Patterns of Coordination Within and Between Stages of Work:
Evidence of Modularity in Healthcare
Jody Hoffer Gittell
The Heller School for Social Policy and Management
Brandeis University
Waltham, MA 02454
(781) 736-3680
jgittell@brandeis.edu
Cori Kautz
The Heller School for Social Policy and Management
Brandeis University
Waltham, MA 02454
(781) 736-3736
ckautz@brandeis.edu
R. William Lusenhop
The Heller School for Social Policy and Management
Brandeis University
Waltham, MA 02454
(781) 736-2582
lusenhop@brandeis.edu
Dana Beth Weinberg
Queens College
Queens, NY
(718) 997-2915
dana_weinberg@qc.edu
DRAFT – Do not quote or cite without permission from the author.
Special thanks Dr. John Wright of Brigham and Women’s Hospital and his colleagues for partnering with
us on data collection for this project and to Elizabeth Lingard for advising us on outcomes measurement
for this patient population. Thanks also to Carliss Baldwin, Steve Eppinger and participants in the 7th
International DSM Conference for feedback on extending the theory of modularity to healthcare delivery.
We thank patients and providers from Brigham and Women’s Department of Orthopedics, and its
downstream partners, for participating in this study. We thank Ann-Marie Audet, Steve Schoenbaum and
Mary Jane Koren of the Commonwealth Fund of New York for their support of this study.
Patterns of Coordination Within and Between Stages of Work:
Evidence of Modularity in Healthcare
We explore the proposition that healthcare delivery systems are modular in much the same way
that many manufacturing industries have become. We followed surgical patients from acute care to rehab
care and to home care, surveying their providers at each stage about the coordination of their care. Using
the design structure matrix methodology, we found a modular pattern of coordination with relatively
strong ties within stages of care and relatively weak ties between stages of care. Consistent with the
theory of modularity, system integrators help to integrate across stages of care, though informal
caregivers unexpectedly play the most prominent role. We find that patterns of coordination are more
modular for more complex patients, consistent with modularity theory, but that these patients also have
marginally poorer outcomes. We conclude with implications for modularity theory, coordination theory,
and for making modularity work in healthcare.
(145 words)
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How do people work together and what are the patterns through which they interact? Early
research on patterns of work group interaction was undertaken in the MIT Group Networks Laboratory in
the 1950s (e.g., Bavelas, 1950; Leavitt, 1951; Shaw and Rothschild, 1956), abandoned for several decades
(Monge and Contractor, 2001), then revived in recent years (Argote, Turner and Fichman, 1989; Brown
and Miller, 2000; Sparrowe, 2001; Cummings and Cross, 2003; Perlow, Gittell and Katz, 2004). Distinct
from the large body of research on work groups, this much smaller body of research focuses on measuring
specific ties between people who work together rather than measuring more aggregate concepts such as
group cohesion. The focus on ties has begun to result in a more fine-tuned understanding of the patterns
of interaction through which work gets done, whether those interactions involve helping, knowledge
sharing or coordination. This approach has enabled researchers to better identify organizational practices
and broader institutional forces that shape those patterns in ways that are conducive to getting work done.
While this research sheds light on the existence and usefulness of ties between members of work
groups, it can also help us to answer the question – where should ties be relatively strong and where can
they be relatively weak? If it is not feasible or efficient to build strong ties with every person in one’s
work group, the question of how to best focus one’s limited time and attention on developing strong ties
where they really matter becomes relevant. This question is critical from both an organizational
efficiency perspective as well as from a personal perspective of how to best focus one’s time and effort.
The answer to this question can inform decisions regarding the design of coordinating mechanisms as
well as the design of jobs.
Coordination theory uses the concept of task interdependency to answer the question of where
strong ties are needed and where weak ties will suffice. Coordination, most simply stated, is the
management of task interdependencies (Malone and Crowston, 1994). In Thompson’s (1967) seminal
work on coordination, he argued that different types of task interdependencies call for different types of
coordination. To summarize his argument using the concept of bandwidth introduced by Daft and Lengel
(1986), weaker (sequential or pooled) task interdependencies require relatively low bandwidth forms of
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coordination, while stronger (reciprocal or iterative) task interdependencies require relatively high
bandwidth forms of coordination. According to the theory of relational coordination, communication and
relationship ties are a key source of bandwidth for coordinating work (Gittell, 2005). By extension then,
coordination theory suggests that weak task interdependencies can be coordinated through weak ties,
while strong task interdependencies require strong ties for their successful coordination.
The theory of modularity builds on coordination theory by predicting that certain patterns of weak
and strong ties are likely to emerge for coordinating complex work processes when the underlying
patterns of task interdependence become modularized. Furthermore, boundaries between firms are likely
to be formed between modules or “clumps of task interdependence.” After exploring the theory of
modularity and considering the arguments put forth by its critics, we will analyze data from a study of
patient care coordination to assess the degree to which the patterns of coordination observed in healthcare
conform to the expectations of modularity theory, and the degree to which these coordination patterns
adapt to meet the needs of more complex patients. We then conclude with implications for modularity
theory, for coordination theory, and for making modularity work in healthcare.
Modularity
Economic historian Richard Langlois (2002) argues that production has become increasingly
modularized over time, driven both by efficiency benefits and by the greater potential for innovation.
Modules are determined by the relative strength of task interdependencies in a work process, with the
strongest task interdependencies found within the modules and the weakest task interdependencies found
between the modules (von Hippel, 1990). Modularity allows participants in a work process to focus on
building strong ties with others who work at the same stage of the process and whose work is most highly
interdependent with their own, while allowing them to maintain relatively weak ties with those who work
at different stages of the process, whose work is less highly interdependent with their own (Baldwin and
Clark, 2000; 2004). Furthermore, modules enable innovation because they enable rapid reconfiguration
and recombination of elements of a process, therefore increasing the options available for changing any
given work process and even customizing it to the needs of a particular customer (ibid). Although there is
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relatively little need for coordination to occur directly between modules, due to their relative
independence, a crucial coordinating role is expected for participants who play systems integrator roles
(Sosa, et al, 2003).
The theory of modularity specifies three factors that facilitate or inhibit modularization. The first
factor, mentioned above, is the underlying pattern of task interdependencies in the work process. A work
process can be highly integrative with task interdependencies that do not cluster into modules, such that it
is not amenable to modularization. Alternatively, task interdependencies may evolve in such a way, or be
designed in such a way, that they cluster into relatively bundles of tasks (von Hippel, 1990). In a fully
modular work process, task interdependencies are reciprocal within modules, requiring high bandwidth
forms of coordination, and sequential or pooled between modules, requiring low bandwidth forms of
coordination.
