The Patient-Centered Medical Home
and Patient Experience
Grant R. Martsolf, Jeffrey A.Alexander, Yunfeng Shi, Lawrence
P.Casalino,Diane R.Rittenhouse, Dennis P. Scanlon,and
Stephen M. Shortell
Objective. To examine the relationship between practices’ reported use of patient-
centered medical home (PCMH) processes and patients’ perceptions of their care
Data Source. Primary survey data from 393 physician practices and 1,304 patients
receiving care in those practices.
Study Design. This is an observational, cross-sectional study. Using standard ordin-
ary least-squares and a sample selection model, we estimated the association between
patients’ care experience and the use of PCMH processes in the practices where they
Data Collection. We linked data from a nationally representative survey of individu-
als with chronic disease and two nationally representative surveys of physician
Principal Findings. We found that practices’ use of PCMH processes was not associ-
ated with patient experience after controlling for sample selection as well as practice
Conclusions. In our study, which was large, but somewhat limited in its measures of
the PCMH and of patient experience, we found no association between PCMH pro-
cesses and patient experience. The continued accumulation of evidence related to the
possibilities of the PCMH, how PCMH is measured, and how the impact of PCMH is
gauged provides important information forhealth care decision makers.
Key Words. Patient-centered medical home, patient care experience, primary
care, chronic disease
The U.S. health care system exhibits substantial gaps between current prac-
tices and optimal care. These gaps have been associated with preventable
deaths, morbidity, cost, and consumer dissatisfaction (Institute of Medicine,
Committee on Quality of Health Care in America 2001). To address these
problems, groups such as the American College of Physicians, the American
Academy of Family Physicians, and others have promoted the redesign of
Health Services Research
organizational infrastructure and clinical care processes in accordance with
the functional domains of the patient-centered medical home (PCMH).
PCMH is a “team-based model of care led by a personal physician who
provides continuous and coordinated care throughout a patient’s lifetime in
order to maximize health outcomes” (American College of Physicians 2010).
The seven principles of the PCMH include a personal physician, physician-
directed medical practice, whole-person orientation, coordinated and
integrated care, quality measurement and improvement, enhanced access to
care, and payment reforms (Patient-Centered Care Collaborative 2007).
Muchof the existingresearchon the PCMH has focused on the relation-
ship between medical homes1and health care–relatedoutcomes. Several stud-
ies, for example, suggest that medical homes are associated with increased
utilization of preventive services (Gill et al. 2005; Ferrante et al. 2010;
Jaen et al. 2010) and decreased hospitalizations and emergency room visits
(Palfrey et al. 2004; Gill et al. 2005; Martin et al. 2007; Cooley et al. 2009;
Rankin et al. 2009; Roby et al. 2010). Others have also identified improve-
ments in quality (Rankin et al. 2009; Reid et al. 2009, 2010; Jaen et al. 2010 )
and reducedclinician burnout (Reidet al. 2009, 2010).
However, the relationship between PCMH and patients’ care experi-
ence has received less research attention, despite the fact that the PCMH
model places both practical and philosophical emphasis on patient-centered
care. The limited existing research in this area has produced mixed findings.
While some studies have found an association between the PCMH and
improved patient experience, others have found that patients have had
negative or no significant changes in experiences after PCMH implementa-
tion (DeVoe et al.2008; Reid et al. 2009, 2010; Jaen et al.2010).
This study examines the relationship between the degree to which
physician practices use PCMH processes and patients’ perceptions of the care
experience in those practices. Previous related studies have been limited to
Address correspondence to Grant R. Martsolf, R.N., M.P.H., Doctoral Candidate, Pennsylvania
StateUniversity,DepartmentofHealthPolicyandAdministration,610 N EuclidAve.,Pittsburgh,
PA 15206; e-mail: email@example.com. Jeffrey A. Alexander, Ph.D., is with the Department of
Health Policy and Management, University of Michigan, Ann Arbor, MI. Yunfeng Shi, Ph.D., is
with the Center for Health Care Policy and Research, Pennsylvania State University, PA.
LawrenceP.Casalino,M.D., M.P.H.,Ph.D.,is withthe DepartmentofPublicHealth,WeillCornell
Medical College, New York, NY. Diane R. Rittenhouse, M.D., M.P.H., is with the Department of
Family and Community Medicine, University of San Francisco, San Francisco, CA. Dennis P.
Scanlon, Ph.D., is with the Department of Health Policy and Administration, Pennsylvania State
University, PA. Stephen M. Shortell, Ph.D., M.P.H., M.B.A., is with the School of Public Health,
2HSR:Health Services Research
single-source survey data (such as the Medical Expenditure Panel Survey),
single health systems, or practices that had self-selected into a PCMH demon-
stration project. Our study attempts to address some of these limitations by
using a large novel dataset that links responses from a national survey of
individuals with chronic disease to two independently administered national
surveys of physician practices. Furthermore, this dataset allows us to analyze
the association between PCMH processes and patient experience across a
large number of diverse practices. A recent paper issued by the Agency for
Healthcare Research and Quality ([AHRQ] AHRQ 2011) suggested that,
instead of evaluating PCMH implementation within a small set of practices
with a large number of patients at each practice, researchers should instead
use data from across multiple practices with fewer patients in each practice
(AHRQ 2011). Our dataset is uniquely structured to do that. Using this
dataset, we address the following research question: Do patients who receive
care in physician practices that use more PCMH processes report better care
experiences,conditional on other patientand practice characteristics?
