Strategic Information System Sourcing in the Presence
of Regulatory Pressures: The Case of Electronic
Health Record Systems
Submitted by:
Mr. Junmin Xu
School of Management
Xi’an Jiaotong University
No. 28 West Xianning Road, Xi’an City, Shaanxi Province, China
Department of Information Systems
City University of Hong Kong
83 Tat Chee Avenue, Kowloon, Hong Kong, China
junminxu2-c@my.cityu.edu.hk
TEL: +86 15394251810
Prof. Wei Thoo Yue
Department of Information Systems
City University of Hong Kong
83 Tat Chee Avenue, Kowloon, Hong Kong,China
Wei.T.Yue@cityu.edu.hk
TEL: +852 34429694
Dr. Alvin Chung Man Leung
Department of Information Systems
City University of Hong Kong
83 Tat Chee Avenue, Kowloon, Hong Kong, China
acmleung@cityu.edu.hk
TEL: +852 34428497
Prof. Qin Su
School of Management
Xi’an Jiaotong University
No. 28 West Xianning Road, Xi’an City, Shaanxi Province, China
qinsu@mail.xjtu.edu.cn
TEL: +86 029 82665043
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Strategic Information System Sourcing in the Presence of Regulatory Pressures: The
Case of Electronic Health Record Systems
Abstract
With rapid technological advancements and turbulent market dynamics, companies constantly
experience external pressures on information technology (IT) sourcing. However, little is known
about how companies make IT sourcing decisions, especially in terms of dealing with the
complexity stemming from the interdependence among different IT subcomponents, to adapt to
such pressures. This study provides insights by exploring hospitals’ sourcing strategies for
Electronic Health Record Systems (EHRS), a typical multicomponent system, in the presence of
regulatory pressures. A theoretical framework is developed to examine how hospitals change the
number of EHRS subcomponents (EHRS scope) and the diversity of their vendors (EHRS
diversity) after they perceive strong regulatory pressures on EHRS sourcing. Evidence from a
large sample of US hospitals through a difference-in-differences estimation shows that hospitals
widen EHRS scope and diversity in the face of regulatory pressures. The impacts of regulatory
pressures on EHRS scope and diversity vary depending on hospital size and case complexity. We
also find evidence that EHRS scope and diversity differentially mediate the impact of regulatory
pressures on patient outcomes. This study contributes to recent literature on IT sourcing
strategies by shedding light on how organizations strategically orchestrate IT portfolios to adapt
to external pressures. It also provides insights into the potential unexpected implications of these
strategic moves.
Keywords: Electronic Health Records (EHRs), institutional theory, institutional pressure, IT
sourcing, vendor selection
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1 Introduction
Electronic Health Record Systems (EHRS) have the potential to improve healthcare delivery
through evidence-based diagnosis and treatment and coordination of patient care (Wani &
Malhotra, 2018). Despite the benefits of EHRS, the high implementation costs of EHRS prevent
their widespread diffusion and investment (Jha, et al., 2009). In 2011, the Centers for Medicare
and Medicaid Services (CMS) established the Medicare and Medicaid EHR Incentive Programs also known as the “meaningful use” (MU) - to provide incentives to accelerate the adoption of
EHRS and their meaningful use. Hospitals are under great regulatory pressure to fulfill MU
requirements to earn incentive payments and avoid financial penalties. Consequently, MU
motivates a rapid diffusion of EHRS (Gopalakrishna-Remani, Jones, & Camp, 2019). By 2016,
over 95% of Medicare hospitals had met the preliminary requirements of MU1. As of July 2023,
more than 642,000 healthcare providers have enrolled in the programs.
Despite a surge in EHRS adoption driven by regulatory pressures, to date, the clinical benefits
of EHRS remain a subject of debate to this day (Appari, Johnson, & Anthony, 2015; Jones,
Heaton, Friedberg, & Schneider, 2011; Murphy, Wang, & Boland, 2020). A key challenge that
prevents hospitals from reaping the benefits of EHRS is the difficulty of efficiently exchanging
clinical data, often stemming from inadequately integrated IT infrastructure (Kwon & Johnson,
2018). Research has attributed the lack of IT integration to a disparity of IT systems sourced
from different vendors (Bardhan, Bao, & Ayabakan, 2022). To facilitate the connection of
individual modules within multicomponent systems like EHRS, a number of interfaces (e.g.,
application programming interfaces, APIs) should be properly deployed (Bardhan, et al., 2022;
Sanchez & Mahoney, 1996). Mixing disparate subcomponents not only demands that hospitals
1
https://www.healthit.gov/data/quickstats/hospitals-participating-cms-ehr-incentive-programs
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have robust IT capabilities to create interfaces customized for clinical workflows (Williams,
Mostashari, Mertz, Hogin, & Atwal, 2012), but it also requires significant resources for interface
monitoring and maintenance (Ruppel, et al., 2020). Hence, it is difficult and costly for hospitals
to achieve a high level of IT integration and interoperability when they source subcomponents
from distinct vendors.
When facing the pressures on EHRS sourcing from MU, hospitals may proactively engage in
different sourcing strategies, manifesting in two dimensions of EHRS portfolios. On the one
hand, they determine the number of EHRS subcomponents to be sourced, depending on the
extent to which they desire clinical processes to be digitized (Bardhan, et al., 2022; Wowak,
Handley, Kelley, & Angst, 2022). On the other hand, hospitals select suppliers for each
subcomponent and carefully weigh the tradeoffs associated with involving more or fewer
vendors in the portfolio (Wowak, et al., 2022). Although a single-vendor approach (also known
as single-sourcing) better integrates distinct subcomponents and facilitates interoperability
(Angst, Wowak, Handley, & Kelley, 2017), mixing EHRS subcomponents from different
vendors (also known as multisourcing or a “best-of-breed” approach) may also bring benefits
because it provides hospitals with affordable and agile solutions to accommodate diverse IT
demands from individual clinical units (Angst, et al., 2017).
This study captures a hospital’s sourcing strategies by introducing two IT portfolio-level
concepts: scope and diversity. Literature in IS sourcing has conceptualized the scope as the
number of IS functions an organization outsources to vendors (Lacity, Khan, & Willcocks, 2009;
Lacity & Willcocks, 1998). IT diversity has been conceptualized as the extent to which an
organization uses IT components from different vendors (Xue, Yang, & Yao, 2018). We extend
these concepts to the context of EHRS sourcing. We refer to EHRS scope as the count of
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subcomponents within an EHRS suite, whereas diversity is defined as the extent to which these
subcomponents diverge in their vendors. Our primary research objective is to examine how
hospitals change the scope and diversity of EHRS to comply with the regulatory pressures from
MU?
Research exploring the determinants of IT sourcing and vendor selection has focused mainly
on organizational internal factors, such as client characteristics (e.g., business objectives, cost
and security considerations, IS capabilities, and sourcing experience), vendor characteristics
(e.g., sourcing experience and locations), and contract-level details (e.g., technologies categories)
(Ang & Straub, 1998; Bapna, Gupta, Ray, & Singh, 2022; Handley, Skowronski, & Thakar,
2022; Lacity, et al., 2009; Lacity & Willcocks, 1998; Xue, et al., 2018; Zhang, Feng, Chen, Li, &
Li, 2021). Few studies have paid attention to the contextual factors beyond an organization’s
boundaries. Although a seminal work by Ang and Cummings (1997) argues that organizations
source IT from third-party vendors as a strategic response to accommodate institutional
pressures, it does not take into consideration the specifics of multicomponent systems. As a
result, an investigation of the impact of MU on the two dimensions of EHRS portfolios bears
great possibilities to advance a more holistic understanding of organizational IT sourcing
strategies.