The second factor is the existence of protocols or rules for coordinating the interface between
modules of the work process. Even when task interdependencies cluster into distinct modules,
modularization can be inhibited by the absence of relatively well-defined protocols or rules governing the
handoffs between the modules. According to coordination theory, protocols, routines and standard
operating procedures are relatively low-bandwidth coordinating mechanisms that facilitate the interface
between tasks when task interdependence is relatively weak (Daft and Lengel, 1986; Argote, 1982;
Gittell, 2002). Moreover, researchers have found that boundary objects can be designed to facilitate the
interface between tasks by providing a clear representation of the work in process (Arias and Fischer,
2000). Along the same lines, Baldwin and Clark (2000) argue that measurability of the output of each
module is critical for modularization because measurability enables contracts to be written that specify the
interfaces between modules.
The third factor that enables modularization is the existence of system integrators whose job is to
ensure that modules adhere to the protocols or rules that govern the process, and to weave together the
discrete modules into a coherent process (Sosa, et al, 2003). System integrators play the role of boundary
spanners or liaisons, people whose primary task according to coordination theory is to integrate the work
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of other people (Galbraith, 1995; Lievens, 2000). In a modular system, system integrators are responsible
for the enforcement and the adjustment of protocols, as well as the potential customization of protocols to
meet an unanticipated need.
As we will argue below, each of these three factors has come to characterize the healthcare
delivery system. But first, let us consider alternative perspectives regarding the desirability of
modularity.
Critique of Modularity
In direct contrast to Langlois’ argument regarding the benefits of modularization, Sabel and
Zeitlin (2004) argue that modularization undermines the ability of organizations to innovate and learn.
The new economy establishes strict limits to modularization, they argue, due to the “impossibility of
establishing standard design interfaces so comprehensive and stable that customers and suppliers can in
effect interact as if operating in spot markets for complex components or subassemblies without
jeopardizing their long-term survival” (ibid). The growing practice of co-design requires collaborators to
routinely “question and clarify their assumptions about their joint project” using methods such as
benchmarking, co-location of personnel, problem-solving teams and quality standards that enable the
parties to reconsider the partitioning of tasks across boundaries in a way that modularization does not
allow (ibid).
Following these authors and others who have explored the dynamics of inter-firm coordination
and learning (Powell, 1990; Grandori and Soda, 1995; Helper, MacDuffie and Sabel, 2003), we might
expect modularity to be less effective, the greater the complexity of the process. With a more complex
process, the boundaries between clusters of tasks arguably need to become more permeable to enable
feedback and learning to occur. This expectation is consistent with coordination theory, which argues
that complexity of various kinds increases the usefulness of high bandwidth forms of coordination
(Argote, 1982; Daft and Lengel, 1986) and relational forms of coordination in particular (Gittell, 2002).
We would therefore expect based on these arguments to find coordination patterns that are less modular,
and more integrative, when the work process is more complex.
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The theory of modularity as posed by Baldwin and Clark (2000) and Eppinger and colleagues
(1994) argues, to the contrary, that modularity is an effective response to complexity. Modular systems
enable participants to focus on building strong ties within modules, where task interdependencies are
greatest, and to rely on weak ties between modules, where task interdependencies are weakest.
Modularity, according to this theory, is more necessary and more powerful, the more complex the
process.
The Case of Patient Care
Looking to the characteristics of modularization described above, there is considerable evidence
that for better or worse, the healthcare delivery system is growing increasingly modularized. Distinct
stages of care – emergency care, intensive care, acute care, rehab care, home care and primary care – have
been formally defined based on acuity levels of the patient. The definition, formalization, and
enforcement of these levels of care have occurred through negotiations over time between clinicians who
deliver the care and payers, both governmental and private, who pay for it. Patients are expected to move
from one level of care to the next when they meet externally defined criteria for transfer; and if they are
not moved in a timely way, the payer can refuse to pay for the higher level of care because the patients
does not meet the criteria for receiving it. The stages of care are designed around the acuity levels of the
patient, but also are intended to take place at a point where the patient is considered sufficiently stable for
transfer. There is an expected clustering of task interdependencies by stage of care, with iterative,
reciprocal interdependencies within a given stage of care, and more clearly defined, sequential
interdependencies between stages of care, as illustrated above in Figure 1.
Secondly, in addition to well-defined stages of care, there are increasingly well-defined protocols
for the hand-off of patients from one stage of care to the next. Standardized protocols have evolved for
the transfer of information about patients, typically a three-page discharge form summarizing basic
patient information, and including evaluations of patient status from care providers who worked with the
patient at previous stages of care, and their recommendations for further treatment. For many diseases
and conditions, clinical pathways have been developed or are currently under development, providing a
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process map for governing the interfaces between stages of care (e.g. Wheelwright, 1992; Bohmer, 1998;
Gittell and Weiss, 2004).
Third and finally, system integrators of various kinds can be found in the healthcare delivery
system, though there are sometimes competing roles and a lack of clarity regarding who is or should be
playing this role. Managed care organizations employ case managers who oversee handoffs across stages,
primarily with the goal of ensuring that patients do not remain at a more costly stage of care longer than
their condition would justify. Each stage of care has its own case managers who are expected to manage
the handoff of the patient from the previous stage and to the subsequent stage of care. In addition, the
primary care physician has been placed in the role of system integrator in some healthcare delivery
systems, with the expectation that he or she will coordinate handoffs between stages of care (Stille, Jerant,
Bell, Meltzer and Elmore, 2005). Yet another potential system integrator is the informal caregiver – the
family member or friend of the patient. Recent research has documented the growing role of the informal
caregiver in today’s healthcare system (Kirk and Glendinning, 1998; Lyons and Zarit, 1999; Donelan,
Hill, et al. 2002). In addition to providing care when the patient returns home, informal caregivers play a
valuable role in coordinating the transfer of the patient between stages of care (Lusenhop, Weinberg,
Gittell and Kautz, 2005).
The presence of these three factors suggests that the healthcare system is becoming modularized
by stage of care. Based on these factors, we expect to find modular patterns of coordination between
healthcare providers who are working with a given patient, with relatively strong coordination ties within
modules, and relatively weak coordination ties between modules.
Hypothesis 1: The patient care process is expected to be modular by stage of care, with
relatively strong coordination ties within stages and relatively weak coordination ties
between stages.
Consistent with modularity theory, a subset of providers in the healthcare system is expected to
play a system integrator role. In particular, we expect that primary care physicians, case managers who
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work for managed care organizations, and the patient’s informal caregiver, will play the role of system
integrators, displaying relatively strong patterns of coordination with providers at each stage of care.
