Continuity in Gatekeepers:

Quantifying the Impact of Care Fragmentation

Abstract

Operational systems increasingly rely upon specialized experts who can provide high-quality service. However, these experts, by definition, only address one part of an overallproblem and so individuals are needed to coordinate the overall service provision. In manyservices, this coordination responsibility may be shared across multiple parties serving in therole of a gatekeeper. In this paper, we explore the operational consequences of continuity orfragmentation in the role of a gatekeeper. We use a unique and comprehensive dataset fromthe Veterans Health Administration, the largest integrated healthcare delivery system in theUnited States to analyze over half a million patients over six years that suffer from a chronicdisease, diabetes, whose successful management requires well-coordinated care. We examinehow the continuity of care with their primary care physicians (PCPs), the gatekeeper inthis environment, affects patients’ health outcomes. Using an instrumental variables ap-proach, we find that continuity of care is related to improvements in three important healthoutcomes: inpatient visits, length of stay, and readmission rate. In addition, we find thatcare continuity is even more important for patients suffering from more complex conditions.Finally, we investigate the intervening processes and find that continuity of care leads to amore efficient provision of effort and time, as measured by diagnostic tests ordered and timeto see a specialist, respectively. Our results have important implications for both the theoryand practice of operations management, in general, and healthcare, in particular.

Key Words: Gatekeepers; Coordination; Continuity of Care; Healthcare Operations;Empirical Operations

1 Introduction

Through the centuries individuals have faced a knowledge challenge. As understanding in an

area grew deeper, it became more difficult for any one individual to learn and retain the necessary

knowledge. Not surprisingly then, the response was specialization – a focus upon a limited area

of study in order to build the necessary expertise (Kuhn, 1962). Gains from specialization, or

as it is often referred to in the operations literature – focus, can be seen at the individual and

organizational levels (Skinner, 1974; Kc and Terwiesch, 2011). Perhaps no industry exemplifies

the potential gains from specialization more so than healthcare. Scientific advances have helped

doctors to learn more about the human body, the diseases experienced, and how to overcome

them. With these advances, doctors have grown more specialized – for example, the number of

recognized sub-specialties in medicine in the U.S. has grown from ten in 1970, to over 145 by

2015 (ABMS, 2015). Further, life expectancy has grown from 70.8 years to 77.9 years in the

U.S. over the same time period (World Bank, 2016).

However, although specialization is valuable in tackling individual difficulties, to address an

overall problem requires identifying which specialists are needed and then coordinating across

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these individuals. In ongoing operations where customers arrive for service, have their needs

assessed, and are then either served or routed, the standard operational advice is a gatekeeper

solution (Shumsky and Pinker, 2003). For example, call centers often offer multi-level technical

support where a first level agent seeks to serve a caller or, if the challenge is too great, routes the

caller to an appropriate specialist (Lee et al., 2012). Within healthcare, the use of gatekeepers

can be seen in emergency department triage, where a caregiver assesses the severity of a patient

and routes her appropriately (Saghafian et al., 2014) or in maternity wards where a midwife

either delivers a baby or routes the patient to a doctor, if necessary (Freeman et al., 2015).

Prior literature clearly establishes the valuable role that a gatekeeper system can play in

improving cost, quality, and efficiency in such single-shot scenarios, as described above. However,

often individuals may need to repeatedly interact with an operational system. In such cases, the

gatekeeper’s role consists of not only routing or serving individuals, but also in coordinating the

ongoing service. Examples include a customer repeatedly interacting with a bank for financial

services, a patient with a chronic disease – for example, diabetes, cancer, or heart disease –

working with her primary care physician to visit specialists, or an IT specialist interacting with

a vendor’s help desk to address her firm’s repetitive, technical difficulties. Despite the fact that

prior literature highlights the challenges that can arise when continuity in service is disrupted

(Press, 2014), prior literature has yet to explore the operational consequences of continuity of

care in gatekeeper systems with ongoing interactions with customers in an empirically rigorous

manner. Therefore, in this paper, building on the research on gatekeepers, coordination, and

healthcare operations, we investigate how continuity of care affects the quality and efficiency of

health outcomes for patients. Moreover, we investigate whether continuity of care has a greater

performance impact for more complex situations.

In addition to examining the effect of continuity of care on health outcomes, we also explore

how continuity of care affects service configuration decisions. Prior work in operations highlights

that gatekeepers may change their behavior under different operational circumstances. For

example, Freeman et al. (2015) find that, under heavy workload, gatekeepers are more likely to

withhold discretionary services from less complex patients and refer more complex patients to

specialists in the system. We consider two service configuration decisions of significant import

in a system with ongoing interactions – efficiency of effort and time resources. With respect

to the former, we investigate whether patients with greater continuity of care experience less

duplication of effort, as measured by fewer diagnostic tests. With respect to the latter, we

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investigate whether patients with greater continuity of care are likely to more quickly and more

efficiently get to see specialists – a key assignment role for a gatekeeper.

To investigate our questions, we use a primary care setting and explore patient interactions

with different primary care physicians (PCPs). PCPs are responsible for: 1) delivering front-

line medical care to patients; 2) providing patients with access to specialists in a health system;

3) coordinating patients care across these specialists. We utilize a unique and comprehensive

dataset from the Veterans Health Administration (VHA), the largest integrated healthcare de-

livery system in the U.S. with 1,700 sites of care. We analyze over half a million patients over six

years that suffer from a chronic disease, diabetes, and examine how their continuity of care with

PCPs affects three dimensions of their care: inpatient visits, length of stay, and readmission

rate. Moreover, we explore how an important moderator, patient severity, affects the continuity

of care and health outcome relationship. In addition, we explore two mechanisms – number of

diagnostic tests ordered and time to see a specialist, to see how continuity of care may reduce

both measures. Finally, visits to a PCP are inherently endogenous. Sicker patients will see a

doctor more often, on average. To correct for this endogeneity, we implement an instrumental

variables approach for our estimation. In particular, we use two variables representing the char-

acteristics of the patient’s primary care facility as instruments: the facility’s designation as an

outpatient clinic and whether the facility is located in an urban area. These two variables reflect

differences in access to PCPs that are likely unrelated to other influences on health outcomes.

Overall we find that continuity of care leads to fewer inpatient visits, shorter length of stays

on those visits, and lower readmission rates. We also see that continuity of care provides even

more operational value for sicker patients. Finally, we see that continuity of care leads to less

waste in the use of resources and in time as both diagnostic tests ordered and time to see a

specialist decreases.

These results offer several important contributions to the operations and healthcare man-

agement literatures. First, we are able to both identify and size the gains from care continuity.

We find that continuity of care results in lower costs and improved clinical quality. This result

is important both theoretically and practically. Although the cost of poor coordination is of-

ten discussed anecdotally (Press, 2014), using an instrumental variables strategy we are able to

econometrically identify the effect. Second, we identify an important moderator – patient sever-

ity. In operational systems that are resource constrained, determining where to allocate limited

resources is a key decision. Our results show that focusing continuity on sicker patients can

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result in savings of over hundreds of millions of dollars annually. Third, we are able to identify

two mechanisms through which continuity of care affects health outcomes. These mechanisms

not only provide theoretical progress, but also offer managerial guidance. Finally, we introduce

a measure to capture continuity of care. Using the Herfindahl-Hirshman Index,

1we are able

to precisely quantify the continuity of care that a patient experiences – at least in terms of

interactions with the gatekeeper. This measure is useful for ongoing studies. Empirical work

can continue to examine the consequences of continuity of care while analytical work can more

accurately model such systems. Altogether, our findings advance both theory and practice in

multiple interaction, gatekeeper systems.

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2 Hypotheses Development

To examine the effect of continuity in gatekeepers over time on operational outcomes we draw

upon three streams of research: (1) gatekeepers, (2) coordination, and (3) healthcare operations.

Briefly we review how each stream informs our problem.