We draw on institutional theory to explain how regulatory pressures influence the scope and
the diversity of a hospital’s EHRS portfolio. According to institutional theory, hospitals have to
conform to regulatory pressures to pursue legitimacy from the government (DiMaggio & Powell,
1983). Nevertheless, seeking legitimacy incurs substantial constraints and costs (Jeong & Kim,
2019; Meyer & Rowan, 1977). Hospitals are likely to pursue MU by deploying more EHRS
subcomponents to automate as many clinical workflows as possible. However, it would be
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costly, in terms of vendor selection and work routine changes, to minimize the diversity of
EHRS vendors. Therefore, when pressured to adopt EHRS, hospitals may strategically leverage
the scope and diversity of EHRS portfolios to earn legitimacy while avoiding high costs and
risks.
We further argue that the strategic sourcing of EHRS hinges on how hospitals assess the MU
pressures and associated compliance costs. Hence, we consider the moderating roles of hospital
size and case complexity. Larger hospitals face more external scrutiny and monitoring and are
more vulnerable to legitimacy loss (Luo, Wang, & Zhang, 2017). However, they may have more
slack resources to adhere to a minimum of vendors to achieve IT standardization and integration
(Sherer & Lee, 2002). Hospitals that handle complex patient cases often establish a
comprehensive array of specialized clinical units, resulting in a wide range of diverse IT
demands (Angst, et al., 2017). Disparate IT demands may increase the costs associated with
searching for and implementing unified EHRS solutions, making it challenging to adhere to a
minimal number of vendors.
We test our hypotheses on a sample of 3,469 hospitals through a difference-in-differences
identification strategy. The results indicate that hospitals increase the scope and diversity of
EHRS portfolios after the release of MU programs. Although larger hospitals are more likely to
enlarge the EHRS scope, they do not significantly change the diversity of EHRS. Hospitals with
higher case complexity are more likely to broaden their EHRS scope and diversity. These results
consistently show that hospitals make strategic sourcing decisions regarding EHRS portfolio
composition in order to conform to regulatory pressures at a minimal cost. The additional
analysis uncovers a negative influence of regulatory pressures on patient outcomes and distinct
mechanisms through which two EHRS portfolio dimensions mediate the relationship; whereas
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the diversity partially mediates the relationship, there is no evidence indicating the bridging role
of the scope.
This study contributes to the existing literature in several ways. Firstly, it adds to the
understanding of organizational IT sourcing strategies by taking into account the influence of
regulatory pressures. Specifically, we draw on the perspective of legitimacy management cost
from institutional theory to articulate how hospitals leverage the composition of EHRS portfolios
to respond to regulatory pressures on EHRS sourcing. These findings advance a more holistic
understanding of organizational IT sourcing strategies in the context of external pressures.
Secondly, the study integrates the scope and diversity dimensions of multicomponent systems in
the same framework and examines the distinct mechanisms behind each dimension. Our findings
indicate that hospitals adopt a nuanced approach in determining the composition of IT sourcing
portfolios in order to strike a balance between meeting external legitimacy requirements and
addressing internal constraints.
2. Background and Research Context
2.1 Electronic Health Record Systems (EHRS)
EHRS are multicomponent systems central to a hospital’s IT portfolio. With the electronic
documentation of medical records, EHRS allows healthcare providers to better document, track,
and manage their healthcare delivery. A typical EHRS includes several different but
interconnected subcomponents. The modular design of EHRS, in tandem with the lowconcentration and highly competitive vendor market, offers hospitals opportunities to either
choose integrated EHRS solutions by adhering to minimal vendors or adopt a “mix-and-match”
method to aggregate subcomponents from multiple different vendors (Angst, et al., 2017).
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As effective coordination of patient care lies at the heart of high-quality healthcare delivery,
hospitals must attend to the integration and interoperability across distinct subcomponents
(Khoumbati, Themistocleous, & Irani, 2006). A disparity of subcomponent vendors inevitably
causes difficulty and high costs of system integration, hindering the effective exchange of
clinical information (Bardhan, et al., 2022) and resulting in a decrease in patient care
(Khoumbati, et al., 2006). Echoing this contention, empirical studies have uncovered the
negative patient outcomes of a disparity of EHRS subcomponents from different vendors
(Wowak, et al., 2022). Therefore, our results regarding the scope and diversity impacts of MU
pressures on EHRS sourcing strategies may yield valuable insights.
2.2 Meaningful Use Programs
The MU programs were announced in 2009 and began in 2011 with the aim of facilitating the
adoption and effective use of EHRS and ultimately achieving the goal of improved care quality
and reduced healthcare costs. The MU initiative took a certificate regime through which billions
of dollars in incentive payments are distributed to healthcare providers who meet the
requirements of MU and have successful certification. MU is rolled out with a phase-in approach
across three stages2. The stage 1 MU (MU 1) certification, which began in 2011, places emphasis
on digitizing clinical workflows by investing in and harnessing the basic functionalities of
EHRS. The core requirements of MU 1 include using EHRS to capture medical information in
electronic format and to use the collected data for basic clinical functions such as patient
communication (Galbraith, 2013). The MU 1 lays a foundation for the objectives of MU stages 2
and 3, which concentrate on more advanced capabilities (e.g., care quality reporting and
healthcare information exchange). Although MU indicates the ideal status of EHRS in terms of
2
https://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/downloads
/mu_stage1_reqoverview.pdf
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interoperability across subcomponents, it, at least in the early few years (e.g., the MU 1 stage),
does not explicitly underscore the importance of system design and focus primarily on processbased evaluations (e.g., to what extent EHRS supports clinical workflows).
The amount of incentive funds hospitals receive for successfully attesting to MU 1 decreases
substantially over time (reducing by 25% of the original amount each year), and failure to
achieve MU 1 by the end of 2015 results in financial penalties in the form of Medicare
reimbursement loss. Hence, MU can be viewed as a strong regulatory force on EHRS sourcing,
and it is always in the best interest of hospitals to attest to MU as soon as possible (Wani &
Malhotra, 2018).
3. Theoretical Background
3.1 Institutional Theory
Institutional theory provides an overarching framework regarding how organizational
practices are influenced by pressures in the institutional environment. An institutional
environment encompasses the cultural belief systems, normative frameworks, and regulatory
systems that specify desirable organizational behavior through norms, laws, regulations, and
social expectations (Scott, Ruef, Mendel, & Caronna, 2000). Organizations comply with
institutional pressures because they do not only compete for technical goals of efficiency but also
for legitimacy, defined as the extent to which an organization’s behavior is endorsed and
perceived as appropriate by its stakeholders (Suchman, 1995). Obtaining legitimacy is critical as
it is associated with external resources and social support necessary for organizational survival
and prosperity.
Despite the importance of legitimacy, institutional theory also argues that there might be
inconsistencies between institutional expectations and internal organizational factors such as
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operational needs, motivations, and capabilities (Bromley & Powell, 2012; Oliver, 1991), and
passive acquiescence to institutional pressure may incur costs and constraints that undermine
operational activities. Hence, it is suggested that organizations may actively manage the
legitimacy costs to balance the tensions between institutional expectations and internal goals and
limitations (Jeong & Kim, 2019).
