To Coordinate with or Di erentiate from Your Neighbor: The ... · Records by U.S. Hospital Systems Jianjing Lin, PhD November 11, 2019 Abstract This paper seeks to understand the

To Coordinate with or Differentiate from Your

Neighbor The Adoption of Electronic Medical

Records by US Hospital Systems

Jianjing Lin PhDlowast

November 11 2019

Abstract

This paper seeks to understand the incentives of affiliated hospitals in choosing health infor-mation technology (IT) vendors By adopting a system popular in the local market hospitalsmay benefit from complementarities but also worry about losing patients If benefits outweighthe competitive pressure the adoption decision is further characterized by the tradeoff betweenscale economies from system uniformity and external local market complementarities when thechoice of the chain differs from that in the market I develop and estimate a discrete-choicemodel to study these dynamics in the adoption decision based on a nationwide sample of hospi-tals from 2005 to 2013 To control for unobservables at the market and chain levels I constructinstruments exploiting the spillover related to chains operated in multiple markets I find thatoverall affiliated hospitals appreciate a vendorrsquos prevalence at both the market and chain levelswith the latter to a greater extent but their assessment of these two factors varies by differenttypes of hospital chains The results offer empirical insights on the balance between standard-ization and adaptation in the strategic management of chains and have potentially importantpolicy implications to improve the coordination of adopting health IT

JEL Codes I11 I18 L13 L15 O33Keywords Standardization adaptation strategic management chains technologyadoption coordination competition

I am deeply indebted to Gautam Gowrisankaran for his continuous guidance and support I alsothank Mauricio Varela for valuable suggestions and comments I have benefited from conversations withKeith Joiner Madhu Viswanathan Tiemen Woutersen and Mo Xiao I am also very grateful for helpfrom Katherine Bao in data processing I acknowledge HIMSS Analytics for providing the data used inthis study Any errors are my own

lowastRensselaer Polytechnic Institute Email linj17rpiedu

1 Introduction

Information management has become a critical activity for businesses to maintain competitive

advantage particularly for information-intensive industries such as health care which is generally

characterized by intra- and inter-organization interactions More and more healthcare providers

attempt to exploit information technology (IT) to optimize the provision of care Establishing an

information system that is compatible with neighboring providers may on one hand reduce costs of

data transportation and facilitate care coordination but on the other hand may introduce fiercer

competition when information transfer is seamless If benefits dominate the competitive pressure

the adoption choice for affiliated hospitals may further involve a tradeoff between conformance to

internal consistency and response to local conditions This paper explores the economic tradeoff

that hospital systems face in the adoption decision of electronic medical records (EMRs)mdashthe

central component of the health IT infrastructure Analysis results shed light on the strategic

management of large healthcare organizations and have potentially important policy implications

for coordinating the adoption of health IT

EMRs allow healthcare providers to store retrieve and exchange health information using

computers instead of paper records Many EMR systems are not compatible with each other

The system from one vendor cannot communicate with that from another1 However hospitals

are likely to benefit from complementarities if they use products from the same vendor (EDM

Forum 2013)2 A vendor that is well-established in the local market may have developed a better

relationship with local providers and payers When a hospital processes medical information and

submits claims it achieves greater cost efficiency if its platform is compatible with that of other

providers and payers Moreover given the heterogeneity in technology between vendors hospitals

enjoy knowledge spillover if they share a common vendor The leading supplier also provides

sufficient expertise in the implementation of similar technologies All of these factors can translate

into a cost advantage when the hospital purchases the technology from the largest vendor in the

local area

1Despite improving interoperability in the past few years it has remained limited across vendors especially duringthe studied period (Furukawa et al 2013)

2The application of most informatics tools and approaches described in EDM Forum (2013) relies on a vendor-based system Thus data access and exchange is expected to be easier on platforms from the same vendor

However interoperability is not necessarily beneficial to hospitals Most healthcare providers

view medical records as proprietary information and limit the type and amount of information

shared externally to avoid patients seeking care from competing providers to maintain their com-

petitive position in the market (Adler-Milstein and Pfeifer 2017) When information can be easily

transferred hospitals may worry about losing patients especially those with insurance plans that

set less stringent rules for referral3 As Kellermann and Jones (2013) note healthcare providers

consequently have little incentive to ldquoacquire and develop interoperable health IT systemsrdquo Ev-

idence suggests that information exchange is more likely to occur inside rather than outside the

organization due to strategic concerns reflecting a tendency to create information silos (Miller and