The gatekeeper literature explores how servers, referred to as gatekeepers, provide service

or route individuals to experts for care. For example, in an early work on this topic, Lee and

Cohen (1985) look to optimize speed for customers based on how agents allocate customers to

service facilities. Shumsky and Pinker (2003) explicitly model the two stage system in order to

recommend incentive policies for appropriate referral management given a principal agent frame-

work. Subsequent analytical papers extend the models to include outsourcing (Lee et al., 2012),

system congestion (Alizamir et al., 2013) and tradeoffs between speed and quality (Anand et al.,

2011). Work in healthcare has also explored the impact of gatekeeper systems, often examining

the benefits of systems with and without gatekeepers and exploring incentive design (Brekke

et al., 2007; González, 2010). Although the empirical work on gatekeeper studies is limited, re-

cent work has begun to explore how these systems deviate from models. For example, Freeman

1HHI is an economic concept widely applied in competition law, typically used as a measure of marketconcentration. It is calculated by squaring the market share of each firm competing in a market, and then summingthe resulting numbers. The closer a market is to being a monopoly, the higher the market’s concentration (andthe lower its competition). It has been used in research in operations, for example, in the study of task variety(e.g .Narayanan et al., 2009 and Staats and Gino, 2012)

2It is worth noting that continuity in primary care literature is mainly viewed as the relationship betweena single provider and a patient that extends beyond specific episodes of illness and is sometimes referred to asrelational continuity Haggerty et al. (2003). So, while the terms “continuity of care” and “care coordination”are often interchangeably used, they are different in a primary care setting, although better coordination followsfrom continuity. For our study, we use continuity of care in our setting as we do not directly observe explicitcoordination.

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et al. (2015) find that workload has important operational consequences for gatekeepers in the

context of obstetrics, as midwifes withhold discretionary service (epidural) from low complexity

patients and route more complex patients to doctors, as workload increases. Similarly, Batt and

Terwiesch (2016) show that gatekeepers follow different strategies to manage higher workload

in the care of patients. In particular, triage nurses are likely to order diagnostic tests when

workload is high, rather than wait for them to be ordered by doctors. Outside of medicine, Tan

and Staats (2016) explore behavioral drivers of the decision to route customers. Our work not

only draws upon this prior work, but extends it by explicitly examining the multiple-visit nature

of gatekeeper systems. In other words, although many systems may be single visit (e.g., deliver-

ing a baby), other operational systems involve repeated interactions with gatekeepers. In such

environments, the gatekeepers’ memory may be an important part of the operational process.

Therefore, we not only expand the empirical examination of the topic, but also investigate a

system with multiple gatekeepers serving customers dynamically, over time that, to the best of

our knowledge, has not been examined previously.

Second, we draw upon work that has explored operational coordination – “the management

of interdependencies among tasks” (Hoffer Gittell, 2002, p.1408). Although foundational work

in operations has focused on the benefits that arise from the division of labor (Skinner, 1974),

subsequent work highlights that work that is divided up must eventually be reintegrated for

product or service delivery (Heath and Staudenmayer, 2000). Brooks Jr (1995) is often credited

with popularizing this challenge as he discussed his difficulties at IBM when the focus was on

work in terms of ‘mythical man-months’. Such divisions of work into person-months looked

appropriate on paper, but did not work in practice. In particular, he suggested that such

thinking would lead one to ignore coordination costs and assign more workers to late projects

and so he posited Brooks’ Law, whereby adding workers to a late project delays it further.

Staats et al. (2012) provide empirical support for Brooks Law and find that individuals focus

their attention on gains from division of labor while ignoring potential coordination costs.

Additional work in operations highlights the operational value that arises when processes

are well coordinated. For example, a number of studies have shown the benefits that arise in

the automotive industry when work is coordinated both within and across functions (Clark and

Fujimoto, 1991; Iansiti and Clark, 1994; MacDuffie, 1997). Ton and Huckman (2008) highlight

how operational processes can help to achieve coordination while Hoffer Gittell (2002) discusses

the important role of relationships between different parties in coordination. Both Reagans et al.

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(2005) and Huckman et al. (2009) show that repeated shared experience between providers can

improve outcomes through better coordination in healthcare and software services, respectively.

In this paper we contribute to the literature on coordination in three ways. First, we are able

to identify cleanly the effect of care continuity on operational outcomes and so accurately show

the significant costs of not coordinating work. Second, we identify mechanisms through which

coordination improves outcomes. Finally, we show a way to measure coordination accurately in

a multi-party system, thus enabling future work on the topic.

Finally, our work is related to research in healthcare operations. In particular, over the last

decade a number of studies have shown the operational consequences of humans as the actors

in healthcare systems, as opposed to automatons. For example, Kc and Terwiesch (2009) find

that workload is an important determinant of individuals’ behavior in operational systems (see

also, Powell et al., 2012; Green et al., 2013; Berry Jaeker and Tucker, 2016). In a number

of studies, Tucker finds that instead of following processes as written, caregivers often pursue

workarounds that may improve short-term efficiency, but impede longer-term performance and

improvement efforts (Tucker, 2004, 2015). Studies of learning find that different experience

gained by caregivers may have important consequences for improvement in healthcare (Lapré and

Nembhard, 2011). Such work explores experience such as task (Pisano et al., 2001), team member

(Reagans et al., 2005), firm-specific (Huckman and Pisano, 2006), customer-specific (Clark et al.,

2013), and success or failure (KC et al., 2013). We build upon this work as we explore the inherent

costs of coordination. Were it possible to perfectly transmit information between parties then

receiving care from multiple gatekeepers would be no different than receiving it from a single

gatekeeper. However, given that gatekeepers are human, this is not the case.

2.1 Continuity of Care and Health Outcomes

We now turn our attention to our research questions. We begin by examining why continuity

of care may be related to improved health outcomes for patients. The first reason is learning,

particularly from repeated interactions. A long line of literature on learning in organizations

highlights that, with repeated experience, individuals are likely to learn more about a given area

(Pisano et al., 2001; Reagans et al., 2005). Thus, learning that is focused on a single individual

rather than spread across individuals, may lead to deeper understanding of the individual’s

unique situation, leading to improved coordination (Clark et al., 2013). A parallel may also be

drawn to existing gatekeeper research. In particular, the operations literature highlights that

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effort may be discretionary and that this has an impact on the quality of the outcome (Hopp

et al., 2007; Anand et al., 2011). Multiple visits can be seen as analogous to spending more time

in the operational setting and so are likely related to better outcomes than the alternative. In

our research context, continuity thus implies that, with each successive visit, a physician is likely

to be able to dig deeper into the care for the patient she is currently treating. On the other

hand, a new physician will need to catch herself up to speed. Even if a doctor does an excellent

job capturing information in an electronic medical record, there may still be information missed,

leading to a reduction in continuity of care.

Second, with continuity, there is less risk of a failure to transfer information. A long line of

research suggests that finding the necessary information may not be an issue, but rather trans-

mitting it between people creates a significant challenge (Hansen, 1999). Individuals may leave

out important information when communicating or, alternatively, they may misunderstand one

another when they attempt to communicate – verbally or in writing (Arrow, 1974). Moreover,

repeated interactions may help to solidify knowledge and limit problems with forgetting (Agrawal

and Muthulingam, 2015). In our context this suggests that not only might individuals fail to

record all relevant patient information, thus compromising the coordination of care, but that

even if they do record the information, different styles or approaches could lead to confusion.

The question of coordination is one that has received attention within the clinical and medical

literature as well. Despite the significant conversations about care coordination that are ongoing

within the health sciences, Peikes et al. (2009) find that the impact of care coordination is mixed.

In particular, they review interventions such as educating patients, encouraging patients to take

medicine, calling patients, and the like. As discussed above, in this paper we are interested

in whether continuity in the care by a given doctor has a positive benefit on health outcomes.

Van Walraven et al. (2010) review 139 studies that claim to examine coordination and find that

only 18 met basic methodological criteria, in terms of rigor. Of those 18, only seven examine

provider continuity. Six of the seven explore the relationship between provider continuity and

hospitalization, while one finds that seeing the same provider, after hospitalization, is related

to a lower likelihood of hospital readmission (Van Walraven et al., 2004). We contribute to the

healthcare clinical research in several ways. First, we provide a test of not only hospitalization,

but also length of stay and the likelihood of readmission – two additional, important, health-

focused variables. Second, we focus on a population of patients that not only have severe

medical needs (diabetes patients), but also have very detailed data that permits us to control

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for many factors that could affect outcomes. Third, combining our long panel of data with an

instrumental variables approach, we are able to identify causality in our proposed hypotheses.