The perspective of legitimacy management cost provides a crucial theoretical anchor to
understand hospitals’ EHRS sourcing strategies in response to regulatory pressures. In the
healthcare industry, the government is a primary source of legitimacy, exerting pressure on
hospital practices. Additionally, the US government holds significant influence as it determines
reimbursement rates for Medicare and Medicaid, making it a vital source of external resources
for hospitals. Consequently, hospitals are pressured to showcase compliance with government
regulations and guidelines. This is particularly the case of MU. By promptly adopting EHRS and
demonstrating MU, hospitals can avoid financial penalties and maximize the received monetary
support. However, as compliance with regulatory pressures may entail considerable operational
risks and expenses, hospitals are likely to employ strategic measures to manage the costs of
obtaining legitimacy.
3.2 Scope and diversity dimensions of EHRS portfolios
This study focuses on the scope and diversity dimensions of EHRS portfolios to unveil a
hospital’s sourcing strategies when facing regulatory pressures on EHRS sourcing. An increase
in the scope may be representative of a higher degree of automation and digitization. However, a
diversity of subcomponents from different vendors indicates the difficulty and high costs of IT
standardization and interoperability (Bardhan, et al., 2022), because hospitals have to possess
strong IT capabilities to develop interfaces (Williams, et al., 2012) and expend considerable
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resources for their monitoring and maintenance (Ruppel, et al., 2020). Research also suggests
that subcomponents from different vendors cause structural complexity (Xue, et al., 2018),
giving rise to undesirable outcomes such as security vulnerability (Li & Yoo, 2022). These
difficulties and complexity are further amplified when hospitals are increasingly migrating to
cloud-based services, which feature implicit knowledge that complicates the design and
assessment of the entire IT infrastructure (Handley, et al., 2022).
The diversity of an IT portfolio does not necessarily correlate with its scope (Bapna, et al.,
2022). As the scope of a multicomponent system enlarges, an organization can maintain a low
diversity of the portfolio by finding IT vendors who can provide integrated solutions that
synergize various IT demands from different organizational units (Xue, et al., 2018).
Nevertheless, reducing vendor diversity comes with substantial resource requirements and
limitations, requiring not only an extensive search for suitable vendors but also significant
alterations to existing work routines (Angst, et al., 2017; Xue, et al., 2018).
When facing regulatory pressures on EHRS sourcing, hospitals are likely to leverage both the
scope and diversity of EHRS as responses. On the one hand, they are likely to source more
EHRS subcomponents to achieve a high level of digitization, which is a primary objective of MU
1. On the other hand, since MU 1 does not particularly stress data exchange and interoperability
capabilities, minimized vendor diversity may contribute little to compliance while incurring
considerable costs in searching for suitable vendors and changing existing clinical workflows.
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4 Hypothesis Development
4.1 Regulatory pressures and EHRS sourcing strategy
Our first hypothesis pertains to the relationship between regulatory pressures and the scope
and diversity of hospitals’ EHRS portfolios. As we argued earlier, MU represents strong
legitimacy pressure from the government. Hospitals have to adhere to the regulatory pressures
from the MU as soon as possible so that they can receive more incentive payments and avoid
potential financial penalties. Specifically, MU 1 outlines a list of clinical processes that must be
digitized for successful attestation while also recommending the digitization of optional
processes. Hence, expanding the scope of EHRS through the implementation of additional
subcomponents is beneficial for hospitals in meeting the requirements of MU 1.
Since the primary objective of MU is to improve patient outcomes and healthcare efficiency,
it is advantageous for hospitals to strive for high IT standardization for smooth data exchange.
An appealing approach is to minimize the diversity of EHRS subcomponents and adhere to a
limited number of vendors (Bardhan, et al., 2022). However, doing so incurs great costs and risks
for two reasons. First, hospitals have to perform a complete search of appropriate vendors that
are able to provide unified solutions that synthesize diverse IT demands from a variety of clinical
units (Xue, et al., 2018). Such a search is challenging as it requires endeavors for careful
evaluation and comparison of complex technique specifications of integrated EHRS solutions
from numerous vendors. Second, since it is costly and time-consuming to find integrated EHRS
solutions well aligned with existing clinical processes, it seems appealing for hospitals to adapt
operations to their best-known or “best practice” unified solutions in the market. Nevertheless,
due to the potential gaps between these solutions and actual clinical workflows, existing clinical
processes must be reengineered to align with the capabilities of unified EHRS solutions (Ray,
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Muhanna, & Barney, 2005), which causes high costs of adaption and huge risks of operation
disruption, and even the failure of the whole EHRS implementation project. These costs arise
because physicians often develop workflow routines based on their own experience, expertise,
and personal preferences. When physicians cannot choose their preferred EHRS solutions, and
serious reengineering efforts are taken due to the implementation of unified EHRS, these
established patterns and routines are largely broken. This may raise concerns among physicians
about their autonomy and professional independence, leading to their backlashes.
Furthermore, the costs and risks due to the search for suitable vendors and the reengineering
of existing clinical processes can be elevated when hospitals face intense pressure manifesting in
a short time window for attestation, quickly diminished incentive payments, and later financial
penalties. Hence, we posit that regulatory pressures on EHRS sourcing may result in an increase
in both the scope and diversity of a hospital’s EHRS portfolio. Indeed, the observations show
that despite a noteworthy surge in EHRS adoption subsequent to the release of MU programs,
many hospitals are found to implement EHRS in a poorly integrated manner with a diversity of
incompatible subcomponents hardly to exchange information cohesively (Kwon & Johnson,
2018). Therefore, we hypothesize that:
H1a: Regulatory pressures on EHRS sourcing are positively related to a hospital’s EHRS scope.
H1b: Regulatory pressures on EHRS sourcing are positively related to a hospital’s EHRS
diversity.
4.2 Moderating effects of hospital size
Research has identified size as a critical predictor of an organization’s IT sourcing strategies
(Nam, Rajagopalan, Rao, & Chaudhury, 1996). A crucial argument is that organizational size is
correlated with slack resources – i.e., larger organizations have more resources and are more
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capable of undertaking the IT sourcing strategies they desire (Ang & Straub, 1998). Meanwhile,
the institutional theory suggests that larger organizations experience more legitimacy pressure
and are more susceptible to legitimacy loss due to more stringent external scrutiny and
monitoring (Luo, et al., 2017). Compared to small hospitals, large hospitals feel amplified
pressure to conform to the regulatory pressures from the government. Hence, we argue that large
hospitals are more motivated to enlarge their EHRS scope to satisfy the requirements of MU and
seek regulatory compliance. Furthermore, as large hospitals have more resources and capabilities
to find suitable vendors and withstand the disruptive and costly adaption to unified EHRS
solutions, they are more capable of sticking to a few vendors such that they can achieve high IT
standardization and integration. Hence, we hypothesize that:
H2a: Hospital size strengthens the positive relationship between regulatory pressures and EHRS
scope.
H2b: Hospital size weakens the positive relationship between regulatory pressures and EHRS
diversity.