PCMHAND PATIENT EXPERIENCE
The concept of patient experience, as contrasted to the more generic “patient
satisfaction,” pertains to how patients perceive specific aspects of the care they
receive from their provider. In contrast to patient satisfaction, patient experi-
ence is thought to be a more useful indicator of quality because it provides a
clear basis for actionable improvements (Jenkinson, Coulter, and Bruster
2002). We focus on three aspects of patient experience: interpersonal
exchange, treatmentgoal setting, and out-of-office contact.
Under the basic principles of the PCMH, patients actively participate in deci-
sion making and physicians seek feedback to ensure patients’ expectations are
being met. Positive interpersonal exchange occurs when providers spend time
listening to patients, develop whole-person knowledge, explain things clearly,
and provide the kinds of information each patient wants. Such experiences
can establish a supportive context for patients to shift from the traditional
passive role to one where they participate more actively in their health care
(Stewart 1995; Blanquicett et al. 2007). Empirical evidence suggests that high-
quality physician communication with patients has been linked to higher
levels of patient adherence to treatment plans, improved self-management of
disease, greater recall of important treatment information, and improved
mental and physical health status (Roter, Hall, and Aoki 2002; Travaline,
Ruchinskas,and D’Alonzo2005; Ratanawongsa et al. 2008).
Treatment Goal Setting
Treatment goal setting involves joint physician-patient decision making to
develop clear and agreed-upon care plans that specifically incorporate prefer-
ences of both physicians and patients. The nature and extent of patients’
involvement in decision making about their care plan can affect patients’
perceptions of their role in their health and health care, their attitude towards
this role, and their confidence to successfully perform the required behaviors
to achieve betterhealth.
Under the PCMH principles, enhanced access to care should be available
through new options for communication between patients, their personal
physician, and practice staff, including those that link providers and patients
outside of the traditional office visit. For example, e-mail communication has
been found to be a more convenient form of communication that increases
satisfaction among patients (Leong et al. 2005; Rosen and Kwoh 2007;
Stalberg et al. 2008; Ye et al. 2010). The importance of out-of-office contact is
supported by the fact that approximately one-fifth of U.S. adults reported that
they do not get “enough time” with their physician during an office visit
(Kaiser Family Foundation 2005) and almost half of them said that they had
difficulty in understanding instructions they received from the physician’s
nication beyond the traditional office visit may provide opportunities to
impart and develop patients’ knowledge, skills, understanding, and confi-
dence in their role as active participants in their own care.
The study group was obtained by merging three datasets: (1) the Aligning Forces
for Quality Consumer Survey (AF4QCS), (2) the 2nd National Survey of
4HSR:Health Services Research
Physician Organizations (NSPO2), and (3) the National Survey of Small and
Medium Physician Practices (NSSMPP). The AF4QCS was a population-based
random-digit-dial survey of individuals with chronic conditions administered
between June 2007 and June 2008. The survey included questions related to
consumerengagement,exposure toanduse ofpublicreports,andpatientexperi-
ence with their providers. The sample consisted of individuals who were 18 or
older with one or more of five chronic conditions: asthma, diabetes, hyperten-
sion, heart disease, and depression. Respondents were randomly sampled from
the 14 original communities that received grant funding from the Robert Wood
Johnson Foundation (RWJF) through the Aligning Forces for Quality project
([AF4Q] Painter and Lavizzo-Mourey 2008). This sample was supplemented
by a national sample of consumers from non-AF4Q communities. The final
sample size was 8,140 individuals. The overall response rate for the survey was
27.6 percent using the AAPOR (American Association of Public Opinion
Research) method of response rate calculation and 45.8 percent using the
CASRO (Council of American Survey Research Organizations) method. To
assess the degree of nonresponse bias in our survey, we compared respondents
in our sample against the 2008 National Health Interview Survey (NHIS)
because NHIS has achieved a 90 percent response rate and thus is arguably
less likely to be subject to nonresponse bias. We found negligible differences
between our respondents and the comparable NHIS sample on demographic
characteristics and prevalence of chronic conditions. For example, 49.6 percent
of our weighted sample was male compared to 48.3 percent in the NHIS. Simi-
larly, 27.4 percent of our weighted sample had hypertension compared to 27.1
percent of the NHIS sample.
NSPO2 was a nationally representative sample of large physician prac-
tices funded by the RWJF that was conducted from March 2006 to March
2007. Practices were included in the sample if they had 20 or more physicians
and were primary care, single-specialty cardiology, endocrinology, or
pulmonology, or multispecialty practices with significant numbers of physi-
cians in these specialties. Five hundred thirty-eight practices responded to this
survey; the overall response rate was60.3 percent.