Finally, as discussed more below, each of our additional hypotheses offers new insight into not

only the operations literature, but also the clinical literature.

We note that although our arguments to date suggest that greater continuity of care would be

related to better outcomes, it is theoretically possible that the relationship could be curvilinear.

Although moderate levels of continuity could be better than low levels, it is possible that too

focused of care could be problematic. Similar to work examining variety (Narayanan et al., 2009;

Staats and Gino, 2012), it is possible that by switching to a different individual, the new person

might identify a new factor and thus provide even better care. Although certainly possible, we

think that this is less likely, given the significant knowledge needed to provide care, especially

for a chronic disease. Nonetheless, it is an empirical question that we examined but ruled out as

an alternative explanation as we do not find support for curvilinearity. Given all of these factors

we hypothesize:

HYPOTHESIS 1: Greater continuity of care is related to better health outcomes, as compared to

lower continuity of care.

2.2 Continuity of Care and Complexity

Next, we consider the interaction of continuity of care and complexity. In particular, is continuity

in care more valuable when patient needs are more complex? We suggest that it is. Theoretically,

the justification comes from the same framework as before. Research on learning suggests,

although not definitively, that repeated interactions are more important when tasks are complex

(Argote, 1993; Edmondson et al., 2001). The intuition behind this is that, for more complex

tasks, more learning is needed and each successive unit of experience can move one further down

the learning curve before reaching the flat portion with diminishing returns. In our setting, this

suggests that, for patients with more complex needs, it could take more work to understand

and then properly coordinate the needs, such that continuity of care offers more value. In

addition, transfer of knowledge is harder for more complex situations (Singh et al., 2007). More

complex cases create not only more knowledge to transfer, and thus a greater risk that part

of the knowledge is not handled adequately, but also the cost of not transferring knowledge in

a complex situation may be greater since the interactions within the work are likely less well

understood (Perrow, 2011).

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Understanding the effect of continuity of care on different types of patients, such as more or

less complex patients, is a question asked by the medical literature (Van Walraven et al., 2010),

but to the best of our knowledge, one that has not been answered. As a result, understanding this

relationship is important for moving research forward in the medical, operations, and learning

literatures. Thus, we hypothesize:

HYPOTHESIS 2: Greater continuity of care is related to better health outcomes for more complex

patients, compared to less complex patients.

2.3 Continuity of Care and Efficiency in Effort and Time

Up until this point, we have focused our attention on the overall outcome of continuity of care.

An important question for both operations theory and managerial practice is the mechanisms

through which continuity may improve performance. We consider two possible mechanisms: (1)

duplication of effort; (2) quicker system response.

We begin by examining the duplication of effort. With greater continuity in care, individuals

are less likely to repeat the same actions. That can be as a result of prior learning. Individuals

are aware of the answers to their queries and so need not ask the same questions. It may also

occur due to better transfer of knowledge. Individuals are less likely to miss the work that has

been completed when they have done it themselves. Finally, related to both points, ego-centric

bias may lead an individual to repeat work done by others because they want to see the results

for themselves, or they want small changes to the work, which may or may not be truly necessary.

In our context, we examine duplication of effort with diagnostic tests. Examples of relevant

diagnostic tests are X-Ray, ultrasound, and magnetic resonance imaging (see §4.2 for a complete

list). A doctor who has previously ordered tests is less likely to order them again since she

should both be aware of the tests, know the information encoded therein, and also have ordered

the precise tests she wised to consider. Thus, we hypothesize:

HYPOTHESIS 3: Greater continuity of care is related to less duplication of effort, as mea-

sured by fewer diagnostic tests, as compared to lower continuity of care.

Turning to speed of response, continuity of care is likely to improve performance for this

reason, as well. First, with greater learning due to continuity, an individual is positioned to

rapidly consider and make a decision in a given situation. Moreover, without problems transfer-

ring information between individuals, the decision maker has what she needs, when she needs

it, and so instead of needing to gather more information and decide upon that information she

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is in a position to move forward rapidly. This could be through less duplication of effort, prior

to making a decision, as discussed above, or alternatively greater confidence in the decision so

less time is required to make the decision.

In our context, we examine the speed of response by exploring how long it takes an individual

to see a specialist after appointments with a PCP. Thus, when a doctor has the necessary

information and is able to interpret and decide upon this information, then less time should

pass before the specialist appointment. Alternatively, if care continuity is not maintained then

an individual may need to collect more information or take more time to consider it, as discussed.

As a result, we hypothesize:

HYPOTHESIS 4: Greater continuity of care is related to quicker system response, as measured

by time to see a specialist, compared to lower continuity of care.

3 Data and Variables

The source of our data is the the Department of Veterans Affairs (VA), in particular the Veterans

Health Administration (VHA), a component of the VA that implements the medical assistance

program. VHA is the largest integrated healthcare delivery system in the U.S. with 1,700 sites

of care and more than 53,000 independent licensed health care practitioners. As of fiscal year

(FY) 2014, there were 9.1 million enrollees in the VHA contributing to 92.4 million outpatient

visits and 707,400 inpatient admissions on an annual basis (VA, 2016). It is worth highlighting

that the number of outpatient visits increased by 99% from 2002 to 2014, in contrast to a 34%

and a 25% increase in the number of enrollees and inpatient admissions, respectively, during the

same time period. VHA is divided into 21 regional systems called Veterans Integrated Service

Networks (VISNs) that provide integrated care to veterans based on geographic location and

differ by regional leadership and policies. The current VISN-network structure was established

in March 1995 with a goal to decentralize decision-making and provide greater consistency in

the quality of care system-wide. It is worth emphasizing that the outpatient setting serves as

the primary point of PCP-patient interaction and by implication, care coordination.

Patients in the VA tend to be relatively sick with multiple co-morbidities. In our study, we

focus on VA patients that receive care for diabetes, a chronic disease. High-quality diabetes

care necessitates improved coordination between clinical teams as patients transition through

different stages of the life span (ADA, 2016). The VA serves as an ideal setting for our study

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for several reasons. First, the VA information system is well developed and includes a rich

assortment of patient-level data maintained in national databases going back to 1997 (Miller

et al., 2004). Second, the VA serves as the primary healthcare system for veterans and patients

pay according to their “enrollment priority” that is based on income and service-connected

conditions. Generally speaking, patients in the lower enrollment priority group have lower out-

of-pocket expenses; as of 2015, patients in the six priority groups (out of eight) had no co-pay.

Finally, physicians in VA are salaried. These characteristics mitigate the agency problems with

heterogenous third-party payers because physicians have a uniform incentive scheme.

We have access to medical records, pharmaceutical claims, and other clinical databases for

all patients treated within the VA (nationwide) from October 1, 2002 to September 30, 2008.

Our study cohort consists of diabetic patients who were 40 years or older on October 1, 2002.

We note that type 2 diabetes accounts for 90-95% of all diabetes cases, and it usually develops

after the age of 40, which is why it was previously called adult-onset diabetes (ADA, 2016).

Patients are included if they meet one of the following two criteria: (a) at least one diabetes

medication filled between October 1, 2002 and September 30, 2003, (b) two or more ICD-9

diagnostic codes 250.xx present for inpatient care and outpatient visits between October 1, 2001

and September 30, 2003. This criteria is known to have 93% sensitivity and 98% specificity

for identifying diabetes (Miller et al., 2004). We limit our data to patients who have (1) have

documented diagnosis of diabetes, and (2) HbA1c values available during the baseline year

(October 1, 2002 to September 30, 2003), (3) no missing values of enrollment priority, date of

death, and patient identifiers, resulting in 550,550 unique patients. We aggregate healthcare

data for all included patients on a quarterly basis, resulting in 9,829,507 unique patient-quarter

observations. A quarter is defined according to a calendar year (e.g., 2003Q1: 01/01/2003 –

03/31/2003). 2003Q3 serves as the baseline period, October 1, 2003 serves as the index date,

and 2003Q4-2008Q3 serves as the observation periods. Our data consists of the following set of

variables (acronyms of key variables are italicized in parentheses).

