 2008). However, a mode
experiment using similar items from the CAHPS Hospital Survey found that
et al. 2009a). Fifth, the current study examined only evaluations of physicians;
future research might address the extent to which differences in expectations
play a larger role in racial/ethnic differences in assessments of access to care
or customer service. Finally, our focus here was limited to investigating the
extent to which previous findings of differences in mean patient experience
evaluations and ERT observed for patients’ real-world encounters might also
occur in response to standardized scenarios. Such an approach does not
groups. Larger scale item response theory work on responses to vignettes
such as these could substantially extend our findings; nonetheless, the more
limited scope here provides insight into the interpretation of previously
In addition, it is important to note that respondents may use modified
CAHPS items and response scales that were adapted to the experiences of a
third party in a single encounter (rather than for themselves over a period of
time) somewhat differently than they useCAHPS measures of experiencesfor
Standardized Encounters to Understand Racial/Ethnic Disparities505