Hypothesis 2: Strong coordination ties are expected between system integrators (case
managers employed by managed care organizations, primary care physicians, and
informal caregivers) and providers at each stage of care.
Together, these strong ties within stages, weak ties between stages, and strong ties between system
integrators and each of the stages, characterize the patterns of coordination expected by the theory of
modularity.
How might these modular patterns of coordination be expected to vary, depending on the
complexity of the patient? Based on the argument that modularity is a response to complexity (Baldwin
and Clark, 2000), we would expect that the modularity of coordination patterns will be even more
pronounced for more complex patients than for less complex patients.
Hypothesis 3a: For more complex patients, coordination ties within stages and with
system integrators are stronger than those for less complex patients, while ties between
stages are the same or weaker.
According to the arguments above regarding the limitations of modularity (Sabel and Zeitlin, 2004), and
the need for higher bandwidth forms of coordination when complexity is high (Daft and Lengel, 1986),
we would expect more complex patients to be less amenable to modular forms of coordination. The
alternative to Hypothesis 3a, therefore, is:
Hypothesis 3b: For more complex patients, patterns of modularity are less pronounced,
with coordination ties between stages that are stronger than those for less complex
patients.
METHODS
In this study, we explore modularity in the coordination of care for knee arthroplasty patients who
received surgery at one particular focal hospital, a large well-regarded urban teaching hospital, who then
went on to receive rehab and home care from a variety of provider organizations. We surveyed patients
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about the quality of their care and the quality of their clinical outcomes, and surveyed providers about
their coordination with other providers involved in caring for the same patient. For analyzing patterns of
coordination and assessing the degree of modularity in those patterns, we used a matrix form of analysis
originally developed by Shepard (1981) and further developed by Eppinger and his colleagues (Eppinger,
et al, 1994; Sosa, et al, 2003). This form of analysis, known as Dependency Structure Matrix (DSM), has
been applied in recent years primarily to the design of products. The current paper is relatively unique in
applying the DSM methodology to a service delivery process. In addition, Sosa, et al (2003) argue that
the ideal use of the DSM methodology is to first model the dependencies between tasks, then model the
patterns of coordination, and assess the extent to which the two “fit.” However, given that the task
dependencies for patient care are relatively well known – reciprocal within stages of care, and sequential
between stages of care – we have skipped that step and instead focus our efforts on modeling the patterns
of coordination. All data, measures and analytical techniques are further described below.
Data Collected for this Study
Patients were eligible for inclusion in this study if they were admitted to the focal hospital for
primary, unilateral total knee arthroplasty with a diagnosis of osteoarthritis between November 2003 and
May 2004. All eligible patients were sent an initial survey prior to their surgery, asking them to
participate and to answer questions regarding their pre-operative status. We received 222 responses to 357
surveys that were sent to eligible patients, for an enrollment rate of 62 percent. Hospital administrative
records were obtained for each patient who enrolled in this study, to extract additional data not captured
through the surveys.
We surveyed a sample of the patient’s care providers at six weeks after discharge. For each
patient, we identified and surveyed a subset of the different types of care providers responsible for the
patient at the acute, rehab, and home stages of care. See Table 1 for the types of providers who were
surveyed, due to expected ease of access, and for types of providers they were surveyed about. Providers
were surveyed about coordination with each other provider who was assigned to the same patient, so we
have information about coordination even with those providers who were not surveyed, and about
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coordination with those who were surveyed but failed to respond. We surveyed a total of 1389 providers
who were assigned to the 222 patients enrolled in our study, on average 6 providers per patient. A total of
519 providers responded, for a response rate of 37 percent, or on average, about 2 providers per patient.
[Insert Table 1 about here.]
We supplemented these provider surveys with 42 structured interviews – 10 with acute care
providers, 8 with rehab and home care providers, 12 with informal care givers (friend or family of the
patient) and 12 with patients themselves.
Relational Coordination
The instrument used to test relational coordination was developed and tested in Gittell, et al
(2000), based on a survey that was initially used to measure relational coordination among employees in
the flight departure process (Gittell, 2005). Each respondent was asked seven questions about his or her
coordination with providers who cared for that patient in each of the three stages of care: How frequently
did you communicate with each of these people about the status of Patient X?; Did these people
communicate with you in a timely way about the status of Patient X?; Did these people communicate with
you accurately about the status of Patient X?; When problems occurred regarding the care of Patient X,
did these people blame others or work to solve the problem?; To what extent did these people share your
goals for the care of Patient X?; How much did these people know about your work with Patient X?; and
How much did these people respect your work with Patient X? These questions have been used to assess
overall coordination within a particular healthcare setting, and patient-specific coordination in a particular
healthcare setting, but have never before been used to assess patient-specific coordination across multiple
settings. Responses were measured on a five-point Likert scale.
To measure relational coordination within stages, a set of seven scores (frequent, timely, accurate,
problem-solving communication, shared goals, shared knowledge, mutual respect) was computed for each
respondent based on his or her responses regarding the providers who worked at the same stage of work.
The seven scores were combined into a single index, with a Cronbach’s alpha of 0.83, called relational
coordination within stages, measured for each individual respondent. To measure the strength of ties
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between stages, a similar set of seven scores was computed for each respondent by averaging his or her
responses regarding providers in all stages of care other than his or her own. These seven scores were
then combined into a single index, with a Cronbach’s alpha of 0.80, called strength of ties between stages,
measured for each individual respondent. Finally, to measure the strength of ties with system integrators,
a similar set of seven scores was computed for each respondent by averaging his or her responses
regarding each of the system integrators. These seven scores were then combined into a single index, with
a Cronbach’s alpha of 0.85, called relational coordination with system integrators, measured for each
individual respondent. Each of these indices easily exceeds the minimum alpha of 0.70 traditionally used
as a standard for index validity (Nunnally, 1978).
Table 2 shows the descriptive data for our study, including means, standard deviations and a
correlation matrix.
[Insert Table 2 about here.]
Patient Complexity
There are multiple patient characteristics that can increase the complexity of care, even for a
straightforward procedure such as knee arthroplasty. Older patients tend to be more complex, so we
gathered patient age from hospital records. Patients with low levels of overall health also tend to be more
complex, so we assessed overall health in the patient survey using the overall health measure from the SF36 (Stewart, Hayes and Ware, 1988). These measures of complexity were considered separately for their
potential impact on the modularity of care.