3.1 Continuity of Care Measures

There is currently no established operational measure of care continuity or care coordina-

tion/fragmentation. Bice and Boxerman (1977) is one of the first to provide an operational

definition of continuity of care using an index that is standardized for the number of visits

and providers. However, the index requires one to distinguish between referred and unre-

11

ferred providers. Other measures that have been used in recent studies include a count of

providers that a patient sees during the course of treatment during a year to measure coordina-

tion/fragmentation (e.g., Pham et al., 2009). A major limitation of this “counting approach”

is that it does not account for the differences in the care concentration. Most recently, Frand-

sen et al. (2015) propose a care fragmentation index, based on the Herfindahl-Hirschman In-

dex (HHI), that captures the degree to which a patient’s care is concentrated among a set of

providers. The index is based on each provider’s share of the total costs associated with that

patient’s claims and is calculated for PCP’s other patients, highlighting PCP’s practice style.

We propose a quantitative “normalized” measure called care continuity index (CCI ), also

based on HHI, but one that captures care concentration from the number of visits. CCI is our

key independent variable of interest. To calculate CCI for a given time period (quarter), we

first calculate the number of outpatient (OP) visits in the previous year (equivalent to past four

quarters) that is prior to the current quarter (OP_VISITS ). The variable PCP_VISITS is used

to represent all OP visits to primary care providers (the focus of our work, see §3.6 below). CCI

is then the sum of the squares of provider share, where the provider share is defined as the

fraction of total outpatient visits to each provider. This implies that the higher the value of

CCI, the greater the continuity in care; conversely, the lower the value of CCI, the higher the

fragmentation in care. The maximum value of CCI is 1, which happens when the patient only

sees one provider.

We note a few things regarding calculation of OP_VISITS and CCI. First, our choice of prior

year as a basis for calculating CCI reflects the fact that annual checkups typically occur once a

year. However, as we show in robustness checks later, varying the number of past periods does

not change our conclusions in a meaningful manner. Second, when calculating these measures, we

disregard any visits that are not directly related to patient care or coordination. We also exclude

“diagnostic” visits to avoid “double-counting” of providers since the provider who ordered the

diagnostic tests is tagged to the patient again when the technicians carry out the tests. Finally,

we note that CCI measure is calculated on a rolling four-quarter basis, implying that for two

successive periods, the calculation of CCI utilizes three common periods.

3.2 Outcome Measures

Our key outcome measures, that we calculate for each patient-quarter combination, come from

inpatient (IP) data, are: (1) total number of inpatient visits in the current period (IP_VISITS );

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(2) the average length of stay (LOS) for all inpatient visits during the current period (MEAN_LOS );

and (3) total number of inpatient visits in the current period that result in a readmission within

30 days (30D_READ). It is worth noting that while care continuity measures are calculated for

the past periods, the outcomes measures are calculated for the current period. We create this

artificial separation to avoid any confounding between care continuity and its impact.

3.3 Patient Controls

Patient demographics (DEMO). Demographics, established at baseline, serve as potential

risk factors and include age, gender, race, marital status, enrollment priority, patient’s duration

of diabetes and Charlson comorbidity index at baseline.

Glucose lowering medications (DRUGS). The medications were classified as either in-

jectable or oral.

3Oral medications include the following drugs: metformin, sulfonylureas, rosigli-

tazone, pioglitazone, and others. A patient is considered to be using a drug if he possessed at

least a 30-day supply of drug in a given quarter, irrespective of the dosage.

4This indicator

variable, representing a patient’s drug use, is created for all the drugs described above. The

30-day supply threshold also serves as a proxy for medication adherence, especially considering

that we cannot directly observe whether the patient actually consumes the medication.

Lab results (LAB). Lab variables are continuous, time-varying, positive variables and in-

clude glucose levels (HbA1c),5

cholesterol (HDL, LDL, total), triglycerides, blood pressure (sys-

tolic, diastolic), and body mass index. We implement the “last observation carried forward”

method to populate the missing lab values (Rosenstock et al., 2006). When multiple values are

observed for a given measure in a quarter, we average them.

Main events (MAIN ). Main events, identified from inpatient and outpatient datasets using

ICD-9-CM diagnoses codes, include death from any cause, cardiovascular (CV) complications,

3Oral medications remain the overwhelming choice of diabetes treatment. Amongst oral hypoglycemic med-ications, sulfonylureas and metformin are the oldest and most commonly used classes of drugs. The standardsof medical care in diabetes issued by American Diabetes Association provides details on all diabetes medications(?).

4Thirty day period is typically considered to be a minimum supply for a treatment to show any effect. Further,ignoring dosage is a reasonable assumption since patients often start with lower dosage and may be prescribed ahigher dosage later, depending on their response. Note that the maximum days supply in a quarter equals thetotal number of days in that quarter, for example, there are 92 days in the quarter spanning October 1, 2003 toDecember 31, 2003.

5Glycated hemoglobin level test or HbA1c test is the most commonly used test to monitor diabetes. Accordingto widely accepted standards, untreated HbA1c levels at or above 6.5% indicate diabetes and levels between 5.7%and 6.4% indicate an increased risk of developing diabetes (?).

13

microvascular (MV) complications, and hypoglycemia. For each of the following events, we

created a binary variable, which takes a value of one if a patient experiences the event at least

once in a quarter: death from any cause, AMI , stroke , congestive heart failure, any CV

complication, any MV complication, and hypoglycemic event.

Organizational Characteristics (ORG). These include characteristics of the patients’ pri-

mary care facility such as number of operating beds, primary service type, as well as indicators

for whether the facility is affiliated with a teaching hospital, in an urban location (URBAN ),

or has a dedicated diabetes unit, where we note that the two main “primary service types” are

community-based outpatient clinic (CBOC ) and VA medical center (VAMC ). We also create an

additional time-varying facility variable indicating the number of deaths in a given quarter for

each facility. Finally, we include the corresponding parent facility and VISN for each facility.

6

3.4 Instrumental Variables

Despite various patient-level controls, there are potential endogeneity concerns that may lead to

biased estimates for the coefficients on key independent variables, CCI and PCP_VISITS. For

example, a patient who is inherently sick, will tend to have a higher number of primary care

visits, thereby inflating the number of PCP visits. Given that sickness is likely to be positively

correlated with inpatient outcomes, the estimates on PCP_VISITS are likely to be upward

biased. However, a sick patient may not be able to see his designated PCP at every visit (for

example, if the PCP works on specific days), resulting in fragmented care (lower CCI), and

a downward bias in the estimate. However, it is also possible for a healthy patient to have

fragmented care, for example if he is indifferent to the provider he sees or is trying out multiple

providers to find the best fit, resulting in upward bias in the estimates. We note that we control

for patient sickness through Charlson index. To more fully address any endogeneity concern we

use an instrumental variables approach. In particular, we select the two variables representing

characteristics of the patient’s primary care facility as instruments, described below.

7

Our first instrument is an indicator for whether the patient’s primary care facility is a

community-based outpatient clinic (CBOC ). CBOCs were designed to bring primary care facili-

ties closer to Veterans’ residences (particularly in rural areas) and was part of the VA’s strategy

6There are 719 primary care facilities, 128 parent facilities, and 21 VISNs in our data. A parent facility alsoserves as a primary care facility.

7These are based on our conversations with the VA primary care providers.

14

to shift the focus of care from an inpatient to an outpatient setting, and for reducing cost per

user. CBOCs improve access to primary care for veterans, especially those who live at significant

distance from VA hospitals (VHA, 2016). These clinics provide the most common outpatient

services, including health and wellness visits, without the hassle of visiting a larger medical

center (VHA, 2016), thus providing additional options to veterans for seeking primary care.

Fortney et al. (2002) find that CBOC patients had more primary care visits but were less likely

to use specialty mental health, ancillary and inpatient care in the VA. Thus, we expect CBOC

to be positively correlated with PCP_VISITS. However, this increased access to VA primary

care via community clinics may fragment outpatient care in unintended ways (Liu et al., 2010),

indicating a potentially negative correlation between CBOC and CCI.