NSSMPP, alsofundedby RWJF, was a nationally representative sample
of small and medium sized physician practices that included oversamples in
the AF4Q communities. NSSMPP was conducted from July 2007 to March
2009. Practices were included in the survey if they had between 1 and 19
physicians in the same practice types as NSPO2. The final sample size for this
survey was 1,809 practices. The overall adjusted response rate was 63.9
istrator in the practice and included questions related to practices’ use of key
processes and structures such as information technology, care management
processes, and provision of preventive care services. Details related to NSPO
Because the three surveys were conducted independently, it was neces-
sary to mergeconsumer survey responseswith the physician practice informa-
tion. At the end of the AF4QCS, respondents were asked the name of the
doctor that they saw most frequently, the name of the group or clinic that the
doctor belonged to, and the city and state in whichthe doctor (or group/clinic)
was located. These answers were used to match AF4QCS respondents to phy-
sician survey respondents. The final matching was performed based on con-
servative matching rules by a trained research assistant. The AF4QCS
respondents correctly matched to a primary care or multispecialty practice in
the physician surveys were retainedin the dataset.
About 1,304 consumers in the AF4QCS (16 percent of all respondents)
were matched to 393 physician practices in either NSSMPP or NSPO2; 872
(66.9 percent) of the consumer matches were to practices participating in
NSSMPP. Matched respondents were more likely to be white, male, high-
income,and better educated.Itis important to note, therefore, that our sample
should be considered a study group rather than a nationally representative
sample. Respondents remained unmatched for two specific reasons. First, a
practice reported by an AF4QCS respondent may not have been surveyed in
NSSMPP or NSPO2. This type of nonmatch represented the majority
(roughly 65 percent) of nonmatches. Our sample also includes more medium-
sized practices, primary care practices, and practices with a higher proportion
of Medicaid and uninsured patients than the full sample of practices. Accord-
ingly, our sample should be considered as a study group. Second, a respon-
dent may not have provided useful physician practice information or refused
to answer the questions. These types of nonmatches represented a minority
(roughly 35 percent), but still significant proportion of the nonmatches. We
employed a selection model (explained below) that attempted to correct for
the potential biasdue to systematic nonmatches.
Our study examined three measures of patient care experience: (1) interper-
sonal exchange, (2) treatment goal setting, and (3) out-of-office contact. These
6 HSR:Health Services Research
measures of patient experience were based on nine survey items related to
patients’interactionswith their physicianduringandoutside ofcarevisitsover
the past 6–12 months. The specific items can be found in Appendix S1. These
items have been used in previous studies of patients’ perceptions of the care
experience. The interpersonal exchange measure captures the quality of inter-
personal exchange during office visits; for example, whether the physician
explains things clearly and spends enough time with the patient (Hays et al.
1999). Treatment goal setting is an indicator of collaboration between physi-
cians and patients when establishing care plans (Glasgow et al. 2005; Strouse
et al. 2009). Out-of-office contact represents contact by physicians through
phone, mail, or e-mail, outside of the office visit (Albright et al. 1999; Free-
man, Sullivan & Company1999; Wasserman et al. 2001).
Responses to these nine items were subjected to a confirmatory factor
analysis to assess whether they constituted distinct dimensions of the patient
experience. Factor analysis results supported a three-factor solution consistent
with the three theoretical dimensions of patient care experience (Bentler and
Bonett 1980; Browne and Cudeck 1993). Accordingly, three scales were con-
structed by averaging the scores for the three relevant items in each factor.
All items were scored such that higher scores reflected more positive patient
experience. A number of alternative approaches to calculating these variables
were also considered. For example, we made each of the individual items bin-
ary and calculated the average of those items. We also calculated factor scores,
but because the factor loadings were nearly identical the factor score was
essentially the same as the simple mean. The results were robust to different
Patient-CenteredMedical Home Index
The main independent variable is the PCMH Index, which measures the use
of specific PCMH processes. This index is comprised of four subindices that
measure 4 of 7 principles of the PCMH model, which were combined to form
a single index. These subindices are created using the same approach as previ-
ous studies (Rittenhouse et al. 2008, 2011) and include the following: physi-
cian-directed medical practice, coordination and integration, quality and
safety, and enhanced access. The specific components are outlined in Appen-
ThePCMH Indexwascalculated asthesummationofthefour subindex
scores after they were standardized on a 0–1 scale (ranging from 0 to 4).
Practices with 1–2 physicians were not asked about primary care teams, so the
Patient-Centered MedicalHome 7
PCMH Index ranged from 0 to 3 for those practices. Therefore, the PCMH
for that practice and multiplied by 4. To check the sensitivity of the results to
different calculation techniques, we also calculated the PCMH Index by add-
ing the total points in each subindex (ranging from 0 to 17) without standardiz-
ing. Resultswere very similar using both approaches.
It is important to emphasize that our PCMH Index is not intended to
represent PCMH as an integrated system of care, but rather a measure of the
extent to which physician practices use processes associated with the PCMH
model. The measure makes no assumptions about the relationship among
these processes, the order in which they are adopted and implemented, or the
relativeimportance of the process to the PCMH model of care.
We also included a number of practice-level and patient-level covariates that
webelieve,basedontheliterature andtheory, werelikelyassociatedwithboth
being in a practice with more PCMH processes and perceiving a better care
experience. The variable specifications for the patient-level and practice-level
control variables are shown in Table 1.