Patient Outcomes
We used a single item measure of patient satisfaction based on the question: Overall, how would
you rate your care in the past six weeks?, and a single item measure of willingness to recommend based
on the question: Would you recommend these healthcare providers to your family and friends?, both
measured at six-weeks post-surgery. We also measured the quality of clinical outcomes. The two key
clinical outcomes expected from joint replacement surgery are reduced joint pain and increased joint
mobility. Percent reduction in joint pain was constructed by comparing post-operative reports on joint
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pain to pre-operative reports, using patient surveys conducted prior to surgery and twelve weeks postsurgery. Percent improvement in joint functioning was constructed similarly. The survey questions
included five items relating to pain and seventeen items relating to mobility from the WOMAC, a
validated self-administered osteoarthritis instrument (Bellamy, et al, 1988). The survey questions asked
about the amount of pain and degree of difficulty with mobility (five potential responses from none to
severe) experienced in the past 48 hours during common activities. To minimize missing values,
responses were included for all patients who completed at least eighty percent of the items in each of the
indices. The mean of the non-missing values for each item was assigned to missing values for that item.
Analyses
Using the technique originally developed by Shepard (1981) and further refined by Eppinger and
his colleagues (Eppinger, et al, 1994; Sosa, et al, 2003), we formed a matrix to find the patterns of ties
within and across stages of the work process. We formed a matrix with the three different stages of care –
acute, rehab and home – down one side, and the same stages of care across the top. We added a final row
and column for those who were expected to play the system integrator role. Because we surveyed 8
provider types about their coordination with 14 provider types, our matrix is asymmetric. Observations
about the 6 provider types that were not surveyed are reported only from the perspective of others, not
from their own perspective. Still, all coordination ties in the matrix are measured from the perspective of
at least one of the two providers in each relationship. In each cell of the matrix is the mean level of
relational coordination on a 5-point scale, reported by the provider type along the left axis with respect to
the provider type along the top axis, regarding the particular patient about whose coordination the
provider was being surveyed. Once the matrix was complete, with all available survey responses reflected
on it, we observed the patterns of relational coordination.
According to our first hypothesis, we expected the three stages of care to display relatively high
levels of relational coordination internally, and relatively low levels of relational coordination with each
other. Hypothesis 1 was tested by comparing the mean level of relational coordination within modules to
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the mean level of relational coordination between modules, and conducting a t-test for significance of
differences between the means.
According to our second hypothesis, we expected that the patient’s primary care physician, the
managed care case manager, and the informal care provider would display relatively integrative patterns
of coordination with the patient’s providers at each stage of care. Our second hypothesis is supported if
the relational coordination found between the system integrators and the modules is significantly higher
than the relational coordination found between the modules themselves. Hypothesis 2 was tested by
comparing the mean level of relational coordination between system integrators and each of the modules
to the mean level of relational coordination between the modules, and conducting a t-test for significance
of differences between the means.
To explore our third hypothesis regarding the impact of patient complexity on modularity, we
divided the patient sample into higher and lower complexity based first on age, then on overall health.
Then, we compared the mean levels of relational coordination within modules, between modules, and
with system integrators. Hypothesis 3a is supported if relational coordination within modules and with
system integrators is stronger for more complex patients, while relational coordination between modules
is weaker or does not change. The alternative Hypothesis 3b is supported if relational coordination
between modules is stronger for more complex patients, while relational coordination within modules and
with system integrators is weaker or does not change. We tested both hypotheses by comparing the mean
levels of relational coordination for more complex patients, to mean levels of relational coordination for
less complex patients, and conducting t-tests for significance of differences between the means.
Patient outcomes models require large numbers of observations to find significant effects, given
unmeasured sources of variation across patients. Because the patients who responded to our surveys often
did not have providers who responded and vice versa, our matched sample of patients and providers is not
large enough to test for significant effects of relational coordination on patient outcomes. However,
previous studies have shown significant positive effects of relational coordination on quality and
efficiency outcomes in airlines (Gittell, 2001) and hospitals (Gittell, et al, 2000). For this study, we
14
simply compare mean performance outcomes for more complex patients, to mean performance outcomes
for less complex patients, and conduct t-tests for significance of differences between the means.
FINDINGS
Modular Patterns of Coordination
Table 3 shows a "dependency structure matrix," using coordination data from the process of
patient care for knee replacement patients. Each cell of this matrix includes the mean level of relational
coordination reported by the provider type listed in the left-hand column, with the provider type listed in
the top row. The number of provider survey responses included in that mean is shown in each cell, in
parentheses. Only responses about the providers a patient was known to have were included.
Let us first consider an example of “within stage” or “within module” coordination. In the
second row of the table, first column, we see that 80 acute physical therapists responded about their
coordination of care with the acute physician regarding a patient in our study. The mean level of
relational coordination that they reported with the acute physician was 3.4 on a 5-point scale. Both the
acute physical therapist and the acute physician work at the acute stage of care, so this is an example of
“within module” coordination.
Let us now consider an example of “between stage” or “between module” coordination. Looking
further along that same row at the fourth column, we see that 39 acute physical therapists responded about
their coordination of care with the rehab physician regarding a patient in our study. The difference
between the 80 responses and the 39 responses is due to the fact that about half of the patients in our
study went from acute to rehab before receiving home care while the other half went directly from acute
care to home care. We see that the mean level of relational coordination that acute physical therapists
reported with the rehab physician was 1.1 on a 5-point scale. The acute physical therapist works at the
acute stage of care, while the rehab physician works at the rehab stage of care, so this is an example of
“between module” coordination.
[Insert Table 3 about here.]
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Looking at the entire matrix, we can see that all of the shaded cells on the diagonal represent
within module coordination, all of the unshaded cells represent between module coordination, and all of
the shaded cells along the bottom and far right column represent coordination by system integrators.
According to Hypothesis 1, we should expect the shaded cells on the diagonal to have higher levels of
relational coordination than the unshaded cells. Consistent with this hypothesis, the matrix shows clear
evidence of modular coordination patterns. We see that all of the shaded cells (within module) have
levels of relational coordination that are greater than 3 on a 5-point scale, while nearly all of the unshaded
cells (between module) have levels of relational coordination that are less than 3 on a 5-point scale.
There is one exception – physical therapists at the home stage of care report unexpectedly high levels of
relational coordination with the physician at the acute stage of care.
Table 4 compares more systematically the average strength of coordination within modules and
the average strength of coordination between modules. Mean relational coordination within modules is
4.0 on a 5-point scale, while mean relational coordination between modules is 2.3 on a 5-point scale. This
difference is significant at the 0.001 level.
[Insert Table 4 about here.]