4.3 Moderating effects of case complexity
Next, we hypothesize how a hospital’s case complexity moderates the relationship between
regulatory pressures and EHRS scope and diversity. Case complexity refers to the extent to
which hospitals treat patients with different clinical conditions (Angst, et al., 2017). Hospitals
dealing with complex cases often opt to divide their clinical processes into specialized units (e.g.,
cardiology, oncology) (Greenwood & Hinings, 1996), resulting in a wide range of diverse IT
requirements (Greenwood & Hinings, 1996). As we mentioned, MU mandates a relatively high
degree of hospital-level digitization by entailing a comprehensive list of clinical processes that
must be digitized, along with recommended ones. To fulfill this requirement, hospitals with high
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case complexity are likely to resort to an enormous scope of EHRS. Furthermore, high case
complexity makes it challenging to adhere to a minimal number of vendors. This is because a
significant diversity of IT demands increases the costs and risks of striving for high IT
standardization by searching for and adapting to unified EHRS solutions. For example, it would
be more difficult for a hospital to find ideal vendors of unified EHRS solutions if it has a large
scope of customized IT needs. Reengineering clinical processes across highly differentiated
clinical units poses a significant challenge to hospitals, as it necessitates not only substantial
efforts in breaking well-established work routines but also triggers backlashes from physicians.
Hence, we hypothesize that:
H2a: Case complexity strengthens the positive relationship between regulatory pressures and
EHRS scope.
H2b: Case complexity strengthens the positive relationship between regulatory pressures and
EHRS diversity.
5 Methods
5.1 Data and sample
We compile a panel data set on a sample of 3,469 hospitals from 2006-2012. The selected
time frame is suitable as MU programs were initially released in February 2009 and commenced
in 2011. We obtain IT-related data from the Healthcare Information and Management Systems
Society (HIMSS) database. Other hospital and hospital-system information is collected from
both HIMSS and the Centers for Medicare and Medicaid Services (CMS) Hospital Compare
databases. The two databases are merged with Medicare number, a unique 6-digit identifier
allocated by CMS for each hospital.
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5.2 Measures
Dependent variables. Our dependent variables are the scope and diversity of a hospital’s
EHRS portfolio. Our calculation is based on the five major EHRS subcomponents (i.e., Clinical
Decision Support System, Computerized Practitioner Order Entry, Physician Documentation,
Order Entry, and Clinical Data Repository) suggested by previous studies (Angst, et al., 2017;
Bardhan, et al., 2022). We deem a specific subcomponent as adopted only if HIMSS reports its
status as “live and operational.” We measure the scope by counting the number of installed
subcomponents within an EHRS suite. To calculate the diversity, we draw on a method by
(Angst, et al., 2017) to examine how close a focal EHRS sourcing portfolio is to a prototypical
multisourcing configuration (i.e., a maximum of diversity). To achieve this, we first determine
the dominant vendor with a suite. Then we compute how many vendors should be changed to
achieve a prototypical single-sourcing configuration (i.e., a minimum of diversity). Next, we
divide the result by the total number of adopted subcomponents to obtain a normalized value
(from 0-1).
Moderating variables. We have two moderating variables: case complexity and hospital size.
We use case complexity to capture the extent to which hospitals treat patients with diverse
clinical conditions. We operationalize the case complexity as the case mix index (CMI), which
refers to the diversity of resources required to treat different types of patients (Rosko &
Chilingerian, 1999). CMI reflects a hospital’s case complexity in previous literature and is
suitable for comparison across hospitals due to its normalized nature (Angst, et al., 2017). The
hospital size is measured by the natural logarithm of the number of staffed beds.
Control variables. A set of hospital and hospital system-level variables that may influence the
scope and diversity of EHRS portfolios are controlled. Many hospitals in the US are affiliated
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with hospital systems. It is probable that the sourcing decisions of individual hospitals are
influenced by the centralized decisions made by their system managers. Therefore, we control
for the size of the hospital system, measured by the natural logarithm of the total number of
hospitals that belong to the focal system. Hospital age is the natural logarithm of the years since
a focal hospital was founded. Academic status captures whether a hospital undertakes academic
tasks (0 or 1). Hospital region is a dummy set as 1 if a hospital is located in an urban area and 0
in a rural area. Ownership status indicates whether a hospital is owned or managed (0 or 1).
5.3 Difference-in-Differences Method
The estimation strategy of this study is a difference-in-differences (DID) approach. The DID
approach is suitable for our research objectives because the regulatory pressures embodied in
MU programs can be viewed as an exogenous shock that prompts hospitals to source EHRS as a
response. The advantage of a DID approach is to mitigate the endogeneity concerns due to
confounding time effects. We specify the following equations to estimate the impact of
regulatory pressures:
𝐷𝑉𝑖𝑡 = 𝛼(𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑖 × 𝑃𝑜𝑠𝑡𝑖 ) + 𝜷𝑪𝒐𝒏𝒕𝒓𝒐𝒍𝒔𝒊𝒕 + 𝛾𝑖 + 𝛿𝑡 + 𝜀𝑖𝑡
(1)
In this equation, 𝑖 denotes an individual hospital, and 𝑡 denotes a year period. 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 is a
dummy that indicates whether a hospital is in a treatment group (i.e., hospitals more prone to
regulatory pressures). Since the official attestation of MU started in 2011, we view those
hospitals attaining MU in and before 2012 as more prone to regulatory pressures (i.e., treatment
hospitals) and those attesting after 2012 as less prone to the pressures (i.e., control hospitals).
𝑃𝑜𝑠𝑡 is a dummy that takes a 0 for the periods before the release of MU programs and a 1 for the
periods after. 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 is a vector of control variables. Furthermore, we include the hospital-
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fixed effect denoted by 𝛾 and time-fixed effects denoted by 𝛿. We also estimate the following
models on top of equation (1) to test the moderating effects of case complexity and hospital size.
𝐷𝑉𝑖𝑡 = 𝛼(𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑖 × 𝑃𝑜𝑠𝑡𝑖 ) + 𝜇(𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑖 × 𝑃𝑜𝑠𝑡𝑡 × 𝑀𝑜𝑑𝑒𝑟𝑎𝑡𝑜𝑟𝑖 ) +
𝜃𝑀𝑜𝑑𝑒𝑟𝑎𝑡𝑜𝑟𝑖 + 𝜷𝑪𝒐𝒏𝒕𝒓𝒐𝒍𝒔𝒊𝒕 + 𝛾𝑖 + 𝛿𝑡 + 𝜀𝑖𝑡
(2)
5.4 Propensity Score Matching
A major endogeneity concern over our estimation is that the selection of hospitals into
different groups might not be random. Several hospital characteristics are likely to influence the
willingness of hospitals to attest to the MU sooner or later. Hence, we leverage the Propensity
Score Matching (PSM) approach to create similar hospital pairs in terms of size, age, CMI,
academic status, and hospital system size. We chose these hospital characteristics because they
predict a hospital’s susceptibility and responses to regulatory pressures. Each hospital pair
contains a treatment hospital and a comparable control hospital.
5.4 Results
We report the descriptive statistics and correlations for all variables in Table 1. Before
running any analyses, we calculate the variance inflation factors (VIFs) for all individual
variables and models. No VIF is larger than 1.9, indicating no severe threats of multicollinearity.
To better understand the trend of scope and diversity over the entire sample frame, we estimate a
dynamic model based on equation (1) by separating the 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 variable into year dummies
(2007-2011). The results in Table 2 show that EHRS scope experienced a significant increase
after 2009. The trend of EHRS diversity is downward before 2009, which echoes the finding that
hospitals migrate from multisourcing to single-sourcing in orchestrating their EHRS portfolio
(Angst, et al., 2017). However, we witnessed a significant eruption in diversity in the year 2009,
the time when MU programs were initially released.