Tucker 2014 Vest and Simon 2018)4

The two competing forces complementaries and the countervailing competition effect make

it difficult to theoretically determine the sign of the net effect from adopting the most popular

technology in the local market In addition to these external factors decision-making for affiliated

hospitals also involves an internal aspectmdashstriving for consistency within the hospital chain If

the benefit from external coordination exceeds the competitive pressure the affiliated hospital has

to further balance internal uniformity required for efficiency with localized operations that meet

the specific needs of the local environment The tension between standardization and adaptation

has been a recurring theme in the strategic management of chains that may operate in dispersed

markets (Sorenson and Soslashrensen 2001) Standardization is a means to achieve scale economies

which is particularly important to manage a large complex healthcare organization5 An internally

standardized IT system can reduce communication costs improve overall organization agility and

provide better support for cross-function and cross-department coordination However the imper-

ative of internal consistency may destroy the ability of its constituent units to serve geographically

differentiated markets (Kaufmann and Eroglu 1999) For instance different types of IT systems

3For instance the likelihood of losing a patient who is seeking a second evaluation or considering a differentprovider becomes higher if information can be accessed without barriers Also hospitals might attempt to limit theoutflow of data created by highly paid clinical or technical staffmdashsuch as the opinions from renowned specialistsmdashtoprevent competitors from benefiting from these valuable inputs (Miller and Tucker 2014)

4An information silo refers to a business division or group (usually an information management system) that isunable to freely or effectively communicate with other groups

5Using the product well-deployed in the parent system could considerably reduce expenses For instance memberhospitals could share the licensing fee human-capital training materials costs on external consultancy and so onAffiliated hospitals also enjoy complementarities in transferring information on shared patients between affiliatedhealthcare providers

may prevail in different markets due to variations in the local environment in terms of demand

and supply side conditions Maintaining the required level of system uniformity may prevent the

local unit from optimizing performance in response to the idiosyncratic local conditions (Cox and

Mason 2007)

This paper is one of the few studies documenting these dynamics behind the adoption decision

Specifically I examine how much profit an affiliated hospital gains or loses from a particular vendor

as the vendorrsquos local market share and system share vary6 Considering the possible impact from

each share variable and the interplay between both three distinct outcomes are possible (i) If

external complementarities exceed the internal network effect the benefit from coordinating with

the market goes beyond the competition concern and moreover affiliated hospitals value external

coordination more than internal integration (ii) if hospitals expect positive returns both at the

market and system levels but the choice that the parent system favors is more desirable organi-

zational consistency plays a more important role and (iii) if the vendor is even devalued by its

popularity in the local market the affiliated hospital tends to deviate from the local choices

Understanding which is the primary incentive is important because of different policy implica-

tions and because of the large dollar values at stake Information technology if properly imple-

mented has the potential to transform health care such as minimizing data-entry work providing

clinicians the best information support streamlining the process for care and administrative tasks

and so on All these promises cannot be fulfilled without an interoperable health IT ecosystem

The federal government has spent over $20 billion7 on promoting the adoption of EMRs but a

truly interoperable healthcare system is still missing In the presence of cases (ii) and (iii) above

policymakers might consider (re)designing appropriate policy and subsidy mechanisms to encour-

age outside cooperation Additionally learning about hospital systemsrsquo incentives is important

because more than 55 of hospitals are part of a hospital chain and are larger than stand-alone

hospitals on average The adoption choice of these hospitals is likely to influence the decision of

other independent healthcare providers in the local area

6The local market share is defined as the ratio of the total number of local hospitals adopting this vendor tothe total number of adopters in the local market The system share is defined as the fraction of member hospitalsadopting this vendor among all members with EMRs

7Accessed at httpswwwcmsgovRegulations-and-GuidanceLegislationEHRIncentivePrograms

DataAndReportshtml on October 28 2019

In this paper I develop a simple model of technology adoption in which hospitals make a

choice between vendors Based on a national sample of hospitals from 2005 to 2013 I estimate a

conditional logit regression of hospital choice over vendors I find that the value of a particular

vendor increases with its popularity in the local market and among all member hospitals with the

latter being much more significant

However endogeneity could arise due to unobserved characteristics at the market or chain level

such as vendor promotions or physiciansrsquo special preferences For instance a vendor achieving

local (system) dominance could simply result from market-wise (system-wise) promotions instead

of network effects I exploit the cross-market spillover within a hospital chain to construct the

instrumental variables (IV) for market share Specifically I first find out a set of markets that

are important to the chains with members located in the same market as the focal hospital and

then average the market share and market-dominance indicatormdashequal to one if the corresponding