Our second instrument is an indicator for whether the patient’s primary care facility is located

in an urban area (URBAN ). Urban areas are more likely to contain large facilities that contain

more equipment (e.g., with diagnostic machines), and are affiliated with a teaching hospital

(correlation coefficient: 0.27). Further, urban areas typically have a higher concentration of

doctors. Consequently, PCPs treating patients in urban facilities have better access to resources

(specialists, medical equipment, and research) not only within the facility, but also in the larger

urban area. In other words, PCP’s in urban facilities are better equipped to coordinate care.

Thus, we expect URBAN to be positively correlated with CCI. The impact of URBAN on

PCP_VISITS is not clear as many veterans living in rural areas (comprising 36% of total

enrolled veterans) visit urban facilities for their primary care needs. Long travel times and

lack of transportation have been identified as significant barriers to health care access for rural

veterans, particularly for primary care services (Gale and Heady, 2013).

Validity of Instruments. A valid instrumental variable should satisfy both relevance and

exclusion restriction conditions. The relevance condition implies that the instruments should

be correlated with the endogenous variable (e.g., CCI). While we have provided theoretical

argument above for why this correlation should hold, our empirical tests confirm this. Table 1

lists the pairwise correlation between the endogenous variables and the instruments, first-stage

regression estimates with robust standard errors, and first-stage regression summary statistics.

We note that the estimates for instruments (CBOC, URBAN ) are highly significant. CBOC

is negatively correlated with CCI but positively correlated with PCP_VISITS. Conversely,

URBAN is positively correlated with CCI but negatively correlated with PCP_VISITS. It is

15

Table 1: Correlation matrix (top left), first stage regression estimates (top right), and first stage

summary statistics (bottom) for the IV regression.

Correlation Matrix

CCI PCP_VISITS CBOC URBAN

CCI 1

PCP_VISITS -0.4074 1

CBOC 0.0722 -0.0052 1

URBAN -0.0381 0.0306 -0.0261 1

Estimates (SE)

CCI PCP_VISITS

CBOC -0.0119*** 0.1952***

(0.0004) (0.0036)

URBAN 0.0063*** -0.0144***

(0.0003) (0.0027)

REST_VISITS -0.0009*** 0.0168***

(0.0000) (0.0001)

Notes: The first stage estimates for patient controls are not reported in the interest of space. Robuststandard errors in parentheses. *p < 0.05; **p < 0.01; ***p < 0.001.

worth noting that, compared to unadjusted pairwise correlation, the adjusted correlation (first-

stage estimates) between instruments and endogenous variables is directionally opposite. The

F -statistic for CBOC (559.95) and URBAN (1506.49), as well as the minimum eigenvalue

statistic of 166.74 (which presumes homoscedastic errors) are well above the critical value for

the Stock-Yogo weak instrument test based on 2SLS Size of nominal 5% Wald test, suggesting

that our instruments are not weak (Cameron and Trivedi, 2010). In addition, based on our

tests of endogeneity (p=<0.0001 for both the Durbin chi2 and Wu-Hausman), we can reject

the null hypothesis that our two independent variables are exogenous, thus justifying our IV

approach. Moreover, our instruments should satisfy the exclusion restriction condition since

the two characteristics of the facility should affect the dependent variable (e.g., MEAN_LOS )

primarily through the endogenous variable (e.g., CCI ). Finally, since the number of instruments

is the same as the number of endogenous variables, our model is just-identified, which means we

can not test for overidentifying restrictions and instead assume that the instruments are valid.

16

3.5 Mediation Variables

To test hypotheses 3 and 4, we create the following two variables for each patient-quarter

combination: (1) DIAG_TESTS : number of diagnostic tests, to test Hypothesis 3, and (2)

MEAN_TIME_SPEC : average time, in days, to visit a specialist provider (not primary care),

to test Hypothesis 4. We note a few things. First, the calculation of DIAG_TESTS includes

the following diagnostic tests: X-Ray, electrocardiogram, ultrasound, computerized tomography,

magnetic resonance imaging, electromyogram, electroencephalogram, positron emission tomog-

raphy, topographical brain mapping, and magnetoencephalography. We note that these tests

are usually expensive, less widely available, more invasive, and riskier; they are ordered by a

provider to typically answer a specific question such as to establish a diagnosis, screen for dis-

ease, provide prognostic information, or to confirm that a person is free of disease (Abram and

Valesky, 2015). Second, the calculation of MEAN_TIME_SPEC involves determining the time

to visit the first specialist after a PCP visit.

8Finally, as with outcome variables, we use the

past four quarters, prior to current quarter for the two mediation variables.

3.6 Healthcare Setting: Primary Care

We focus on primary care visits since that serves as a principal setting for coordinating care.

We create this “primary care” dataset, henceforth referred to as PCMDOC data, by limiting the

overall data to only those visits that are to (1) a clinic stop classified as primary care medicine

and (2) providers classified as physicians and (3) periods where there is at least one primary

care visit. This resulted in 6,855,243 unique patient-quarter observations and 481,658 patients

in the PCMDOC data.

9

While the variable PCP_VISITS represents the total visits to a PCP in the previous year,

we also create a new variable REST_VISITS (=OP_VISITS -PCP_VISITS ) that captures all

other visits by the patient. While the results in this paper are primarily based on PCMDOC

data, we conduct a similar analyses on a dataset that is limited to “mental health” visits since

that is another care type where continuity of care is critical for an effective treatment. We find

that our results continue to hold (see §4.3).

8Additional visit(s) to specialist(s) are typically dependent on previous visits.9The rules used to establish PCMDOC data imply that not every patient, who is alive throughout the obser-

vation period, appears in each quarter.

17

Summary Statistics. Table 2 lists the summary statistics for the key variables for the baseline

period. We report these statistics for both the overall cohort and those included in the PCMDOC

data and note that the statistics are similar. The average age in our overall cohort is 66.9 years

old, and approximately 13% of the patients are non-Hispanic black. The average values of BMI,

HbA1c, LDL, and Systolic BP of our overall cohort are 30.9 kg/m2, 7.3%, 100.4 mg/dL, and

137.3 mm Hg, respectively. Table 3 lists aggregate summary statistics for the key outpatient,

inpatient, and mediation measures in the PCMDOC data. On average, a patient sees 1.38

primary care physicians annually for 2.94 PCP visits (approximately 2 visits per physician).

The mean CCI is fairly high at 0.88 but considering that the visits are limited to PCPs, this is

not surprising. In terms of outcomes, a patient is hospitalized 0.06 times per quarter on average,

resulting in a mean LOS of 0.3 days. On average, 0.01 PCP visits, or a sixth of all visits, result

in a readmission within 30 days. It is worth noting that (1) almost three-quarters of all patients

have 3 PCP visits or less, (2) the length of stay is at most a week for three-quarters of the

hospitalizations. It is worth noting that (1) almost three-quarters of all patients have 3 PCP

visits or less, (2) the length of stay is at most a week for three-quarters of the hospitalizations.

4 Estimation and Results

We first want to understand the unadjusted relationship between care continuity and patient

outcomes (using raw data). To achieve this, we first calculate average CCI across all patients,

and then split the patients into a “lower fragmented” group (CCI � mean) and a “higher

fragmented” group (CCI < mean). Then, for each of the twenty time periods, we calculate the

mean value of each of the three outcomes: IP_VISITS, MEAN_LOS, 30D_READ, for patients

in the lower and higher fragmented groups, as well as across all patients. We then calculate

the deviation from the mean or the difference between the overall mean and group mean. We

calculate this measure for CCI and all the patient outcomes for each of the twenty time periods.

Figure 1 plots this deviation measure for CCI and the three outcomes. Note that the differences

across all measures are statistically significant for all time periods (p<0.001). The figure shows

that, without controlling for other factors, patients in the “lower fragmented” group have better

outcomes for all the time periods. This supports H1. We test it formally next.

18

Table 2: Summary of baseline statistics.