Practice-Level Covariates. We included three variables that capture characteris-
tics of the physician practices where patientsreceive their care, includingprac-
tice size, primary care versus multispecialty practices, the ownership status of
the practice, and the proportion of revenue that came from patients that had
either Medicaid or were uninsured. Each of these variables likely affects the
type and level of resources available as well as the interest and willingness to
implement PCMH processes (Rittenhouse et al. 2008; Goldberg and Kuzel,
2010; Rittenhouse et al. 2011). Furthermore, patients at practices with differ-
ent levels of practice-level covariates are also likely to have different care
experiences (Rodriguezet al. 2009).
Patient-Level Covariates. Three categories of patient-level control variables
were incorporated in this study: socio-demographics, health status, and
exposure to health care provider. Four socio-demographic variables included
race, age, education, and income. Health status was measured by the pres-
ence any of five chronic conditions (diabetes, hypertension, heart disease,
8 HSR:Health Services Research
asthma, and depression). Measures of respondents’ exposure to their current
provider included the number of provider visits over a 3-month period and
whether the respondent switched providers over the past year. We included
patient-level characteristics because they may be associated with being in a
Table 1: Sample Descriptive Statistics
practice that uses more PCMH processes (Raphael et al. 2009; Stevens et al.
2009) and because they are also likely correlated with the patient care experi-
ence (The Commonwealth Fund 2002; Willems et al. 2005; Wilshire et al.
As a baseline analysis, we estimated a linear regression model with the follow-
PEij¼ a þ b ? PCMHjþ
where i and j index patients and physician practices, respectively. The out-
come variable on the left-hand side measures (a particular aspect of) the
patient experience. Our key explanatory variable is PCMH. There are K
patient-level and N practice-level covariates (X’s and Z’s) included, corre-
sponding to the same number of parameters. The stochastic term e represents
unobserved patient and practice characteristics. We estimated the model using
ordinary least-squares (OLS) with robust standard errors clustered at the level
of physician practices.
Because many patientscould not be matchedto apractice in the physician sur-
veys, there was a potential concern about sample selection bias, as the patients
who gave usable physician information may systematically differ from those
who failed to do so. If such (unobserved) systematic differences were corre-
lated with patient experience, our regression coefficients from OLS would be
biased and inconsistent.
To address this potential problem, we adopted the selection model
proposed by Heckman (1979). Our motivation for using the model was
two-fold. First, an initial comparison showed that the average experience
measures of the patients included in the final sample were higher than
patients who responded to the survey but were not matched to a NSSMPP
or NSPO2 practice. Second, the potential effects of the PCMH on the expe-
rience of all the patients in the consumer survey (not just the study group)
10HSR: Health Services Research
were of interest in this case. Without correcting for the possible selection
bias, our conclusion from the model cannot be generalized beyond the
The Heckman approach extended our baseline model to include the
PrðSi¼ 1jWiÞ ¼ Fð/0þ
where S is a binary variable indicating whether the patient was in the final
study group. There are M predicting variables (W’s) in the equation, includ-
ing patient-level characteristics from the main equation (1) including age,
race, income, and education. F (.) is the standard normal cumulative distri-
bution function. Equations (1) and (2) are estimated sequentially by the
standard two-step procedure (Heckman 1979), with adjusted standard
errors. Because the predicting variables were also used in the main equa-
tion, the selection model presented here relies solely on the normality
assumption for identification.
In an alternative specification, we also used additional exclusion
restrictions as “selection instruments.”2Four variables were created from
the data and used as exclusion restrictions in the first-stage equation of our
selection model. We used three dummy variables (1 = nonmissing,
0 = missing) measuring an individual’s tendency and accuracy of reporting
potentially sensitive personal information, including (1) working e-mail
address, (2) street address, and (3) reference contact that could be used to
locate the respondents for a follow-up survey. We also included another
variable indicating the general missingness patterns in the responses, count-
ing the frequency (ranging from 0–7) of missing and “don’t know” among
the following variables: income, having a regular physician, diagnosis of
diabetes, diagnosis of heart disease, diagnosis of hypertension, diagnosis of
asthma, and diagnosis of depression. The regressors in this selection equa-
tion (first stage) were jointly significant, indicating that our first-stage model
had overall explanatory power for the selection mechanism. Also, two of
the variables serving as exclusion restrictions (count of missing values and
missing follow-up references) were individually significant, indicating that
our “selection instruments” were operational (Madden 2008). However,
since the validity of those exclusion restrictions cannot be easily checked
and results across both selection models were nearly identical, we chose to
present the version without them.
Patient-Centered Medical Home11
Multivariate Regression Results
Table 2 presents multivariate regression results for the relationship between
the use of PCMH processes and the three measures of patient experience.
Based on the estimated coefficients, the use of PCMH processes was not sig-
nificantly associated with any of the three measures of patient experience.
Results are discussed for the OLS model, while the selection model (Table 3)
was considered as a secondary specification. The results from the two models
Table 2:Ordinary Least-Squares (OLS)Multivariate Regression Results
Interpersonal ExchangeTreatment GoalSetting Out-of-Office Contact
*p < .05;**p < .01;***p < .001.
12HSR: Health Services Research
Table 3: Heckman Selection ModelResults
Medicaid and uninsured
Inverse Mills ratio
*p < .05;**p < .01;***p < .001.
Patient-Centered MedicalHome 13
were similar. Associations between patient experience and each of the prac-
tice-level and patient-level covariatesare discussed below.