vendor has the highest local market sharemdashfor each vendor across these markets The instrument

is relevant in the sense that the parent systems of the focal hospitalrsquos competitors might take into

account the conditions in the important markets which will affect the decision of its constituent

units and ultimately the focal hospital via network effects It is plausible that the unobservables

in these external markets are unrelated to those in the focal market Similarly I use the spillover

across hospital chains via the overlapped market to construct the instruments for the system share

variable I use the control function (CF) approach in the IV estimation The heteroskedasticity-

robust Hausman test confirms the concern about endogeneity

After addressing endogeneity the positive network effect remains at both levels and the differ-

ence between them becomes smaller I further examine whether affiliated hospitalsrsquo assessment of

these two share variables varies across different types of hospital chains I focus on the following

chain characteristics (1) the level of IT standardization (2) experience of ownership changes (3)

the presence of top-level IT governance and support (4) the level of vertical integration and (5)

the presence of certain in-house IT infrastructure Hospitals show conformance to standardization

if the entire system has been consolidated into a single platform whereas the choice by hospitals

from IT-fragmented chains seems to be less constrained Acquired hospitals tend to adopt the

vendor chosen by the ldquonewrdquo parent system and hospitals from acquiring chains seem to actively

respond to local market conditions Among chains subject to a high-level IT governance hospitals

tend to follow the parent system and deviate from the local choices The positive network effect

at the market (system) level diminishes (increases) with the number of affiliated ambulatory care

facilities Lastly chains that have strengthened infrastructure for IT services emphasize internal

integration to a greater extent

The rest of the paper proceeds as follows Section 2 describes this paperrsquos relation to the

literature Section 3 provides basic information and institutional background Section 4 presents the

datasets and summary statistics Section 5 develops a simple model characterizing hospital choice

of EMR vendors Section 6 describes the empirical strategy Section 7 discusses the estimation

results Section 8 concludes and points out limitations

2 Relation to the Literature

This paper contributes to various strands of the literature First it complements current studies on

EMR adoption built on network effects theory Miller and Tucker (2009) studied the relationship

between privacy protection policy and technology diffusion and found that privacy regulation in-

hibits the adoption of EMRs by suppressing network externalities Lee et al (2013) focused on the

impact of health IT on hospital productivity and found little evidence of network effects A more

related paper by Wang (2019) examined hospital adoption decisions at the basic and advanced

levels in a dynamic strategic environment and found competitive effects at both levels8 These

studies assume EMR systems are compatible but in reality systems from different vendors are

unable to communicate Recognizing this nature I define a network according to the identity of

an EMR vendor Desai (2016) also accounted for vendor heterogeneity in the network definition9

She focused on the network effect at the market level for both stand-alone and affiliated hospitals

whereas I incorporate the impact from the desire for internal consistency into the adoption decision

for hospital chains

8A number of factors could contribute to the different results between Wangrsquos paper and mine For instance Iuse different criteria to define adoption and focus on different components in the technology Moreover she defineda network without taking into account vendor identity like other prior studies

9Freedman et al (2018a) applied a similar definition of network in exploring the role of market structure in thedistribution of EMR vendors across markets They found evidence of agglomeration but their measure for vendorclustering is derived from a statistical test and is not directly related to hospital incentives

Moreover this paper complements the literature on network effects by exploring the case where

weak or negative network effects might be present (Tucker 2018) Most discussion of this topic in

the literature pertains to the intragroup network effect in the two-sided market (Rochet and Tirole

2002 2003 2006 Kurucu 2007 Belleflamme and Toulemonde 2009 Bardey et al 2014) Classic

network effects arise when having more participants in the network makes it more appealing In

the two-sided market however agents within the same group do not always appreciate the increase

in group size due to competition effects such as merchants in payment card systems advertisers

in yellow pages developers in computer operating systems and drivers and riders in Uber The

adoption of EMRs is in a similar vein except that both the positive and negative network effects

that hospitals experience are direct network effects in the one-sided market My paper complements

this strand of literature by studying this economic tradeoff in the context of IT adoption in health

My paper also provides empirical evidence on understanding the balance between the two strate-

gic imperativesmdashinternal consistency and localized operations The debate on pursuing standard-

ization versus adaptation has been one of the most difficult issues in strategic management espe-

cially for franchising systems and yet it remains inconclusive about what is the most appropriate

arrangement (Kaufmann and Eroglu 1999 Dant and Gundlach 1999 Cochet et al 2008 Chiou

and Droge 2015) Although most hospital chains are not operated in the franchising format their

choice of IT vendors reflects such a tradeoff and the findings here offer empirical insights on this

tension in an industry where efficient management of information is a key to success The policy

implication becomes subtle depending on the interaction between the external and internal forces