All PCMDOC data

Number of patients 550,550 481,658Patient characteristics

Male 98.2 98.3Non-Hispanic White 71.2 71.0Non-Hispanic Black 12.9 13.1Age, years, mean ± SD 66.9±10.5 66.6±10.5

<65 40.1 41.265-74 32.3 32.3�75 27.6 26.5

Diabetes duration, yrs.<3 41.2 40.3�3 58.8 59.7

Risk factors

BMI, kg/m2, mean±SD 30.9±5.9 31±5.9�25 61.5 61.5Missing 29.1 29.3

A1C, % b, mean±SD 7.3±1.4 7.3±1.4�7.0 52.0 52.5

LDL-C, mg/dL, mean±SD 100.4±31.9 100.5±31.9�100 32.8 32.8Missing 30.2 30.2

Systolic BP, mm Hg, mean±SD 137.3±18.1 137.3±18.1�140 31.8 31.6Missing 24.5 25.1

Medication use

Insulin 23.5 23.9Sulfonylureas 49.1 49.6Metformin 37.0 37.7Rosiglitazone 7.4 7.6Pioglitazone 2.7 2.9Other 1.2 1.2Facility characteristics

VA medical center 78.3 77.8Community Based Outpatient Clinic 19.3 19.6Urban Location 87.6 87.4Teaching-Affiliated 70.8 70.4No. of outpatient beds

<300 70.5 70.5�300 29.5 29.5

Notes: All reported values are percentage of patients, unless otherwise specified. A patient could use asingle medication or in conjunction with other diabetes medications; the categories are not exclusive.

19

Table 3: Summary statistics for key outpatient, inpatient, and mediation measures.

Min Max Mean Std. Dev. Median 99 pctl 75 pctl 25 pctlNumber of Providers 1 27 1.38 0.85 1 5 2 1PCP_Visits 1 142 2.94 2.10 2 11 4 2CCI 0.04 1.00 0.88 0.22 1 1 1 0.86IP_VISITS 0 13 0.06 0.29 0 1 0 0MEAN_LOS 0 1441 0.30 3.02 0 8 0 030D_READ 0 13 0.01 0.15 0 0 0 0DIAG_TESTS 0 90 1.50 2.35 1 11 2 0MEAN_TIME_SPEC 0 1879 47.93 52.95 32.5 242 62 16

4.1 Continuity of Care and Patient Outcomes

4.1.1 Exogeneity Assumption

The regression model specification to test Hypothesis 1 is listed below; we use a linear probability

model (OLS) to estimate the effects.

Yit = �CCIit + �1PCP_V ISITSit + �2REST_V ISITSit+

�TCONTROLS_TVit + µTCONTROLS_TIVi + Y Qt + ✏it.

Here Y represents the main outcome of interest for each time period (IP visits, mean LOS, and

30-day readmissions), � is the coefficient of interest, and �1 and �2 represent coefficients for two

visit-related controls. Further, � and µ represent coefficients on time-variant and time-invariant

patient-level variables, respectively, Y Q represent the dummies for time, ✏ is the error term, and

the subscripts i and t refer to the patient and time period, respectively.

Table 4 shows the OLS estimates, where we only include estimates for CCI (�), PCP_VISITS

(�1), and REST_VISITS (�2). Note that we assume the errors are heteroscedastic and, there-

fore, report robust standard errors. We test eight different specifications of the model, each

of which includes CCI, the key independent variable of interest. Each specification builds on

the previous one, starting with the first specification, where the only controls are for time. In

specifications 2 and 3, we add controls for the number of PCP visits (PCP_VISITS ) and the

rest of the OP visits (REST_VISITS ). In the remaining specifications, we add the following

patient controls: patient demographics at baseline (specification 4), lab results (specification 5),

medications used (specification 6), and main events (specification 7), and characteristics related

to patient’s primary care facility (specification 8). CCI is negatively correlated with all three

outcomes and in all eight specifications, with the effect size decreasing as we add more controls.

20

Figure 1: Deviation from the average in the occurrence of patient outcomes for the lower frag-

mented (CCI�mean CCI) and higher fragmented (CCImean CCI) groups.

Notes: The vertical axis represents the deviation from the mean, which equals the difference between themean patient outcome in a given group (lower or higher fragmented) and across all patients. All differencesare statistically significant for all time periods (p<0.001). The red dotted line (with ⇤) represents “lowerfragmented” group (CCI � mean CCI) and black dotted line (with �) represents “higher fragmented”group (CCI < mean CCI). The x-axis represents time (year-quarter).

Next, we test Hypothesis 2 by including a (non-linear) interaction term, CHARLSON ⇥

CCI, in the above linear regression model. Table 5 shows the resulting estimates for key

dependent variables of interest. All estimates are significant for all the outcomes. Compared

to healthy patients (CHARLSON=0), patients who are sicker benefit more from continuity of

care in terms of improved outcomes. Further, the sicker the patient (the higher the Charlson

Index), the greater the benefit of care continuity for that patient, with the sickest patients

(CHARLSON=3) deriving the most benefit. Further, the F-test reveals that the coefficients on

the three interaction terms are jointly significant (p<0.001).

4.1.2 Endogeneity Assumption

To address potential endogeneity concerns, we estimate our model using IV regression, where

our choice of instruments are CBOC and URBAN. We use standard two-stage least squares

21

Table 4: Association between CCI and patient outcomes: OLS estimates.

Notes: Robust standard errors in parentheses. *p < 0.05; **p < 0.01; ***p < 0.001.

(2SLS) for estimation such that we fit our endogenous variables in the first stage and outcome

variables in the second stage, using residuals from the first stage. The regression coefficients,

along with robust SE, are shown in Table 6 (left panel). After controlling for PCP visits, other

OP visits, and patient risk factors, CCI is negatively and significantly correlated with all three

outcomes. Further, the effect sizes are bigger by one to two orders of magnitude, compared to

the case when CCI and PCP_VISITS are treated as exogenous.

To test Hypothesis 2, we again include a (non-linear) interaction term, CHARLSON⇥CCI,

in the regression model. Our results, shown in Table 6 (right panel), continue to hold; compared

to healthy patients, sicker patients gain the most benefit from continuity of care. Again, the effect

sizes are an order of magnitude bigger compared to the case when CCI and PCP_VISITS are

treated as exogenous. Together with the robustness checks (see §4.3), we conclude that continuity

of care is associated with improved patient outcomes, particularly so for sicker patients.

22

Table 5: Association between CCI and patient outcomes as function of patient severity: OLS

estimates.

IP_VISITS MEAN_LOS 30D_READCHARLSON=1 ⇥ CCI -0.0192*** -0.0975*** -0.0061***

(0.0016) (0.0156) (0.0009)CHARLSON=2 ⇥ CCI -0.0276*** -0.1607*** -0.0088***

(0.0022) (0.0212) (0.0012)CHARLSON=3 ⇥ CCI -0.0483*** -0.2629*** -0.0184***

(0.0031) (0.0332) (0.0018)CCI 0.0056*** 0.0208** 0.0031***

(0.0007) (0.0073) (0.0004)PCP_VISITS 0.0061*** 0.0232*** 0.0016***

(0.0001) (0.0009) (0.0001)REST_VISITS 0.0010*** 0.0059*** 0.0003***

(0.0000) (0.0001) (0.0000)CHARLSON=1 0.0281*** 0.1364*** 0.0078***

(0.0015) (0.0141) (0.0008)CHARLSON=2 0.0364*** 0.1991*** 0.0101***

(0.0020) (0.0198) (0.0011)CHARLSON=3 0.0791*** 0.4050*** 0.0261***

(0.0029) (0.0305) (0.0017)

All Patient Controls Yes Yes YesTime Dummies Yes Yes YesObservations 6,855,243 6,855,243 6,855,243R-squared 0.0035 0.0118 0.0314

Notes: CHARLSON=0 serves as the reference group. Robust standard errors in parentheses.*p < 0.05; **p < 0.01; ***p < 0.001.