Interpersonal Exchange. Patients were significantly more likely to express posi-
tive perceptions of their interpersonal exchange with providers if the practice
in which they received care was jointlyowned by physicians and a hospital
compared to those in a purely physician-owned practice. Individuals who had
higher levels of education and income were more likely to hold positive
perceptions of the quality of interpersonal exchange while Hispanic patients,
those with more provider visits, and those who recently switched providers
were less likelyto hold positive perceptions.
Treatment Goal Setting. No practice characteristics were significantly associ-
ated with patients’ perceptions of the quality of treatment goal setting with
their providers. Patients who had diabetes were more likely to perceive higher
quality treatment goal setting experiences with their physicians. However,
patients who recently switched physicians were less likely to positively view
the treatment goal setting experience with providers.
Out-of-OfficeContact. Nopracticecharacteristics wereassociatedwithpatients’
perceptions of out-of-office contact with providers. Individuals who were
older, black, who had diabetes, heart disease, or asthma, and who had more
provider visits in the past year were more likely to hold positive views of out-
of-office contact withtheir providers.
Joint Significance of Grouped Covariates. As shown in the main results (Table 2),
the majority of the covariates were not significant individually and there was
no clear pattern. To address our concern for the overall explanatory power of
the model, we combined patient-level and the practice-level covariates into
three groups and tested the joint significance of the grouped variables. The
three groups were the following: (1) patient socio-demographic characteristics
(i.e., age, race, income, and education); (2) patient health status (i.e., the five
chronic illness indicators); and (3) physician practice characteristics (other
than the PCMH Index). The results are presented in Table 4. Both practice
and patient characteristics were jointly associated with a number of the patient
14 HSR: Health Services Research
care experience variables, even if many of the individual variables were not
Despite the importance of patient-centered care in the PCMH model, rela-
tively little research has assessed the care experienced by patients in practices
conforming to the PCMH model. We find that, after accounting for sample
selection bias and controlling for patient and practice characteristics, greater
use of PCMH processes was not associated with our measures of patient expe-
rience. We believe that there are five possible explanations for the lackof asso-
First, our measure includes a large number of processes necessary, but
not sufficient, for the full implementation of the PCMH model. These pro-
cesses correspond to four of the seven PCMH principles and exclude impor-
tant aspects of patient-centered care such as having a personal physician or
whole-person care that, if measured, might impact patient experience. Some
of the processes that we measured may not have a strong impact on the
elements of patient experience included in our study. For example, whereas
the use of patient registries is an important component of the PCMH model,
patients are unlikely to be aware of such registries or to understand their use.
The use of registries and other “back office” components of the PCMH
model will influence patients’ care experience only to the extent that the use
of these components contributes to, or distracts from, the delivery of patient-
centered care. Furthermore, our measures of the four PCMH principles are
comprehensive but not complete. For example, our measures of enhanced
access are limited to only two processes and do not include a measure of af-
Table 4:JointSignificanceof GroupedCovariates
F p-valueF p-valueF p-value
*Specific variablesfoundin Table 1.
Patient-Centered Medical Home 15
Second, most practices we surveyed were not explicitly attempting
to become a PCMH and used relatively few of the measured PCMH pro-
cesses; none had implemented the full complement. It is plausible that the
impact of the PCMH may not be felt by patients until practices are closer
to full implementation of the model, and that implementation of the
whole is more than the sum of its parts. The holistic view of the PCMH
would suggest that until the PCMH is fully integrated as a system of care
in a physician practices, measureable results at the patient level may be
Third, we estimated essentially a contemporaneous relationship
between the PCMH Index and patient care experience. However, it may be
the case that PCMH processes have a lagged effect on patient experience.
It may take time for measureable results to be detected. For example, as prac-
tices begin to incorporate electronic health records and change provider roles
and workflows, these changes may initially increase wait times and provider
frustration and have a mixed impact on the patient experience. Related to the
timing of the data collection, it is worth noting that some of the physician
survey data was collected after some of the consumer survey data. To the
extent that the PCMH processes were undertaken by the practice after a
patient’s data was collected, the timing of the surveys may contribute to the
Fourth, another potential explanation is that we may not have a large
enough sample size to estimate small effects of PCMH on patient experience.
Because the dependent variables and the PCMH Index are aggregates from
ordinal scales, it is hard to know a priori what the effect size might be. There-
fore, it is difficult to assess whether power is an important explanation for the
lack of statistically significant findings. However, our sample size is similar to
or larger than other studies that have investigated the relationship between
PCMH and patient experience. We believe that our sample, combined with
the detailed information of PCMH processes, is already an improvement over
previousstudiesin termsof data.
Finally, it is possible that the PCMH model may not actually have an
association with patient experience. Patient experience is a complex, multidi-
mensional concept driven by an array of individual and environmental influ-
ences that the PCMH model, or any practice model, may not be able to
address (Stevens and Shi 2003; Rodriguez et al. 2009). These influences are
likely to be varied and vast, including patient characteristics that cannot be
easily measured such as prior expectations, preferences, attitudes, and avail-
able resources that may be in place before a patient ever interacts with a
16 HSR: Health Services Research
provider. Therefore, patient experience may be difficult to impact, given that
providers may have limited interaction with patients and lack control over
outside or prior factors that may shape expectations, preferences, and
attitudes. Although the PCMH aims to transform the nature of the physician-
patient relationship, it is not yet known whether patients will become more
active in their care or change their expectations of the care encounter with
their providers under the PCMH.