3 Industry Background

EMRs were invented in the 1970s but their acceptance was slow until recent years In 2009 the

American Recovery and Reinvestment Act provided $27 billion to promote health ITmdashin particular

to encourage the adoption of EMRs It was the first substantial commitment of federal resources

to support the adoption of this technology and created a strong push in the diffusion

I focus on inpatient EMR systems According to the Healthcare Information and Management

Systems Society (HIMSS) a solid inpatient EMR foundation includes the following key components

Clinical data repository (CDR) order entry (OE) clinical decision support capabilities (CDS)

computerized physicianprovider order entry (CPOE) and physician documentation (PD) CDR

is essentially a centralized database that collects stores accesses and reports health information

It is the backbone of the entire system OE is an automated process of entering order information

into an electronic billing system The orders are usually associated with ancillary services such as

lab work and radiology CDS assists clinicians in decision-making tasks such as determining the

diagnosis or setting treatment plans CPOE is a more advanced type of electronic prescribing that

can link to the adverse drug event system to avoid potential medication errors PD offers physicians

structured templates to document a patientrsquos daily progress operative notes consultation notes

emergency department visits discharge summary and other relevant information during the course

of a hospital admission

The implementation cost of an EMR system varies tremendously depending on numerous fac-

tors including the sophistication of the system the amount of data conversion the level of cus-

tomization one-on-one assistance during training and ongoing use According to a study conducted

by the Congressional Budget Office (Orszag 2008) the average implementation cost for a 250-bed

hospital ranges from $3 million to $16 million and the ongoing cost for subsequent upgrades and

maintenance is approximately 20 to 30 of the initial contract value per year The rollout cost

could even escalate to hundreds of millions of dollars for large hospitals For example in 2011 the

medical center at the University of California San Francisco spent $150 million to put in place its

EMR system

Despite the hefty price tag accompanying EMR implementation hospitals are willing to make

a massive investment in this technology for various reasons Besides the strong push from the

federal incentive program EMRs enable hospitals to engage in better documentation to lower their

administrative costs and to streamline and automate their revenue practices Digitizing medical

records also helps hospitals adapt to reforms in the payment system as well as to the new features

of Accountable Care Organizations Last but not least a qualified EMR system may improve the

quality of health care although the literature suggests mixed evidence overall or positive effects

among subpopulations10

Choosing between EMR vendors is a complicated decision Najaftorkaman et al (2015) iden-

tified 17 distinct theories and models as theoretical foundations for EMR adoption studies and

documented 78 factors that affect adoption Among those factors technical factors (Van Der Mei-

jden et al 2003) financial factors (Simon et al 2007) and organizational and environmental

factors (Kazley and Ozcan 2007 Jha et al 2009 Miller and Tucker 2009 Angst et al 2010) have

received significant attention in the health informatics and economics literature In this paper

I focus on an environmental factor the network effect which reflects strategic interaction in the

adoption decision

I focus on the adoption decision of hospitals affiliated with a hospital chain system Accord-

ing to the American Hospital Association (AHA) a hospital chainsystem is defined as ldquoeither a

multi-hospital or a diversified single-hospital systemrdquo11 For hospital systems most organizational

decisions are made by the managing party for which the decision of IT adoption involves the im-

perative to standardize chain-wise rules and procedure for strategic concerns and the pressure to

respond to variations in the local environment In this study I examine the adoption of health IT

at the hospital level by implicitly assuming that the payoff of each member is aligned with that

of the entire system The IT adoption choice of an individual hospital reveals the parent systemrsquos

willingness to participate in the local health information network

4 Data

41 Data sources and important variable definitions

My primary dataset comes from the HIMSS Analytics Database which is the longest running

survey and comprehensive national source of health IT adoption data The database contains

detailed demographic and IT profile information for the majority of US hospitals and a large set

10The following studies are a few that examine the impact of EMRs on patient outcomes McCullough et al (2010)Miller and Tucker (2011) Agha (2014) McCullough et al (2016) Freedman et al (2018b) Lin and Olson (2019)See also Atasoy et al (2019) for a comprehensive review