4.2 Continuity of Care: Efficiency in Effort and Time

We next test Hypotheses 3 and 4. We specify two regression models to estimate how each of

the two mechanisms affect outcomes. In the first specification, we estimate how care continuity

impacts the DIAG_TESTS and MEAN_TIME_SPEC, that we refer to as mediation measures

(MM). In the second specification, we estimate the effect of the MM on the main outcome

variables, where we include CCI as a predictor. We estimate the coefficients using IV regression

(2SLS), where CBOC and URBAN serve as instruments, as described earlier.

Tables 7 lists the estimates, along with robust SE, of the key coefficients of interest. Our

results confirm H3 and H4. CCI is negatively associated with DIAG_TESTS, implying that care

continuity reduces the number of expensive diagnostic tests conducted, thus improving efficiency

of care. However, DIAG_TESTS is positively correlated with patient outcomes, which makes

sense since providers are likely to diagnose/screen those who they believe are likely to have or

develop the disease. We also find that CCI is negatively associated with MEAN_TIME_SPEC,

23

Table 6: Association between CCI and patient outcomes (left panel) and as a function of severity

(right panel): IV estimates.

Hypothesis 1 Hypothesis 2

IP_VISITS MEAN_LOS 30D_READ IP_VISITS MEAN_LOS 30D_READ

CHARLSON=1⇥CCI -0.3725*** -2.2616*** -0.0907***

(0.0340) (0.1830) (0.0129)

CHARLSON=2⇥CCI -0.0834 -1.5551*** 0.0072

(0.0467) (0.2442) (0.0184)

CHARLSON=3⇥CCI -0.3000*** -3.7690*** -0.0643*

(0.0723) (0.3784) (0.0304)

CCI -1.2442*** -1.4088* -0.4676*** -1.4342*** -0.6725 -0.5603***

(0.1136) (0.7117) (0.0476) (0.1443) (0.7684) (0.0620)

PCP_VISITS -0.1663*** -0.4865*** -0.0507*** -0.1826*** -0.4942*** -0.0573***

(0.0074) (0.0455) (0.0031) (0.0089) (0.0480) (0.0037)

REST_VISITS 0.0029*** 0.0133*** 0.0008*** 0.0028*** 0.0129*** 0.0008***

(0.0001) (0.0006) (0.0000) (0.0001) (0.0006) (0.0000)

CHARLSON=1 0.3600*** 2.1222*** 0.0878***

(0.0298) (0.1618) (0.0113)

CHARLSON=2 0.1153** 1.5306*** 0.0045

(0.0403) (0.2117) (0.0160)

CHARLSON=3 0.3495*** 3.6019*** 0.0803**

(0.0610) (0.3205) (0.0256)

All Patient Controls Yes Yes Yes

Time Dummies Yes Yes Yes

Observations 5,403,245 5,403,245 5,403,245

Notes: CHARLSON=0 serves as the reference group. Robust standard errors in parentheses.*p < 0.05; **p < 0.01; ***p < 0.001.

implying that continuity of care shortens the time to see a specialist after a PCP visit, thereby

improving effectiveness of care. We also find that MEAN_TIME_SPEC is negatively correlated

with patient outcomes. This makes sense since a quicker visit to a specialist is likely to lead to an

early diagnosis/screening. The table also reports p-values from the Sobel-Goodman Mediation

Tests, which test the significance of a mediation effect. All the p-values are very low (p<0.01),

indicating that the mediation effect is significant.

4.3 Robustness Checks

1. Restricting the number of PCP visits: A person with just one annual PCP visit

would have a CCI of 1, signaling high care coordination, which may bias our estimates.

On the other extreme, patients with an very large number of PCP visits could also skew

the estimate of the coefficients on CCI. To address this concern, we restrict the data to only

those observations, where PCP_VISITS is at least 2 and/or at most 11 (99

thpercentile).

24

Table 7: Association between the mediator and patient outcomes: number of diagnostic tests

(left panel) and average time to visit a specialist (right panel): IV estimates.

Notes: Robust standard errors in parentheses. *p < 0.05; **p < 0.01; ***p < 0.001.

2. Restricting to “Healthy” Patients: To alleviate the concern that our results are driven

mainly by sicker patients, we restrict the data to only “healthy” patients, those with a

baseline Charlson index of zero.

3. Restricting to “Medicare Ineligible” Patients: One of the limitations of our data is

that we do not have records on services that veterans may have obtained from outside of

the VA, Medicare in particular. Thus, we limit the data to patients who are 60 years or

younger at baseline, thereby guaranteeing that none of the patients qualify for Medicare

eligibility during the observation period.

4. Varying the number of quarters for calculating CCI: We use past 3 quarters as

well as past 5 quarters to calculate the CCI.

5. Alternate Model Specifications: We test two alternate specifications where we replace

(1) REST_VISITS with OP_VISITS (=PCP_VISITS + REST_VISITS ), (2) also re-

place PCP_VISITS with FR_VISITS (=PCP_VISITS / OP_VISITS ), a variable that

represents the fraction of total outpatient visits that are to PCPs.

Table 8 shows the IV estimates for all the above-mentioned robustness checks, where we report

only the estimates of the coefficients on CCI. We observe that, in general, our IV estimates do

not change in any meaningful manner.

Mental Health Setting. In addition to the above, we test our results in a mental health set-

ting, where continuity of care has been highlighted as critical to positive outcomes for patients

25

Table 8: Multiple robustness checks: IV estimates.

Description Condition IP_VISITS MEAN_LOS 30D_READ # patientsPCP_VISITS �2 -0.8428*** -0.2527 -0.3903*** 4,290,360

(0.1141) (0.8488) (0.0520) (443,999)Restricting PCP_VISITS 11 -2.2818*** -4.8679*** -0.7713*** 5,361,390PCP visits (0.2077) (0.9637) (0.0762) (481,450)

2 PCP_VISITS 11 -1.4757*** -2.2243* -0.5888*** 4,248,505(0.1748) (0.9849) (0.0721) (443,790)

CHARLSON=0 -0.7770*** 0.3532 -0.2957*** 3,237,803Restricting by (0.1143) (1.0206) (0.0483) (277,154)

Severity AGE60 -0.9956*** 1.5075 -0.3688*** 1,836,970AND/OR (0.1745) (1.3842) (0.0789) (154,552)

Age CHARLSON=0 & AGE60 -1.0304*** 1.7910 -0.3834*** 1,251,131(0.2521) (2.1411) (0.1084) (104,173)

Using past 3 quarters -2.6998*** -2.0941 -1.0786*** 5,119,544Calculation (0.4275) (1.9378) (0.1744) (478,556)

of CCI Using past 5 quarters -0.9306*** -0.9138 -0.3508*** 5,368,722(0.0787) (0.5503) (0.0336) (478,337)

PCP_VISITS & OP_VISITS -1.4937*** -2.3417** -0.5355*** 5,403,245Alternate (0.1244) (0.7216) (0.0509) (481,568)controls FR_VISITS & OP_VISITS -3.2474*** -7.5737*** -1.0667*** 5,403,245

(0.3335) (1.2845) (0.1185) (481,568)

Notes: We only report the coefficient on CCI . Robust standard errors in parentheses. # patients representsnumber of unique patients. *p < 0.05; **p < 0.01; ***p < 0.001.

with mental illness (Adair et al., 2005). We test hypothesis 1 on the dataset limited to mental

health visits and find CCI is negatively and significantly associated with all three outcomes. Ta-

ble 9 reports both the IV and the OLS estimates, where we note that, consistent with PCMDOC

data, IV estimates are two orders of magnitude bigger than OLS estimates.

4.4 Policy Implications

We first note that decreasing CCI by one standard deviation increases the average IP_VISITS

by 0.28 visits, and MEAN_LOS and 30D_READ on those visits by 0.32 days and 0.1 revisits,

respectively.

10This is equivalent to increasing the inpatient hospitalizations five-fold compared

to its mean value, doubling the average length of stay, and increasing the average thirty-day

readmissions 8.7 times. Considering that we have 6,855,243 observations, a 1� decrease in CCI

results implies an annual increase of 381,842 inpatient visits, or equivalently 116,364 inpatient

days. According to recent estimates, the average cost per inpatient day in the U.S. is $2,212.