Our results raise a number of questions and considerations. First, many
of the existing measures of the PCMH illustrate an important conceptual
question: Is PCMH more than the sum of its parts? PCMH is a holistic model.
However, in practice, the model can be operationalized as a collection of pro-
cesses and changes. It is unclear at what point a practice might cross over from
having a number of PCMH processes to becoming a “PCMH” that might
begin to significantly affect patient experience. Also, our findings raise an
important question about how the impact of the PCMH might be measured.
We find that contemporaneous measures of patient experience are not associ-
ated with the use of PCMH processes. This does not mean that patient experi-
ence is not important, but it does suggest the need for more thinking about
how and when to measure the impact of PCMH. As criteria are developed to
gauge PCMH implementation and its impact, decision makers should con-
sider the complex associations between particular aspects of the model, multi-
ple importantoutcomes, and time.
Finally, despite the fact that we did not find a statistically significant
association between the use of PCMH processes and patient experience,
PCMH model. Our results do suggest that practices should execute the
model with close attention to enhancing the experience of patients. Fur-
thermore, policy may also have to include incentives for patients to assume
a more active role in their care in addition to supporting physician pay-
concept underlying the
Our study has important limitations. The study groupthatwe used may not be
representative of a given population of patients or practices. The matching of
patients and practices was incidental, and we were able to match only a small
proportion of patients from the patient survey. Although we used the
Heckman model to correct for patient selection in our study group, such a
Patient-Centered Medical Home 17
procedure would not completely solve the problem of a nonrepresentative
patient sample. Therefore, our results need to be interpreted with caution,
especially when generalizing. However, there are very little data on the rela-
tionship between patient experience and the PCMH, and our approach yields
a sample that is larger and more geographically diverse than most other simi-
In addition, this is a cross-sectional study and causality cannot be
inferred. Although our model controlled for a number of important patient
and practice characteristics, the PCMH Index is likely endogenous as there
may still be unobserved factors associated with both the PCMH Index and
patient experience. For example, if the physicians in a practice are highly
motivated to improve patient care because of organizational culture or
strong local competition, they may implement more PCMH processes
while taking other unobserved actions that improve the care experience. In
such a case, the estimated PCMH coefficient in our model would be biased
The PCMH has emerged as a prominent approach to improving physician
practice and is being adopted, endorsed, and promoted by stakeholders
from across the health care landscape (Rittenhouse and Shortell 2009; Cen-
ters for Medicare and Medicaid Services 2011). In our study, which
included a large number of patients and practices but is somewhat limited
in its measures of the PCMH and of patient experience, we did not find a
significant cross-sectional association between the use of PCMH processes
and patient experience. The continued accumulation of evidence related to
the possibilities and limitations of the PCMH, how PCMH is measured,
and how the impact of PCMH is gauged can provide critical information to
health care leaders working to develop more effective ambulatory care
Joint Acknowledgment/Disclosure Statement: This research was supported by the
Robert Wood Johnson Foundation through the Aligning Forces for Quality
18 HSR: Health Services Research
1. Some of this literature focuses on “medical homes” more generally, as opposed to
the more recent specification of “patient-centered medical homes.” However, the
operational differences are not large enough to warrant considering them different
2. As those variables were also used in the main equation, the selection model
presented here relies solely on the normality assumption for identification. In an
alternative specification, we used additional exclusion restrictions as our “selection
instruments.” No meaningful difference has been found between the two specifica-
tions. Since the validity of those exclusion restrictions cannot be easily checked, we
choosetopresent the versionwithout them.
AHRQ. 2011. “Improving Evaluations of the Medical Home” [accessed on January 20,
2012]. Available at: http://pcmh.ahrq.gov/portal/server.pt/gateway/PTAR
Albright, A. L., D. Hopkins, G. Boyce-Smith, and J. Wasserman. 1999. California Col-
laborative to Improve Diabetes Management (CCHRI). Chicago: American Public
Health Association Conference.
American College of Physicians. 2010. “Patient-Centered Medical Homes” [accessed
on September 7, 2010]. Available at http://www.acponline.org/running_
Bentler, P. M., and D. G. Bonett. 1980. “Significance Tests and Goodness of Fit in the
Analysis ofCovarianceStructures.”PsychologicalBulletin 88: 588–606.
Blanquicett, C., J. H. Amsbary, C. Mills, and L. Powell. 2007. “Examining the
Perceptions of Doctor-Patient Communication.” Human Communication 10
Browne, M. W., and R. Cudeck. 1993. “Alternative Ways of Assessing Model Fit.”
In Testing Structural Equation Models, edited by K. A. Bollen and J. S. Long,
pp. 445–55.Newbury Park, CA: SagePublications.