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[Insert Tables 1 and 2 Here]
Table 3 lists the estimates from the DID regressions of EHRS sourcing strategies without
PSM matching. These estimates show that after the release of MU programs, hospitals more
prone to regulatory pressures attain a significant increase in scope (𝛽 = 0.6710, 𝑝 < 0.01 in
Model 2) and diversity (𝛽 = 0.0151, 𝑝 < 0.01 in Model 6). Hence, our H1a and H1b are
supported. The interaction term of Treat×post×Size in Model 4 is significant and positive (𝛽 =
0.0896, 𝑝 < 0.05), suggesting that larger hospitals source more EHRS subcomponents after the
release of MU programs. Therefore, H2a is supported. We do not find evidence that hospital size
moderates the relationship between the regulatory pressures and EHRS diversity because the
interaction term of Treat×post×Size in Model 8 is non-significant (𝛽 = 0.0050, 𝑝 > 0.1). Hence,
H2b is not supported. The interaction terms of Treat×post×CMI in Model 3 (𝛽 = 0.4929, 𝑝 <
0.01) and Model 7 (𝛽 = 0.0430, 𝑝 < 0.01) are both significant and positive, indicating that
hospitals with higher case complexity move towards an EHRS portfolio with larger scope and
higher diversity after MU programs release. Hence, our H3a and H3b are supported. Table 4 lists
summary statistics for matched and unmatched samples. Table 5 reports the estimates from the
DID regressions of EHRS sourcing strategies with the PSM sample, which show support for our
main results.
[Insert Tables 3-5 Here]
5.5 Robustness Analysis
One may argue that the scope and diversity of an EHRS portfolio are somewhat correlated,
especially for an EHRS portfolio with few subcomponents. Also, it could be disputed whether
EHRS portfolios with few subcomponents are genuinely indicative of vendor selection strategies
(Angst, et al., 2017). Hence, we reestimate equation (1) on a sample excluding hospital-year
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observations with 0 or 1 adopted subcomponents. The results reported in Table 6 are essentially
unchanged, validating the robustness of our main results.
[Insert Table 6 Here]
5.6 Mediation Analysis
We conduct the mediation analysis to identify whether the changes in the scope and diversity
of a hospital’s EHRS portfolio resulting from regulatory pressures have impacts on patient
outcomes. The patient outcome is operationalized as an average of 30-day individual mortality
rates for heart failure, pneumonia, and heart attacks. We take a three-step approach to evaluate
the mediating roles of scope and diversity in the relationship between regulatory pressures and
the mortality rate. First, we regress the mortality rate on the Treat×post to identify the direct
impact of regulatory pressures on the mortality rate. Second, we regress the scope (diversity) on
Treat×post. Third, we run the regressions with the mortality rate as the dependent variable and
both Treat×post and the scope (diversity) as the independent variables. The significance of the
coefficients of Treat×post and the scope (diversity) indicates the presence or absence of the
mediation pathways. Consistent with the insights from previous literature, our results in Table 7
hint at the positive influence of regulatory pressures on the mortality rate (𝛽 = 0.0904, 𝑝 < 0.01
in Models 1 and 4). The coefficient of the scope in Model 3 is insignificant (𝛽 = 0.0403, 𝑝 >
0.1 in Model 3), suggesting that EHRS scope does not mediate the relationship between
regulatory pressures and the mortality rate. The coefficients of both diversity (𝛽 = 0.0120, 𝑝 <
0.05 in Model 6) and Treat×post (𝛽 = 0.0844, 𝑝 < 0.01 in Model 6) are positive and
significant, indicating a partial mediation role of the diversity in the relationship between
regulatory pressures and the mortality rate.
[Insert Table 7 Here]
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6 Discussion
Our empirical analysis yields several interesting and significant insights. First, the results
suggest that the regulatory pressures on EHRS sourcing positively impact the scope and diversity
dimensions of hospitals’ EHRS portfolios. These findings support our hypotheses that hospitals
respond to regulatory pressures by making strategic sourcing decisions regarding EHRS portfolio
composition to meet different objectives. On the one hand, given that deploying more EHRS
subcomponents is conducive to conforming to regulatory pressures by fulfilling the requirements
of MU, hospitals are likely to make an effort to enlarge the scope of their EHRS portfolios. On
the other hand, hospitals are less willing to maintain a low vendor diversity because doing so
incurs considerable costs and risks in terms of a complete search of appropriate vendors that are
able to synthesize diverse IT demands from various clinical units and potential organizational
changes to align existing clinical routines with unified EHRS solutions. In other words, hospitals
tend to demonstrate regulatory compliance and obtain legitimacy from the government in a costefficient and affordable manner.
Second, the results suggest that larger hospitals are more likely to enlarge their EHRS scope
to seek regulatory conformance. This aligns with our arguments that larger hospitals experience
more legitimacy pressure due to intensified external scrutiny and monitoring. Furthermore, we
hypothesize that hospital size negatively moderates the positive association between the
regulatory pressures and EHRS diversity because larger hospitals have more slack resources and
capabilities to adhere to a minimum of vendors to strive for IT standardization and withstand the
disruptive and costly adaption to unified EHRS solutions. However, our results do not show
consistent support. This is comprehensible as larger hospitals, despite their ability to limit the
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number of vendors for IT standardization, often face significant inertia that hinders them from
altering established decision patterns (Kelly & Amburgey, 1991).
Third, we find evidence that hospitals dealing with more complex patient cases are more
likely to experience an increase in the scope and diversity of their EHRS portfolios. This is
consistent with our contention that high-complexity hospitals possess a broad range of disparate
IT demands because they frequently differentiate into multiple specialized clinical units. In the
presence of regulatory pressures, on the one hand, they need to enlarge the scope of EHRS to
digitize more clinical workflows in order to show regulatory compliance. On the other hand, a
diverse base of IT demands makes it less likely to find and adhere to a minimal set of different
vendors. Finally, although no formal predictions are made, we uncover a negative influence of
regulatory pressures on patient outcomes. Our mediation analysis confirms EHRS diversity,
rather than EHRS scope, as a partial mediation channel. This indicates that a high level of EHRS
diversity, as a result of regulatory pressures, is associated with inferior patient outcomes. These
results are reasonable because the difficulty and high integration costs due to disparate
subcomponents from different vendors undermine interoperability and care coordination, which
lie at the heart of healthcare delivery.
7 Theoretical Contribution
This study makes several contributions. First, we contribute to the IT sourcing literature by
examining how regulatory pressures influence hospitals’ IT sourcing strategies. Previous studies
have focused primarily on internal organizational characteristics and contract-level factors to
explore whether and how organizations make IT sourcing arrangements (Ang & Straub, 1998;
Lacity, et al., 2009; Lacity & Willcocks, 1998; Liang, Wang, Xue, & Cui, 2016). Although a
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stream of literature shifts its research focus to the elements in the external institutional
environment and conceptualizes sourcing strategies as strategic responses to institutional
influences (Ang & Cummings, 1997), little is known about how detailed sourcing strategies are
orchestrated in this case. We complement this literature by drawing on the theoretical perspective
of legitimacy management cost from institutional theory to uncover how hospitals leverage
EHRS portfolio composition to respond to institutional pressures on EHRS sourcing. Our results
indicate that hospitals use nuanced sourcing strategies to balance the tradeoffs between
legitimacy pressures and internal constraints in pursuing legitimacy. We also contribute to the
literature on strategic IT sourcing by examining the outcome implications. The results show that
strategic IT sourcing as a response to regulatory pressures may not be conducive to improved
operational performance, possibly due to the difficulty and high costs required for IT
standardization and integration. In sum, the findings underscore the importance of considering
both external and internal dynamics, as well as non-economic objectives (e.g., the pursuit of
legitimacy) in understanding an organization’s IT sourcing strategies.