11

10Consider a patient who makes 6 primary care visits in a year, with 5 visits to one provider and 1 visit to asecond provider. A one � change (=0.22) decrease in CCI is equivalent to the patient seeing both provider threetimes each.

11Source: http://kff.org/other/state-indicator/expenses-per-inpatient-day/

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Table 9: [Mental health visits] Summary statistics (top) and association between CCI and patient

outcomes: OLS and IV estimates (bottom).

OLS estimates (SE) IV estimates (SE)IP_VISITS MEAN_LOS 30D_READ IP_VISITS MEAN_LOS 30D_READ

CCI -0.0445*** -0.3916*** -0.0172*** -7.1055*** -61.5136*** -1.8395***(0.0028) (0.0404) (0.0016) (1.3853) (12.8112) (0.3887)

PCP_VISITS 0.0001 0.0090** -0.0002 -0.1374*** -0.8417*** -0.0361***(0.0002) (0.0032) (0.0001) (0.0270) (0.2496) (0.0077)

REST_VISITS 0.0006*** 0.0035*** 0.0002*** -0.0001 -0.0060* -0.0000(0.0000) (0.0003) (0.0000) (0.0003) (0.0024) (0.0001)

All Controls Yes Yes Yes Yes Yes YesTime Dummies Yes Yes Yes Yes Yes YesObservations 557,015 557,015 557,015 557,015 557,015 557,015Unique Patients 75917 75917 75917 75917 75917 75917R-squared 0.1021 0.0138 0.0403

Notes: Robust standard errors in parentheses. *p < 0.05; **p < 0.01; ***p < 0.001.

This translates into an increased cost burden of $257.4 million annually (95% CI: $211.3 million-

$303.5 million), on average.

12The cost related to increase in 30-day readmission rates are tied

to the penalty that the Centers for Medicare and Medicaid Services (CMS) imposes on hospitals.

Currently, it is a maximum 3% penalty for every 30-day readmission; for 2015, CMS estimates

total readmissions penalties to be $428 million.

13To estimate the cost impact due to efficiency

in effort and time, we first note that increasing CCI by one standard deviation decreases the

number of diagnostic tests by 58% and the days to see a specialist by 55%, on average. While

it is hard to estimate the economic benefit of time the visit to specialist, we estimate savings

for diagnostic tests based on the current cost estimates that could range from an average of $70

for a routine electrocardiogram to an average of $900 for an MRI.

14This results in an average

annual savings ranging from $83.9 million to $385.6 million.

5 Discussion

Our empirical findings, based on a unique, large, and comprehensive dataset, convincingly

demonstrate that continuity of care results in significant benefits. First, we find that conti-

nuity of care leads to substantial improvement in patient outcomes - fewer inpatient visits,

12The OLS estimates project more conservative estimates of increased cost burden of $9.1 million annually(95% CI: $6.4 million-$11.8 million), on average.

13Source: http://kff.org/medicare/issue-brief/aiming-for-fewer-hospital-u-turns-the-medicare-hospital-readmission-reduction-program/

14Source: http://www.bluecrossma.com/blue-iq/pdfs/TypicalCosts_89717_042709.pdf.

27

shorter length of stays on those visits, and lower readmission rate. Further, we are able to size

the gains from care continuity. These are important metrics from both operational and clinical

standpoints. Second, we find that the operational value derived from continuity of care is more

pronounced for sicker patients. Finally, we find that continuity of care results in a leaner, more

efficient operation as it leads to a reduction in waste both in the use of resources and in time.

We make several important contributions to the operations, coordination, and clinical lit-

eratures. Our first contribution is to the gatekeeper literature. While prior literature has

established the valuable role that gatekeeper systems play in improving quality and efficiency

in single-interaction settings (e.g., Shumsky and Pinker, 2003), our work establishes this value

in a setting with repeated interactions between the individual and the operational system. We

show that gatekeeper systems result in lower costs and improved quality. Further, we show that

the operational value offered by the continuity of care is increasing in task complexity since a

transfer of knowledge is harder and costlier for complex tasks (Singh et al., 2007). Our findings

offer guidance to managers in a resource-constrained operation, who seek to identify the optimal

allocation of a firm’s limited capital and labor resources.

Second, we make contributions to the clinical and medical literature in three ways. First, we

econometrically identify the precise effect of care continuity on patient outcomes. In particular,

we show that reducing continuity in care by one standard deviation would increase the average

number of hospitalizations five-fold, doubles the average length of stay per visit, and increases

the average rate of thirty-day readmissions nearly nine times. Our estimates assume particular

significance since they are based on data from the largest federally-funded integrated healthcare

system in the U.S., that is devoid of incentives that often plague private healthcare systems.

Further, we are able to precisely estimate the health and monetary costs of not providing care

continuity. Second, we identify an important moderator – patient severity, which offers insights

into how the providers should invest their efforts. Third, we are able to identify two mechanisms

through which continuity of care affects health outcomes. In particular, improving the continuity

of care by one standard deviation reduces the average number of diagnostic tests ordered by 58%

and shortens the average time to see a specialist by 55%.

Finally, we contribute to the literature on coordination. Using an instrumental variables

strategy, we are able to cleanly identify the effect of coordination on operational outcomes.

Further, we identify mechanisms through which coordination improves these outcomes; this not

only provides theoretical progress, but also offers managerial guidance. From a methodological

28

standpoint, we introduce a normalized measure that allows us to quantify coordination, thus

enabling future work on this topic - both empirical and analytical modeling. Overall, our work

contributes to both theory and practice in repeated interaction gatekeeper systems.

Limitations. Our study has limitations, many of them common to studies using observational

data. First, our data is limited to within the VA and does not include care that patients may

have received from providers outside of the VA. However, as we have demonstrated, our results

hold even if we restrict the data to patients that are “Medicare ineligible.” Further, the cost of

obtaining care is lower in the VA than outside, making it quite likely that most veterans obtained

their care primarily from within the VA. Consequently, we estimate the effect of omitting non-

VA data to be relatively small. Second, we aggregated our data into quarters, which prevents us

from analyzing trends at a more granular level. Aggregating data at a quarterly level allows us

to use 30-day supply threshold to classify a patient as a drug user, which is not possible at lower

levels of granularity. Third, our analyses and results are limited to primary care setting, where

care coordination typically takes place. However, we note that our tests on mental health setting

validate our results from PCMDOC data. Finally, we note that our cohort is predominantly

male. However, we believe that the policy implications from our study can be extended, with

adjustments, for the general population.

Directions for Future Research. There are a number of ways that future research could

and should follow up on our work. Within the healthcare setting, there is an opportunity to

empirically explore the effect of continuity of care on other important health outcomes such as

cardiovascular events that typically consume significant resources. Extending the analyses to

other areas where continuity is important would serve as a good direction for future work. An

example outside of healthcare is IT project management, where a project manager (gatekeeper)

is responsible for coordinating resources and repeatedly interacts with the client to learn about

her key requirements as the project evolves. There is also an opportunity to expand analytical

models of gatekeeper systems to include ongoing interactions and the continuity of care. Future

research should also explore additional mechanisms that are impacted by continuity of care and

the resulting operational consequences. This has the potential for contributions to both empiri-

cal operations and analytical modeling. Identifying the drivers that motivate care coordination,

both from theoretical and empirical standpoint could offer excellent contributions to the behav-

ioral operations. Understanding these drivers would help managers make appropriate cultural,

29

sociological, and psychological changes that enhance this motivation. Finally, understanding the

impact of providing care continuity on providers/gatekeepers themselves could serve as a fruitful

direction for future research.

Conclusion. In summary, our paper has important implications for practitioners, managers,

policymakers, and scholars. We establish the critical role that gatekeepers play in a system

where individuals have repeated interactions with the system. Within the primary care setting,

we show that continuity of care leads to substantial improvements in patient outcomes, more so

for sicker patients, and improvements in operational efficiency. Moreover, continuity can have a

significant impact on system costs not only in terms of improved outcomes but also the reduction

in waste. By shedding light on the mechanisms and the impact of continuity, our work offers

insights and guidance on optimal allocation of a firm’s limited resources, and ultimately, on how

better to design an operationally efficient system.

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