Centers for Medicare and Medicaid Services. 2011. “Details for Medicare Medical
Mome Demonstration” [accessed on February 8, 2011]. Available at https://
The Commonwealth Fund.2002. “Diverse Communities, Common Concerns:Assess-
ingHealth Care Quality for
August 8 2011]. Available at http://www.commonwealthfund.org/~/media/
Patient-Centered Medical Home 19
Cooley, W. C., J. W. McAllister, K. Sherrieb, and K. Kuhlthau. 2009. “Improved Out-
comes Associated with Medical Home Implementation in Pediatric Primary
Care.” Pediatrics 124 (1): 358–64.
DeVoe, J. E., L. S. Wallace, N. Pandhi, R. Solotaroff, and G. E. Fryer Jr. 2008. “Com-
prehending Care in a Medical Home: A Usual Source of Care and Patients Per-
ceptions about Healthcare Communication.” Journal of the American Board of
Family Medicine 21 (5): 441–50.
ples of the Patient-Centered-Medical Home and Preventive Services Delivery.”
Annals ofFamilyMedicine 8 (2): 108–16.
Freeman, Sullivan & Company. 1999. Survey Operations Report: Diabetes Patient Survey.
Prepared for the California Collaborative to Improve Diabetes Management.
SanFrancisco:Freeman,Sullivan & Co.
Gill, J. M., H. B. Fagan, B. Townsend, and A. G. Mainous. 2005. “Impact of Providing
a Medical Home to the Uninsured: Evaluation of a Statewide Program.” Journal
ofHealth Carefor the Poorand Underserved 16 (3): 515–35.
Glasgow, R. E., E. H. Wagner, J. Schaefer, L. D. Mahoney, R. J. Reid, and S. M.
Greene. 2005. “Development and Validation of the Patient Assessment of
ChronicIllness Care (PACIC).”Medical Care43: 436–44.
Goldberg, D. G., and A. J. Kuzel. 2010. “Elements of the Patient-Centered Med-
ical Home in family practices in Virginia.” Annals of Family Medicine 7 (4):
Hays, R., J. Shaul, V. Williams, J. Lubalin, L. Harris, S. Sweeny, and P. Clearly. 1999.
“Psychometic Properties of the CAHPS 1.0 Survey Measures.” Medical Care 37:
Heckman, J.1979. “Sample Selection Bias as a Specification Error.” Econometrica 47 (1):
Institute of Medicine, Committee on Quality of Health Care in America. 2001. Crossing
the Quality Chasm: A New Health Systemfor the 21st Century. Washington, DC: Insti-
Jaen, C. R., R. L. Ferrer, W. L. Miller, R. F. Palmer, R. Wood, M. Davila, E. E. Stewart,
B. F. Crabtree, P. A. Nutting, and K. C. Stange. 2010. “Patient Outcomes at 26
Months in the Patient-Centered Medical Home National Demonstration Pro-
ject.”Annals of Family Medicine 8 (1): S57–S67.
Jenkinson, C., A. Coulter, and S. Bruster. 2002. “The Picker Patient Experience
Questionnaire: Development and Validation Using Data from In-Patient Sur-
veys in Five Countries.” International Journal of Quality in Health Care 14 (5):
Kaiser Family Foundation. 2005. “Health Care Costs Survey” [accessed on May 22,
2011]. Available at http://www.kff.org/newsmedia/upload/7371.pdf
Leong, S. L., D. Gingrich, P. R. Lewis, D. T. Mauger, and J. H. George. 2005. “Enhanc-
ing Doctor-Patient Communication Using Email: A Pilot Study.” Journal of the
American Board of Family Practice 18 (3): 180–8.
20HSR: Health Services Research
Madden, D. 2008. “Sample Selection Versus Two-Part Models Revisited: The
Case of Female Smoking and Drinking.” Journal of Health Economics 27:
Martin, Brock A., S. Crawford, J. C. Probst, G. Smith, R. P. Saunders, K. W.
Watkins, and K. Luchok. 2007. “Medical Homes for Children with Special
Health Care Needs.” Journal of Health Care for the Poor and Underserved 18
Painter, M. W., and R. Lavizzo-Mourey. 2008. “Aligning Forces for Quality: A Pro-
gram to Improve Health and Health Care in Communities across the United
States.”Health Affairs 27 (5): 1461–3.
Palfrey, S., L. A. Sofis, E. J. Davidson, J. Liu, L. Freeman, and M. L. Ganz. 2004. “The
Pediatrics 113 (5): 1507–16.
Patient-Centered Care Collaborative. 2007. “Joint Principles of the Patient-Centered
Medical Home” [accessed onJune 23, 2011]. Available at http://www.pcpcc.net/
Rankin, K. M., A. Cooper, K. Sanabria, H. J. Binns, and C. Onufer. 2009. “Illinois
Medical Home Project: Pilot Intervention and Evaluation.” American Journal of
Medical Quality 24 (4): 302–9.
Raphael, J. L., B. A. Guadagnolo, A. C. Beal, and A. P. Giardino. 2009. “Racial and
Ethnic Disparities in Indicators of a Primary Care Medical Home for Children.”
Academic Pediatrics 9: 221–7.
Ratanawongsa, N., D. Roter, M. C. Beach, S. L. Laird, S. M. Larson, K. A.
Carson, and L. A. Cooper 2008. “Physician Burnout and Patient-Physician
during Primary Care Encounters.” Journal of General Internal Medicine 23 (10):
Reid, Robert J., Paul A. Fishman, Yu Onchee, Tyler R. Ross, James T. Tufano, Michael
P. Soman, and Eric B. Larson. 2009. “Patient-Centered Medical Home Demon-
stration: A Prospective, Quasi-Experimental, before and after Evaluation.”