Second, this study introduces two dimensions of IT portfolios (i.e., scope and diversity) and
explores the distinct mechanisms behind each dimension. Previous literature has considered what
makes organizations 1) source more or fewer IT services or 2) use more or fewer vendors in a
given suite (Angst, et al., 2017; Aral, Bakos, & Brynjolfsson, 2018; Bapna, et al., 2022; Handley,
et al., 2022; Xue, et al., 2018). Few studies have considered the scope and diversity dimensions
in an integrated framework and provided theory-based explanations about how external pressures
impact them. Our findings suggest that hospitals proactively alter both dimensions in the
presence of external institutional pressures. On the one hand, hospitals are facing regulatory
pressures to enhance digitization, which has led to an expansion in the scope of EHRS. On the
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other hand, strong regulatory pressures make hospitals forgo the opportunities to maintain
meaningful relationships with a minimal set of vendors offering more integrated EHRS suites.
Furthermore, we uncover internal hospital dynamics, i.e., size and case complexity, as
contingency factors that shape the two EHRS dimensions in response to regulatory pressures.
Notably, our results of more diverse vendor selection contrast with the findings of a longitudinal
study which finds that hospitals generally migrated from multisourcing to a single-sourcing
approach in sourcing EHRS from 2005-2013 (Angst, et al., 2017). Our results do not challenge
their views because we take into account a unique source of institutional pressure stemming from
administrative forces. In this case, hospitals may prioritize fast and affordable regulatory
conformance over technical benefits, thus abandoning a more seamlessly integrated system
design. Hence, we suggest that an organization’s sourcing strategies in the face of external
pressures are likely to be context-specific, depending on the strength and source of the pressures.
Third, we complement the literature on how institutional pressures influence organizational IT
adoption. Research has, in general, increasingly recognized institutional influence on
organizational IT adoption. However, it is documented that the strategies of organizations to
adopt IT under pressure may vary, leaving the benefits of IT uncertain. To explore this problem,
a large stream of literature has focused on either IT adoption (i.e., the extent of adopting IT) or
IT assimilation (i.e., the extent of using newly adopted IT to support core operations) (Barratt &
Choi, 2007; Dang & Pekkola, 2020; Gosain, 2004; Hsu, Lee, & Straub, 2012; Liang, Saraf, Hu,
& Xue, 2007). We complement these studies by shifting the research focus to detailed IT
adoption strategies. Our findings hint that organizations may strategically leverage their sourcing
portfolio composition to respond to institutional pressures, which, in turn, determines the
benefits of IT investment.
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Fourth, we contribute to the IS and healthcare management literature that aims to understand
the performance implications of MU programs and resultant EHRS adoptions. Despite millions
of dollars distributed by MU programs to incentivize the adoption and effective use of EHRS for
improved patient outcomes, the empirical findings regarding their outcome implications remain
mixed (Appari, et al., 2015; Jones, et al., 2011; Kim & Kwon, 2019; Kwon & Johnson, 2018;
Murphy, et al., 2020). Research has shed light on the mixed findings with a focus on the degree
of EHRS adoption (e.g., full or partial) and the extent to which EHRS are assimilated to meet
MU requirements (Baird, Davidson, & Mathiassen, 2017; Trout, Chen, Wilson, Tak, & Palm,
2022; Wani & Malhotra, 2018). This study provides new insights to reconcile the conflicting
insights. We suggest that the possible difficulty of interoperability that emanates from sourcing
EHRS subcomponents from different vendors serves as a barrier for hospitals to reap the benefits
from EHRS.
8 Practical Implications
Our findings carry crucial implications for the government, hospital managers, and EHRS
vendors. First, as the U.S. healthcare system is increasingly pursuing value-based care in the
hopes of improved patient outcomes and reduced healthcare costs, a central theme is how to
achieve coordination of care delivery through the deployment and effective use of EHRS. Hence,
the US government has been dedicating considerable resources and leveraging its administrative
power to facilitate the adoption and meaningful use of EHRS. However, our findings call into
question whether the increasing adoption of EHRS leads to an effective exchange of clinical
information and interoperability. These findings echos the observations from the practitioner3:
3
https://www.healthcare-informatics.com/article/value-based-care/healthit-summit-boston-one-aco-s-datafacilitated-journey-value
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“…[the] data resides in different systems. We have 46 different EHRs, and 150 different
installs. There is data in core EHRs, but also in lab, radiology, and infusion systems. You want to
try to get your hands on all that data, and that can be a challenge in and of itself. … All systems
store data differently, and there is no fully embraced HIE standard. What’s more, an overall,
industry-wide vision of interoperability has still not been achieved. And there remains a lack of
vendor support, as well as fragmented access to EHR data….”
A potential explanation suggested by our results is that hospitals tend to source EHRS
subcomponents from diverse vendors as an affordable and less risky approach in pursuit of
regulatory compliance. Given that EHRS function with complex interdependence among
subcomponents and subcomponents from different vendors are difficult to integrate and
communicate, we advocate the government to encourage a more careful and thorough search of
suitable vendors that accommodate as many diverse IT demands as possible. To that end, it
might be beneficial to ensure that hospitals bear in mind the ultimate purposes of MU and shift
the MU from a process-based attestation to an outcome- and function-based evaluation. Besides,
rushing the implementation of EHRS with a high-pressure approach and tight timeline may not
be advisable. Successfully implementing complex systems like EHRS requires a thorough search
for integrated solutions, as well as significant operational and adaptive changes that can be timeconsuming, costly, and risky. In addition, implementing such organizational changes may require
hospitals to go beyond their current IS capabilities and seek assistance from third-party vendors
and IT consultants to achieve success. Therefore, it is advisable for the government to allow for a
relatively long-term plan for EHRS adoption and allocate consistent resources and support over a
substantial time frame.
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Second, we advise hospital managers to be wary of sourcing IT services from multiple
different vendors. Although such an approach is appealing for adhering to the government
pressure on digitization, a diversity of vendors and the resultant interoperability barriers could be
detrimental to patient outcomes. Hence, hospital managers should carefully balance the tradeoffs
of engaging more or fewer vendors in their EHRS portfolios. Third, this study has implications
for EHRS vendors. While hospitals strive to benefit from a limited number of EHRS vendors,
they often face challenges in finding suitable vendors and adapting to unified solutions. In light
of this, EHRS vendors should consider expanding their business scope to offer integrated EHRS
solutions encompassing a wide range of clinical processes. Additionally, it is wise to offer
customized options for each EHRS subcomponent to adequately address the vastly different IT
needs of various hospitals.
9 Limitations
This study has several limitations. First, we examine the impact of regulatory pressures on
EHRS sourcing using a DID identification strategy leveraging the release of MU programs. To
avoid the confounding factor associated with time, we differentiate between treatment and
control hospitals according to hospitals’ timing of successful MU attestation. We admit that this
method could lead to estimation bias because an early attestation of MU may not indicate that
hospitals are more susceptible to regulatory pressures. Although we try to mitigate these
concerns by harnessing matched samples based on observable confounding factors, it is difficult
to take into consideration other unobservable confounding factors. Future studies may tackle the
problem with more robust research designs. Second, our additional analysis uncovers the
negative impact of regulatory pressures on patient outcomes and unveils the partial mediator role
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of EHRS diversity. However, we do not have more information to probe into the context of
EHRS use (e.g., EHRS assimilation). The variable omission could potentially bias the results.