Modularity theory also leads us to expect system integrators to have relatively strong ties with
each module in the process. According to Hypothesis 2, we should expect the shaded cells along the
bottom and far right hand column of Table 3 to have higher levels of relational coordination than the
unshaded cells. This expectation is only partially met. We see that only the informal caregiver has strong
ties (greater than 3 on a 5-point scale) with at least one provider in each module. The primary care
physician has no strong ties with anybody, and the managed care case manager has relatively strong ties
only with the acute case manager and the rehab case manager.
Table 4 compares more systematically the average strength of coordination between modules and
the average strength of coordination with system integrators. Mean relational coordination between
modules is 2.3 on a 5-point scale, while mean relational coordination with system integrators is 2.6 on a
5-point scale. This difference is significant at the 0.001 level. Hypothesis 2 therefore receives support in
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these data. However, as we saw from the disaggregated data on Table 3, the system integrator role is not
played consistently by all of the parties who were expected to play it. The unanticipated finding is that
the informal caregiver plays the lion’s share of this role. Implications of this finding will be explored
below.
Excerpts from interview data help flesh out and illustrate these quantitative results. One interview
was selected from each of the three stages of care – acute, rehab and home. Excerpts were then selected
from the three interviews to illustrate the different types of coordination: coordination internal to that
stage of care, coordination with other stages of care, and coordination with system integrators. These
results are summarized in Table 5. Several observations from these interviews are worth noting. First,
we see evidence of a highly formalized set of protocols and forms for handing off patient information
from one stage of care to the next, with little conversation or discussion required, from the perspective of
the interviewees. Second, we see evidence of frequent conversations and discussions regarding the care
of a particular patient among providers who work within a particular stage of care, and the stated belief
that this is good and necessary. Third, we see a mixed picture of the role played by system integrators.
The informal caregiver is seen as playing an essential role, while the primary care physician’s
coordination role is seen either as unimportant or as important but unreliable.
[Insert Table 5 about here.]
Effect of Complexity on Modular Patterns of Coordination
Next, let us see what happens to these modular patterns of coordination when we consider
complexity of the patient. According to the theory of modularity, patterns of coordination should be more
modular for more complex patients (Hypothesis 3a), while competing theories argue that modularity
becomes less effective as complexity increases (Hypothesis 3b).
The data presented on Table 6 show
clearly that modularization increases rather than decreases with complexity of the patient. Relational
coordination for older patients is significantly higher within modules (p=0.015) and with two of the
system integrators (p=0.034 and p=0.001), while there is no significant increase in relational coordination
between modules (p=0.430). Relational coordination for sicker patients is marginally higher within
17
modules (p=0.067) and with two of the system integrators (p=0.011 and p=0.004), while again there is no
significant increase in relational coordination between modules (p=0.181).
[Insert Table 6 about here.]
In the language of design structure matrices, between module coordination does not become more
extensive for more complex patients – rather the response to complexity is to increase coordination within
the modules and to increase coordination between the modules and the system integrators. Interestingly,
even though two of the system integrators (primary care physicians and managed care case managers)
show limited evidence of strong coordination ties with the modules, they do show an increase in
coordination for more complex patients. Taken together, these results suggest that patterns of
coordination in healthcare are distinctly modular, and that this modularity is even more pronounced for
more complex patients.
There is no direct evidence about the benefits or drawbacks of this increased modularity for more
complex patients. On the positive side, we observe that outcomes for patients in this study were quite
favorable overall. Patients in this study reported an average 67 percent improvement in joint pain levels
and 81 percent improvement in joint mobility levels by 12 weeks post-surgery, relative to pre-surgery.
Furthermore, 83 percent of patients rated their care as excellent or very good, while 76 percent of patients
reported that they were highly likely to recommend their care providers to others, and another 21 percent
of patients were somewhat likely to recommend. On the negative side, however, we observe that more
complex patients experienced somewhat lower outcomes than less complex patients. As shown on Table
7, older patients were marginally less satisfied with their care (p=0.061) and less likely to recommend
their care providers to others (p=0.013) but their clinical outcomes were the same as for younger patients.
Sicker patients (those who started out with lower overall levels of health) were significantly less satisfied
with their care (p=0.024), marginally less likely to recommend it to others (p=0.071) and achieved
marginally smaller improvements in joint pain (p=0.062). We do not know however if the satisfaction
and clinical outcomes achieved by more complex patients would have been comparable to those for less
complex patients if their care were delivered in a less modular way.
18
[Insert Table 7 about here.]
DISCUSSION
We conclude that modularization does exist in the current healthcare delivery system, with
patterns that are quite similar to those previously found in other industries. One aspect of modularization
is high levels of relational coordination within modules. By allowing relational forms of coordination to
be relatively weak with providers who work at other stages of care, providers can focus on coordinating
patient care with others at their own stage of care. The expectation is an overall positive effect on
outcomes, if indeed the modules have been formed with the strongest task interdependencies within the
modules, and the weakest task interdependencies between the modules.
We also have seen that system integrators do indeed play a role in this industry, as they are
expected to do under conditions of modularity (Sosa, et al, 2003). However, the system integrator role is
played quite unevenly by the different parties. We see that neither the managed care case manager nor
the primary care physicians play an extensive system integrator role. The managed care case manager has
strong ties only with the case manager at the acute stage of care, and ties that are fairly strong with case
managers at the rehab stage of care. The only ties for the primary care physician that approach our
definition of strong ties (greater than 3 on a 5-point scale) are with the physician at the acute stage of care.
Our results do show that both the primary care physician and the managed care case manager increase
their role in coordination when the patient in question is older or sicker. However, the role still remains
very weak (1.9 for the primary care physician and 2 to 2.3 for the managed care case manager, on a 5point scale). These findings call for investigation into the roles of the managed care case manager and in
particular, into the primary care physician’s intended role as gatekeeper and coordinator of care. The
primary care physician is typically not compensated for care coordination, and anecdotal evidence
suggests that they neglect it due to intense pressures to see more patients in less time. Some health plans,
however, such as Harvard Vanguard, have begun to compensate primary care physicians for their role in
coordination and to include coordination of care as one of their performance metrics.
19
We find that the system integrator role is played most extensively by the informal caregiver who
has strong ties (greater than 3 on a 5-point scale) with each stage of care. This is an important finding
because while many studies have already documented the amount and kinds of care provided by informal
caregivers (e.g., Lyons and Zarit, 1999; Donelan, et al, 2002), this study documents the significant role
that they are playing in the coordination of care relative to paid providers. Given the lack of systematic
training for informal caregivers and the highly unequal resources that different individuals can bring to
this role, this finding is a cause for concern. Some families have highly educated members, while others
have none, and many informal caregivers are themselves frail and elderly. These results suggest the need
to consider the coordination role that is played by informal caregivers in a modular healthcare system and
its impact on both caregiver and patient well being, and to support this role with more active involvement
by paid caregivers including potentially the primary care physician.