American Journal ofManaged Care15 (9): 71–87.
Reid, R. J., K. Coleman, E. A. Johnson, P. A. Fishman, C. Hsu, M. P. Soman, C. E.
Trescott, M. Erikson, and E. B. Larson. 2010. “The Group Health Medical
Home at Year Two: Cost Savings, Higher Patient Satisfaction, and Less Burn-
out for Providers.” Health Affairs 29 (5): 835–43.
Rittenhouse, D. R., and S. M. Shortell. 2009. “The Patient-Centered Home: Will It
Stand the Test of Health Reform?” Journal of the American Medical Association 301
Rittenhouse, D. R., L. Casalino, R. R. Gilles, S. S. Shortell, and B. Lau. 2008. “Measur-
ing the Medical Home Infrastructure in Large Medical Groups.” Health Affairs
27 (5): 1246–58.
Rittenhouse, D. R., L. Casalino, R. R. Gilles, S. S. Shortell, J. A. Alexander,
S. McClellan, and M. Drum. 2011. “Small and Medium-Sized Physician Prac-
tices Use Few Patient-Centered Medical Processes.” Health Affairs 30 (8):
Patient-Centered Medical Home 21
Roby, D. H., N. Pourat, M. J. Pirritano, S. M. Vrungos, H. Dajee, D. Castillo, and G. F.
Kominski. 2010. “Impact of Patient-Centered Medical Home Assignment on
Emergency Room Visits among Uninsured Patients in a County Health Sys-
tem.”Medical CareResearch and Review67 (4): 412–30.
Rodriguez, H. P., T.von Glahn, W. H. Rogers, andD. G. Safran. 2009. “Organizational
and Market Influences on Physician Performance on Patient Experience Mea-
sures.”Health Services Research 44 (3): 880–901.
Rosen, P., and C. Kwoh. 2007. “Patient-Physician E-Mail: An Opportunity to Trans-
form Pediatric Health Care Delivery.”Pediatrics 120 (4): 701–6.
Roter, D. L., J. A. Hall, and Y. Aoki. 2002. “Physician Gender Effects in Medical Com-
munication: A Meta-Analytic Review.” Journal of the American Medical Association
288 (6): 756–64.
Stalberg, P., M. Yeh, G. Ketteridge, H. Delbridge, and G. Delbridge. 2008. “E-Mail
Access and Improved Communication between Patientand Surgeon.” Archives of
Surgery 143 (2): 164–8.
Stevens, G. D., and L. Shi. 2003. “Racial and Ethnic Disparities in the Primary Care
Experiences of Children: A Review of the Literature.” Medical Care Research and
Review 60 (1): 3–30.
Stevens, G. D., T. A. Pickering, M. Seid, and K. Y. Tsai. 2009. “Disparities in the
National Prevalence of a Quality Medical Home for Children with Asthma.”
Academic Pediatrics 9: 235–42.
Stewart, M. A. 1995. “Effective Physician-Patient Communication and Health Out-
comes: AReview.” CanadianMedical AssociationJournal 152 (9): 1423–33.
Strouse, R., B. Carlson, J. Hall, and K. Cybulski. 2009. “Report on Survey Methods In
the Community Tracking Study’s 2007 Round Five Household Survey-Final.”
Report submitted to the Center for Studying Health System Change.
(HSC Technical Publication No. 72). Princeton, NJ: Mathematica Policy
Travaline, J. M., R. Ruchinskas, and G. E. D’Alonzo Jr. 2005. “Patient-Physician Com-
munication: Why and How.” Journal of the American Osteopathic Association 105
Wasserman, J., G. Boyce-Smith, D. S. P. Hopkins, V. Schabert, M. B. Davidson, R. J.
Ozminkowski, A. Albright, and S. Kennedy. 2001. “A Comparison of Diabetes
Patients’ Self-Reported Health Status with Hemoglobin A1cTest Results in 11
California HealthPlans.”Managed Care10 (3): 59–70.
Willems, S., S. De Maesschalck, M. Deveugele, A. Derese, and J. De Maeseneer.
2005. “Socio-Economic Status of the Patient and Doctor-Patient Communica-
tion: Does It Make a Difference?” Patient Education and Counseling 56 (2):
Wilshire, J., V. Roberts, R. Brown, and G. Sarto. 2009. “The Effects of Socioeconomic
Status on Participation in Care among Middle-Aged and Older Adults.” Journal
ofAgingand Health 21 (2): 314–35.
Ye, J., G. Rust, Y. Fry-Johnson, and H. Stothers. 2010. “E-Mail in Patient-Provider
Communication: A Systematic Review.” Patient Education Counseling 80 (20):
22HSR: Health Services Research
SUPPORTING INFORMATION Download full-text
Additional supporting information may be found in the online version of this
AppendixS1: Items Used to CreateDependent Variables.
AppendixS2: Items Used to CreatePCMH Index.
Please note: Wiley-Blackwell is not responsible for the content or func-
tionality of any supporting materials supplied by the authors. Any queries
(other than missing material) should be directed to the corresponding author
for the article.