10 Conclusion
After billions of dollars distributed by the US government to incentivize the adoption and
effective use of EHRS, hospitals are reportedly plagued by fragmented IT infrastructure and low
IT interoperability. Our research suggests that although regulatory pressures result in a quick
diffusion of EHRS, manifesting in an increase in EHRS scope, hospitals, especially those dealing
with complex patient conditions, tend to source EHRS subcomponents from more diverse IT
vendors. These findings suggest that hospitals may resort to a diversity of EHRS vendors to
reduce the cost of regulatory compliance. However, these benefits may come at the expense of a
lack of IT integration and standardization, which undermines patient outcomes. Our study
provides novel insights into how organizations make strategic moves in orchestrating IT
portfolios in response to external pressures on IT sourcing.
Declarations
Declaration of conflicting interest
The authors have no relevant financial or non-financial interests to disclose.
Funding
The authors did not receive support from any organization for the submitted work.
Data availability
Data are available on request from authors.
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Ethical Approval
Ethics approval was not required for this study because no human or animal subjects were
involved.
Authors' contributions
JX conceived of the study, collected archival data, performed the statistical analysis, and
drafted the manuscript. WTY conceived of the study, participated in its design and coordination,
and helped to draft the manuscript. ACML participated in the design of the study and performed
the statistical analysis. QS participated in the design of the study and helped to edit the
manuscript. All authors read and approved the final manuscript.
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Tables
Table 1. Descriptive statistics and correlations analysis
Variables
Mean
SD
1
2
3
4
5
6
1 Scope
3.152 1.649 1.000
2 Diversity
0.074 0.142 0.295 1.000
3 Size
5.153 0.844 0.182 0.178 1.000
4 Age
3.149 1.008 0.087 -0.024 0.134 1.000
5 CMI
1.404 0.274 0.157 0.179 0.634 0.010 1.000
6 Region
0.725 0.446 0.087 0.137 0.472 -0.047 0.465 1.000
7 Academic
0.079 0.269 0.108 0.075 0.365 0.070 0.333 0.172
8 Ownership 2.932 0.328 0.043 0.032 0.091 0.129 0.076 0.058
9 System size 1.660 1.644 0.011 0.092 0.059 -0.473 0.118 0.104
Notes: The correlation coefficients marked in bold are significant at p < 0.05
7
8
9
1.000
0.043
-0.081
1.000
-0.144
1.000
Table 2. Estimates from the dynamic model estimation
EHRS scope
EHRS diversity
Year 2007
-0.0634
-0.0107*
(-1.052)
(-1.914)
Year 2008
0.1545**
-0.0117**
(2.540)
(-2.066)
Year 2009
0.3956***
0.0188***
(6.498)
(3.327)
Year 2010
0.3749***
-0.0044
(6.133)
(-0.770)
Year 2011
1.0315***
0.0075
(19.315)
(1.522)
Hospital effects
yes
yes
Year effects
yes
yes
Cluster by hospital
yes
yes
31
Electronic copy available at: https://ssrn.com/abstract=4618875
Obs.
21,761
21,761
Adj. R2
0.482
0.493
Notes: Robust t-statistics are reported in parentheses; All control variables are included but not displayed for
conserving the space; *** p<0.01, ** p<0.05, * p<0.1
Table 3. Estimates from the regression of the EHRS sourcing strategy
Model 1
Treat×post
Treat×post×CMI
EHRS scope
Model 2
Model 3
0.6710*** -0.0357
(13.429)
(-0.199)
0.4929***
(3.992)
Model 4
0.2117
(0.940)
0.0896**
(2.132)
Size
0.1059
0.0353
0.0217
-0.0031
(1.023)
(0.352)
(0.217)
(-0.032)
Age
-0.0551
-0.0495
-0.0481
-0.0230
(-1.140)
(-1.049)
(-1.019)
(-0.546)
CMI
0.0058
0.0170
-0.1752
-0.0410
(0.031)
(0.094)
(-0.944)
(-0.268)
Region
0.2351
0.1928
0.1923
0.1975
(1.144)
(1.031)
(1.035)
(1.060)
Academic
0.1557
0.2093
0.2454*
0.2519*
(1.129)
(1.593)
(1.902)
(1.903)
Ownership
0.0401
0.0747
0.0728
0.0768
(0.387)
(0.732)
(0.720)
(0.749)
System size
-0.0563*
-0.0445
-0.0468
-0.0455
(-1.875)
(-1.502)
(-1.589)
(-1.530)
Constant
2.5699*** 2.5859*** 2.9293*** 2.7698***
(3.672)
(3.813)
(4.302)
(4.321)
Hospital effects
Yes
Yes
Yes
Yes
Year effects
Yes
Yes
Yes
Yes
Cluster by hospital
Yes
Yes
Yes
Yes
Obs.
21,761
21,761
21,761
21,761
Adj. R2
0.482
0.493
0.494
0.495
Treat×post×Size
Model 5
EHRS diversity
Model 6
Model 7
0.0151*** -0.0465**
(2.882)
(-2.534)
0.0430***
(3.299)
Model 8
-0.0127
(-0.600)
0.0050
(1.227)
-0.0124
-0.0140
-0.0152* -0.0165*
(-1.390)
(-1.567)
(-1.706)
(-1.840)
0.0092** 0.0093** 0.0094** 0.0079**
(2.101)
(2.126)
(2.140)
(2.030)
0.0326** 0.0329**
0.0161
0.0316**
(2.028)
(2.045)
(0.948)
(2.155)
0.0080
0.0071
0.0070
0.0075
(0.529)
(0.467)
(0.473)
(0.499)
0.0544*** 0.0556*** 0.0588*** 0.0618***
(3.509)
(3.572)
(3.758)
(3.673)
0.0074
0.0082
0.0080
0.0083
(1.317)
(1.446)
(1.429)
(1.473)
-0.0044
-0.0042
-0.0044
-0.0044
(-1.576)
(-1.482)
(-1.555)
(-1.540)
0.0390
0.0393
0.0693
0.0590
(0.694)
(0.701)
(1.232)
(1.077)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
21,761
21,761
21,761
21,761
0.418
0.419
0.420
0.420
Notes: Robust t-statistics are reported in parentheses; *** p<0.01, ** p<0.05, * p<0.1
Table 4. Summary Statistics for Matched and Unmatched Samples
Unmatched sample
Variables
Control group
Treat group
Mean
N
Mean
N
Diff.
Size
5.056
9653
5.231 12108 -0.175***
Age
3.074
9653
3.208 12108 -0.134***
CMI
1.393
9653
1.414 12108 -0.020***
Academic
0.066
9653
0.089 12108 -0.023***
System Size
1.593
9653
1.713 12108 -0.121***
* p < 0.1, ** p < 0.05, *** p < 0.01
Matched sample
Control group
Treat group
Mean
N
Mean
N
5.149
8551
5.150
8551
3.151
8551
3.153
8551
1.391
8551
1.395
8551
0.066
8551
0.068
8551
1.640
8551
1.627
8551
Diff.