Finally, we have seen that patients who are more complex receive care that is more, rather than
less, modular – that is, relational forms of coordination become stronger within modules and with system
integrators, while relational coordination between modules remains the same. However, we do not know
if this increased modularization for more complex patients is positive or negative. Indeed, the primary
limitation of this study is the lack of sufficient sample size to test the performance effects of modularity.
Numerous studies have found evidence to support the theoretical proposition that relational forms of
coordination improve the quality and efficiency of patient care (e.g., Baggs, et al, 1992; Shortell, et al,
1994; Young, et al, 2000; Gittell, et al, 2000) and moreover have shown that these forms of coordination
become more important, the more complex and uncertain the inputs to the work process (Argote, 1982;
Gittell, 2002). However, these findings have not been replicated for coordination between distinct stages
of care. Although we have documented the modularity of coordination in healthcare, the greater
modularity for more complex patients, and the marginally poorer outcomes for those patients, we cannot
answer definitively the question of whether modularity is beneficial in this setting, and whether the
adaptations to the needs of more complex patients that we observed were adequate.
20
Modularity theorists suggest that modularity is an effective response to complexity, because it
helps to reduce complexity by simplifying the interfaces between modules and enables those who are
working within each module to better focus their efforts on the critical task interdependencies within.
Coordination theorists and critics of modularity have argued instead that complexity calls for more
integrative patterns of coordination, rather than for simplification of the interfaces. Our results suggest a
potential reconciliation between these arguments, via the role played by system integrators. Modularity
theorists have noted the role that system integrators play in modular systems (Sosa, et al, 2003); arguably,
this role should increase as complexity increases. An increased role for system integrators could be a way
to provide the higher bandwidth needed for effective coordination of complex work processes, according
to coordination theorists (Daft and Lengel, 1986), and to provide the collaborative interface that is
required for learning, according to the critics of modularity (Sabel and Zeitlin, 2004). One theoretical
contribution of this paper, therefore, is a potential reconciliation between the argument that modularity is
an effective response to complexity and the argument that the effectiveness of modularity is limited by
complexity. Complexity can potentially be handled without increasing direct interfaces between the
modules, so long as coordination by system integrators increases sufficiently to meet the need for higher
bandwidth.
More generally, this study extends the literature on patterns of work group interaction by helping
us to answer the question: where should ties be relatively strong and where can they be relatively weak?
As we noted at the start of this paper, if it is not feasible to build strong ties with every person who is
engaged in the same work process (for example, working with the same patient), the question of how to
best focus one’s limited time and attention on developing strong ties where they really matter becomes
relevant. The results presented here suggest that modularized work processes may provide an answer to
this question, so long as system integrators are equipped and encouraged to play an expanded role as
complexity increases.
21
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25
Figure 1:
Reciprocal Task Interdependencies Within Modules,
and Sequential Task Interdependencies Between Modules
Module A
Module B
Module C
Acute
Rehab
Home
Function x
Function w
Function x
Function y
Function w
Function x
Function y
Function z
Function z
Function w
Function y
Function z
Table 1:
Providers Who Were Surveyed and About Coordination with Whom
Providers Surveyed
Stage of Care
Acute
Provider Type
Physical Therapist
Case Manager
Rehab
Physical Therapist
Case Manager
Home
Nurse
Physical Therapist
System Integrator
Primary Care Physician
Informal Caregiver
Each Was Surveyed About Coordination With
Stage of Care
Provider Type
Acute
Physician
Physical Therapist
Case Manager
Physician
Rehab
Nurse
Physical Therapist
Case Manager
Home
Nurse
Physical Therapist
Case Manager
System Integrator
Physician
Informal Caregiver
Managed Care Case Mgr
Table 2: Correlation Matrix
1. RC within
stages of care
2. RC between
stages of care
3. RC with system
integrators
4. Patient age
(1=high, 0=low)
5. Patient health
(1=high, 0=low)
6. Satisfaction
with care
7. Willingness to
recommend
8. Improvement in
joint pain
9. Improvement in
functioning
Mean
(SD)
4.0
(1.01)
2.3
(.98)
2.6
(1.14)
.20
(.40)
.40
(.49)
4.3
(.83)
4.5
(1.03)
.67
(.78)
.81
(1.24)
Obs
1.
334
--
334
.146
(.007)
.258
(.000)
.131
(.015)
-.099
(.067)
-.055
(.355)
-.058
(.355)
.001
(.984)
-.108
(.087)
336
201
197
161
154
154
147
2.
3.
4.
5.
6.
7.
8.
9.
-.546
(.000)
.043
(.429)
-.073
(.181)
-.069
(.257)
-.034
(.587)
-.047
(.468)
-.024
(.702)
-.075
(.162)
-.052
(.340)
-.048
(.425)
-.012
(.851)
-.070
(.283)
.004
(.953)
--.071
(.110)
-.121
(.013)
-.141
(.005)
-.109
(.035)
-.089
(.080)
-.213
(.000)
.166
(.001)
.186
(.000)
.144
(.005)
-.904
(.000)
-.074
(.174)
-.063
(.240)
--.090
(.107)
-.064
(.240)
-.679
(.000)
--
Table 3: Modular Patterns of Relational Coordination1
Physician
Acute
Rehab
Home
System
Integrator
1
Acute
Case
manager
Physical
therapist
Physician
Rehab
Nurse
Case
manager
Physical
therapist
Home
Nurse
Physical
therapist
System Integrator
Primary Informal Managed
care
care
care case
physician
giver
manager
Case
manager
3.5
(35)
---
4.3
(34)
1.1
(21)
2.2
(23)
2.6
(23)
2.3
(23)
2.5
(29)
2.2
(33)
2.5
(34)
3.6
(34)
3.9
(29)
Physical
therapist
3.4
(80)
3.8
(75)
4.1
(45)
1.1
(39)
1.2
(39)
1.1
(38)
1.4
(39)
1.1
(67)
1.3
(65)
1.1
(73)
1.9
(72)
1.2
(37)
Case
manager
2.1
(36)
2.2
(35)
2.0
(35)
3.6
(35)
4.3
(35)
4.5
(41)
4.4
(35)
2.3
(28)
1.9
(29)
1.8
(33)
3.3
(34)
2.7
(21)
Physical
therapist
2.3
(41)
1.7
(41)
2.1
(41)
3.8
(41)
4.2
(41)
4.2
(41)
4.6
(31)
1.5
(29)
1.8
(33)
1.4
(39)
3.0
(39)
1.5
(21)
Nurse
2.8
(68)
1.5
(63)
1.3
(64)
1.2
(32)
1.2
(32)
1.2
(32)
1.2
(31)
4.5
(37)
4.0
(67)
2.1
(65)
3.4
(66)
1.6
(33)
Physical
therapist
3.5
(89)
1.8
(81)
1.9
(85)
1.6
(44)
1.6
(44)
1.5
(44)
1.9
(43)
3.8
(84)
4.3
(25)
2.0
(87)
3.3
(87)
2.1
(40)
Primary
care
physician
Informal
care
giver
2.8
(50)
1.6
(46)
1.3
(49)
1.4
(24)
1.7
(24)
1.8
(20)
1.7
(21)
1.5
(44)
1.4
(45)
---
1.9
(45)
1.3
(30)
3.3
(120)
1.8
(111)
2.4
(114)
2.2
(60)
3.0
(60)
2.1
(59)
2.8
(60)
3.3
(100)
3.6
(103)
1.9
(114)
---
1.2
(56)
Shaded areas indicate where strong ties were expected based on theory of modularity. Strength of ties is measured on a five point scale. Strong ties are defined
as ties > 3, and are indicated in bold and underlined. Row labels indicate the types of care providers who were surveyed, and column headings indicate the types
of care providers they were surveyed about. Number of respondents shown in parentheses in each cell.
Table 4:
Differences in Relational Coordination
Within Stages, Between Stages and With System Integrators
Within
Stages of
Care
Between
Stages of
Care
With
System
Integrators
Relational
Coordination
4.0
(1.01)
2.3
(.98)
2.6
(1.14)
Observations
334
334
336
Within
Stages
> Between
Stages
With System
Integrators
> Between
Stages
(p-value)
(p-value)
0.000
0.000
Table 5:
What Care Providers Say About Coordination Within Stage, Between Stage and with System Integrators
Acute
Rehab
Coordination Between Stages
Q: What type of information do you have about
the patients when they come to you?
A: We have a past medical history, surgical
history. We can look up and see what type of
surgery they had. Usually the notes in the chart
will say any complications or any differences in
the care of a joint replacement. So we have access
to pretty much all the information that’s available.
Q: Is there information that maybe you don’t need
but that you would like to have about the patients
when they come to you?
A: No, I think it’s pretty thorough.
Q: And there are certain providers from whom
you don’t receive the information that you need.
A: Nope.
Q: And what happens when they leave the
hospital?
A: We do a discharge evaluation and update the
notes so that the outside facility will be able to see
where the patient is presently and any instructions
they have regarding the exercises or range of
motion or anything that may be different or you
know, per the normal protocol.
Q: Do you have any communication with the
facilities other than writing these notes?
A: No. They can call us, but we don’t usually talk
to them at all.
Q: So how does the handoff occur? What kind of
information do you get?
A: Basically, we get a screening form. That
screening form would have the patient’s basic
information. What their general status is. …We
get the date of the nurse’s screen, the date of the
Coordination Within Stages
Q: When and how does the communication
usually take place [here]?
A: We can usually textpage the resident to leave a
note in the chart. With the care coordinators we
usually talk to them directly or they can decide by
looking at our progress notes.
Q: Do you feel that you have enough opportunity
to share information with other care providers?
A: Yeah, definitely. …I feel like everyone’s
helpful and everyone’s usually on the same
page….
Q: And would you say that the physical therapists
work well together as a team?
A: Oh, definitely.
Q: What about with the nurses?
A: Yeah, I think there’s good communication.
They usually give us a call if something that we
need to do, or come check out the patient or make
sure that they are on our list to be seen for the day,
so I think that’s good.
Q: So, would you say that when things are kind of
standard, you don’t really need to talk to other
members of the team as much?
A: Ummmmh, we really still communicate with
them, like the doctors still need to let us know that
medically if everything’s going all right, and we
still check with the care coordinator. Make sure
that this is still the plan, that what they are having
and working on as well.
Q: Anyone that you need to interact with about
the patient while they are [here] besides the family
member, or the patient themselves?
A: We have meetings continually. We have an
interdisciplinary team meeting on every patient on
Wednesdays, which includes PT, OT, social work,
Coordination With System Integrators
[Acute providers not questioned about their
coordination with system integrators due to an
oversight in the design of interview protocol.]
Q: Anything that you need to, any interaction you
need to have with the primary care physician?
A: Because we use the surgeons’ precautions, we
like to speak directly to the surgeon. The PCP we
don’t have any contact with, because when they
are here they are under the care of the house
Home
admission. Who is screening them. The name,
date of birth, address, city, zip, phone, marital
status, next of kin. Kind of the basic socioeconomic information. Medicare, Harvard, what
their insurance is and what their back up insurance
is. Then we get like a primary diagnosis,
secondary diagnosis, past medical history, and
basic current history. This is the chart review that
the screening nurse would do…Medication, do
they have an IV, what’s going into it? The things
that we would kind of basically need to know.
…Have they been seen by therapist, what would
they estimate they will tolerate for therapy
.…What I’ve found is these screens are good basic
information to know what you are walking into.
The other thing that we get when the patient
arrives here is their discharge summary from the
hospital.
Q: Is there anything that consistently doesn’t go
well, things that are typically missing, or difficult
to get, …if you have to follow up?
A: It’s rare, I mean, I rarely have to call...
Q: When you are passing the patient on to the next
stage of care, what do you have to send with them?
A: I send a history… You know that basic 3-pager
has social work on top, and rehab on the bottom
… I always include past medical history, social
history, weight-bearing status, current functional
status, including like independent [sit leg] versus
not, have they achieved what the range of motion
is, if they haven’t, if a home CPM has been
ordered. What equipment they have. You know,
blah, blah, blah.
Q: What kind of information do you get from the
hospital that’s discharging them?
A: Most of our patients come from the rehab
[hospital] after a knee replacement, and what I will
get is a referral, a three-page referral from the
discharging agency, which is a page 1 that the
physician has signed with the orders, the
nursing, nurse managers, recreation. [We have a]
daily morning meeting. …There is a review of all
the patients.
Q: Just a quick, is everyone moving along?
A: Yup, it’s like a round. We have rounds. So we
have managers go to that. We have PTs and OTs
in the same office, and we continually chat daily.
We [especially] talk about the patients on Tuesday.