-0.002
-0.001
-0.004
-0.002
0.013
Table 5. Results of PSM sample regressions of EHRS sourcing strategies
Model 1
Treat×post
Treat×post×CMI
EHRS scope
Model 2
Model 3
0.6253*** -0.1619
(11.503)
(-0.775)
0.5585***
Model 4
0.1838
(0.698)
Model 5
EHRS diversity
Model 6
Model 7
0.0100* -0.0630***
(1.768)
(-3.187)
0.0517***
Model 8
-0.0219
(-0.935)
32
Electronic copy available at: https://ssrn.com/abstract=4618875
(3.833)
(3.633)
0.0858*
(1.720)
Size
0.0617
-0.0029
-0.0182
-0.0274
(0.524)
(-0.025)
(-0.159)
(-0.236)
Age
-0.0097
-0.0155
-0.0162
-0.0149
(-0.183)
(-0.297)
(-0.311)
(-0.288)
CMI
-0.0341
-0.0044
-0.2145
-0.0312
(-0.159)
(-0.021)
(-1.000)
(-0.148)
Region
0.0369
0.0275
0.0305
0.0343
(0.201)
(0.164)
(0.182)
(0.202)
Academic
0.4285** 0.4273*** 0.4581*** 0.4499***
(2.565)
(2.706)
(2.935)
(2.856)
Ownership
0.0378
0.0754
0.0817
0.0778
(0.295)
(0.589)
(0.642)
(0.607)
System size
-0.0629*
-0.0498
-0.0531
-0.0514
(-1.851)
(-1.488)
(-1.592)
(-1.535)
Constant
2.8027*** 2.8086*** 3.1645*** 2.9598***
(3.499)
(3.584)
(4.040)
(3.770)
Hospital effects
Yes
Yes
Yes
Yes
Year effects
Yes
Yes
Yes
Yes
Cluster by hospital
Yes
Yes
Yes
Yes
Obs.
17,102
17,102
17,102
17,102
Adj. R2
0.473
0.483
0.484
0.483
Treat×post×Size
-0.0134
-0.0144
(-1.330) (-1.429)
0.0080
0.0079
(1.568)
(1.547)
0.0261
0.0265
(1.391)
(1.416)
-0.0097
-0.0099
(-0.553) (-0.559)
0.0472** 0.0471**
(2.422)
(2.418)
0.0086
0.0092
(1.264)
(1.342)
-0.0031
-0.0028
(-0.923) (-0.860)
0.0644
0.0645
(1.011)
(1.012)
Yes
Yes
Yes
Yes
Yes
Yes
17,102
17,102
0.421
0.421
-0.0158
(-1.577)
0.0079
(1.529)
0.0071
(0.358)
-0.0096
(-0.550)
0.0500**
(2.563)
0.0098
(1.440)
-0.0032
(-0.953)
0.0974
(1.525)
Yes
Yes
Yes
17,102
0.422
0.0062
(1.341)
-0.0162
(-1.594)
0.0080
(1.552)
0.0246
(1.303)
-0.0094
(-0.532)
0.0488**
(2.499)
0.0094
(1.376)
-0.0030
(-0.894)
0.0754
(1.177)
Yes
Yes
Yes
17,102
0.421
Notes: Robust t-statistics are reported in parentheses; *** p<0.01, ** p<0.05, * p<0.1
Table 6. Robustness analysis with the reduced sample
Model 1
Treat×post
Treat×post×CMI
EHRS scope
Model 2
Model 3
0.4403*** -0.3602***
(12.035)
(-2.805)
0.5541***
(6.343)
Treat×post×Size
Size
0.1647**
0.1153*
0.0988
(2.299)
(1.677)
(1.442)
Age
-0.0425
-0.0411
-0.0410
(-1.456)
(-1.464)
(-1.465)
CMI
0.0407
0.0337
-0.1548
(0.379)
(0.314)
(-1.524)
Region
-0.0406
-0.0507
-0.0562
(-0.297)
(-0.374)
(-0.415)
Academic
-0.0293
0.0033
0.0371
(-0.289)
(0.034)
(0.396)
Ownership
0.0302
0.0373
0.0372
(0.440)
(0.557)
(0.563)
System size
-0.0270
-0.0132
-0.0170
(-1.263)
(-0.631)
(-0.822)
Constant
2.9799*** 3.0422*** 3.4032***
(6.306)
(6.698)
(7.599)
Hospital effects
Yes
Yes
Yes
Year effects
Yes
Yes
Yes
Cluster by hospital
Yes
Yes
Yes
Obs.
17798
17798
17798
Adj. R2
0.592
0.602
0.604
EHRS diversity
Model 6
Model 7
0.0175*** -0.0347
(2.716)
(-1.558)
0.0361**
(2.305)
Model 4
-0.2526
(-1.502)
Model 5
Model 8
0.0127
(0.486)
0.1308***
(4.170)
0.0707
(1.021)
-0.0375
(-1.344)
0.0060
(0.056)
-0.0462
(-0.341)
0.0558
(0.591)
0.0420
(0.627)
-0.0158
(-0.748)
3.2873***
(7.241)
Yes
Yes
Yes
17798
0.603
0.0009
(0.182)
-0.0088
-0.0108
-0.0119
-0.0111
(-0.836)
(-1.021)
(-1.126)
(-1.046)
0.0077
0.0078
0.0078
0.0078
(1.597)
(1.599)
(1.594)
(1.600)
0.0466*** 0.0463*** 0.0341** 0.0462***
(2.920)
(2.916)
(2.016)
(2.900)
0.0149
0.0145
0.0141
0.0145
(0.956)
(0.932)
(0.926)
(0.935)
0.0614*** 0.0627*** 0.0649*** 0.0631***
(3.489)
(3.541)
(3.653)
(3.578)
0.0126*
0.0129*
0.0129*
0.0129*
(1.666)
(1.704)
(1.719)
(1.712)
-0.0052
-0.0047
-0.0049
-0.0047
(-1.484)
(-1.332)
(-1.406)
(-1.340)
0.0025
0.0050
0.0285
0.0067
(0.038)
(0.075)
(0.427)
(0.100)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
17798
17798
17798
17798
0.509
0.510
0.511
0.510
Notes: Robust t-statistics are reported in parentheses; *** p<0.01, ** p<0.05, * p<0.1
33
Electronic copy available at: https://ssrn.com/abstract=4618875
Table 7. Mediation analysis
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Mortality rate EHRS scope Mortality rate Mortality rate EHRS diversity Mortality rate
0.0904***
0.0135***
0.0898***
0.0904***
0.6823***
0.0844***
Treat×post
(4.278)
(3.703)
(3.733)
(20.318)
(3.460)
Scope
0.0403
(0.690)
Diversity
0.0120**
(2.068)
Constant
12.7066***
0.0504
12.7058***
12.7066***
2.6154***
12.6735***
(29.832)
(1.093)
(29.829)
(29.832)
(5.345)
(29.737)
Hospital effects
Yes
Yes
Yes
Yes
Yes
Yes
Year effects
Yes
Yes
Yes
Yes
Yes
Yes
Cluster by hospital
Yes
Yes
Yes
Yes
Yes
Yes
Obs.
14,734
21,761
14,734
14,734
21,761
14,734
Adj. R2
0.712
0.420
0.712
0.712
0.495
0.712
Notes: Robust t-statistics are reported in parentheses; All control variables are included but not displayed for
conserving the space; *** p<0.01, ** p<0.05, * p<0.1
34
Electronic copy available at: https://ssrn.com/abstract=4618875