We talk about them all day long. We go through
everything on Tuesday before the meeting and
that’s mainly where we talk about the rehab
frequency, what they need, equipment, so we’re on
the same page.
physician, who is a general practitioner. The
general practitioner here would write the orders,
change the medication, and do all of those pieces.
Q: Right, now how about the family member?
A: Always actively involved. I mean, sometimes
if there is trouble, we’re getting someone in here.
We have a very, not really a formal environment
here. We like to foster more of a “hey, I’m the
therapist. How are you doing? so and so.” I
always tell the patient what the deal is, what’s
going on, what to expect. If they have any
questions, we encourage family members to come
to rehab.
Q: Right, because they are going to be supporting
this when they get home.
A: Unless they have a [special] situation that
makes it not beneficial for the patient. … It’s just
a judgment call. Sometimes people get in the way.
(both laugh). We encourage it. We try things out
and we try and encourage the family members to
come on down, you know, we are a subacute floor,
the rehab department is on the same floor. So it’s
“come on down.”
Q: When both you and a physical therapist are
assigned to a patient in their home, is there any
kind of communication between you?
A: I always communicate with my physical
therapists. Even if it’s just …, obviously if I have
a concern, but even if it’s just “things are going
well. This is my plan.” And I do that with the
Q: Now, in terms of working with information or
communication with the patient and their family?
A: Most total knees are elderly, older adults and
there may be other issues going on. There may be
other, you know, congestive heart failure, maybe
another diagnosis…Most people will have, if not a
spouse, a son or a daughter who may be involved.
32
medications, the physical therapy orders and then
the page 2 would be a nursing, kind of an idea of
where the patient’s been, what they’ve been
through, how they are doing, their ADLs
[activities of daily living] and they will sometimes
incorporate the hospital forms into that because
this is the rehab now, giving me this information,
and then the page three will be the PT/OT part of
it. So I have that and then there’s a liaison in the
rehab that works for our agency who will also give
me a page or two of information that they would
find helpful. So, they’ve got about five pages of
information. And if they come from the hospital,
we also would get a discharge summary which is
much more in detail and sometimes that comes
through the rehab, but not always.
Q: So the discharge summary, meaning when
they first left the acute care hospital, that was put
together by the surgeon?
A: Right.
Q: Do you often have to call back to anyone,
either at the rehab or the acute to clarify
information or to update them on problems that
have occurred?
A: [Sometimes] we do. For instance if I had a
patient who came out from rehab on coumadin,
and the blood level was a little high, a little low, I
might call to find out what the most recent
coumadin dose was, or how about the INR’s that’s
the blood results have been running in the facility,
that sometimes is not clear. That’s not common,
luckily I don’t spend a lot of time doing that.
physical therapist very consistently.
Q: So you each will develop a plan of care and
then share it with each other typically?
A: Yes, and sometimes one of us will be leaving,
the other one will be showing up, so there’s that
communication as well. But I’ve always had
phone contact with my physical therapists and it
works both ways.
Q: And, it’s the same physical therapist assigned
to a patient throughout that period or does that
vary, because that seems like that would
complicate things?
A: It is always the same physical therapist and I
am actually fortunate that the territory I cover I
have the same one, sometimes two therapists so I
work with them …. We work together very well.
And are always in contact with each other. We
have e-mail as well through our laptops that we
utilize. And there’s definitely communication
with our plan. Discharge plan is also another,
Eileen has a scheduled visit for a day I don’t and I
have a concern, I will ask her to please check on
this or that ….
So I would maybe try to plan a visit with
somebody, like a main support, if the person lived
alone. Like maybe have the son or daughter be
available for the first visit. Where you just want to
utilize the pill boxes, pre-filled with the
medication in it.
Q: Now, how about the PCP?
A: You know, it’s very individual and it would be
ideal if a PCP got some sort of, you know, this is
what the patient has been through hospital course,
so they have, because there are sometimes when I
would have to go to their PCP and if they haven’t
seen the patient, some people only see their PCP
once a year. So that is sometimes a problem, and
what I would do in that case is I usually encourage
my patients at some point to make a follow up
appointment so that the PCP will have an idea of
what’s going on. ..The doctors in the hospital or
the rehab may change the patient’s medication and
I find a lot of patients are like “My doctor put me
on this” and then the doctor in the rehab doesn’t
even know them, puts them on something else
because of an incident… I will always tell a
patient in that position to touch base with your
primary care physician... So I would say, who
regulates your cardiac medication and then at that
point, I would say, you need to touch base with
them and set up an appointment because now,
you’re home... They may change their insulin dose
while they are in. …So in that case I would
always say, you know, make a phone call, and
some people are more in close touch with their
PCP than others.
33
Table 6:
Differences in Modularity by Complexity of Patient
Relational Coordination
Within
Stages of
Care
Between
Stages of
Care
With System
Integrators
With System
Integrator 1
(Informal
Care Giver)
With System
Integrator 2
(Primary
Care
Physician)
With System
Integrator 3
(Managed
Care Case
Manager)
Patient age < 75
3.9
(1.05)
2.2
(.99)
2.6
(1.15)
2.8
(1.38)
1.8
(1.11)
1.7
(1.21)
Patient age > 75
4.2
(.79)
2.3
(.94)
2.8
(1.11)
3.2
(1.33)
1.9
(1.07)
2.3
(1.48)
P-value
0.015
0.430
0.162
0.034
0.235
0.001
343
343
346
378
451
267
Patient health = good or excellent
3.9
(1.06)
2.2
(.96)
2.6
(1.15)
2.8
(1.40)
1.6
(1.01)
1.5
(1.06)
Patient health = fair or poor
4.2
(.72)
2.3
(.99)
2.7
(1.14)
3.0
(1.37)
1.9
(1.15)
2.0
(1.40)
P-value
0.067
0.181
0.340
0.131
0.011
0.004
343
338
341
373
444
262
Observations
Observations
34
Table 7:
Differences in Patient Outcomes by Complexity of Patient
Satisfaction with
Care
Willingness to
Recommend
4.6
(.91)
Percent
Improvement in
Pain
.70
(.78)
Percent
Improvement in
Functioning
.83
(1.25)
Patient age < 75
4.4
(.82)
Patient age > 75
4.1
(.88)
4.0
(1.41)
.58
(.78)
.71
(1.20)
P-value
0.061
0.013
0.437
0.618
Observations
161
154
147
152
Patient health = good or excellent
4.5
(.80)
4.6
(1.00)
.81
(.66)
.98
(1.64)
Patient health = fair or poor
4.2
(.84)
4.3
(1.05)
.57
(.91)
.68
(.86)
P-value
0.024
0.071
0.062
0.148
158
155
146
150
Observations
35