11ldquoA multi-hospital system is two or more hospitals owned leased sponsored or contract managed by a centralorganization Single freestanding hospitals may be categorized as a system by bringing into membership three ormore and at least 25 percent of their owned or leased non-hospital pre-acute or post-acute health care organiza-tionsrdquo See Fast Facts Archive 2019 from the AHA Annual Survey Accessed at httpwwwahaorgresearchrcstat-studiesfast-factsshtml on October 28 2019

of non-hospital facilities including ambulatory and sub-acute care facilities It records the time and

choice of the adoption decision enabling a more realistic network definition

I define a hospital as having adopted EMRs if the component CDR is live and operational in

the hospital The implementation of CDR is the prerequisite for other applications Other typical

and common applications such as CDS and CPOE will often be put in place after CDR12 usually

purchased from the same vendor This paper seeks to evaluate the strategic effect on the choice of

vendors which is considered at the early stage of the adoption decision The time to adopt CDR

signals a hospitalrsquos willingness to enter the market and its preference for a particular vendor13

With information on the identity of the vendor I construct the main dependent variablemdashthe

choice of a particular vendor by an affiliated hospitalmdashfrom the HIMSS data My adoption data

extend from year 2005 through 2013

I focus on two types of product characteristics a vendorrsquos local market sharemdashdefined as the

ratio of the total number of local hospitals adopting this vendor to the total number of adopters

in the local marketmdashand system sharemdashdefined as the fraction of chain members adopting this

vendor among all members with EMRs14 I call the vendor with the highest local market share

the market-dominantmarket-leading vendor and the vendor that is implemented by most member

hospitals the system-dominant vendor

The HIMSS data include key demographic information about hospitalschains such as bed

size location whether the chain has an information system (IS) Steering Committee the number

of data centers a chain owns and so on15 I complement the HIMSS data with the AHA Annual

Survey using the Medicare provider number and geographic information to link the datasets I

extract variables related to profit status location and whether a hospital is a teaching hospital from

the AHA data because I can only access a shorter time period (from 2005-2010) of this dataset

Moreover I merge my hospital data with a consistent hospital level panel database created by

Cooper et al (2018) to obtain information on hospital system affiliation and ownership changes

12In the sample more than 95 of hospitals with advanced components have already adopted CDR13The record of ldquoenterprise EMRsrdquo another basic component examined by some prior studies was discontinued

as of 2008 Therefore I focus on CDR14In both definitions I exclude the focal hospital I also use the weighted average of share that accounts for bed

size The main results hold Section 7 provides more detail15An IS Steering Committee made up of high-level stakeholders and experts gives IS strategic directions and

provides guidance and support to IT project management

(eg mergers and acquisitions)16 Finally I use information on hospitalsrsquo Federal Information

Processing Standards (FIPS) codes from the AHA survey to define a market A market is equivalent

to a hospital service area (HSA) a measure developed by Makuc et al (1991) An HSA consists

of one or more counties that are relatively self-contained with respect to the provision of routine

hospital care The location of each hospital can be directly linked to the corresponding HSA by

the FIPS codes The sample contains about 920 HSAs in the sample covering more than 95 of

HSAs in the US17

In addition the HIMSS database also contains demographic and IT adoption information for

non-hospital facilitiesmdashsuch as data centers and ambulatory and sub-acute care facilitiesmdashwhich

are associated with a health care delivery system either because they are (entirely or partly) owned

leased or managed by the system With these data I construct variables characterizing system

heterogeneity and try alternative marketsystem share definitions for robustness checks

42 Summary statistics and suggested evidence

Figure 1 presents the trend of adoption and switching rates separately for stand-alone and affiliated

hospitals with the upper panel for adoption rates and the lower panel for switching rates Table A1

in the Appendix provides more detail on these variables Almost 49 of affiliated hospital adopted

EMRs in 2005 and the fraction had risen to more than 96 by 2013 Switching rates have been

increasing over time and peaked in 2010 right before the implementation of the HITECH Act

Stand-alone hospitals share a similar pattern in adoption and switching except that both rates

have always been lower than those of affiliated hospitals Therefore it is important to understand

the dynamics behind the adoption decision of affiliated hospitals which could affect the choice by

stand-alone hospitals

Figure 2 shows the distribution of the adoption choice between the market- and system-dominant

vendors by affiliated hospitals that are new adopters in the sample and for which none of the dom-

inant local vendors is the main IT provider for its parent system18 Thus these hospitalsrsquo choice

16According to Cooper et al (2018) the AHA documents system information with lags and ldquosometimes deals withmergers and acquisitions in a way that would complicate analysis if used directlyrdquo Cooper et al (2018) modifiedthe lagged system information in the AHA annual survey Also they verify the existence and timing of hospitalownership changes using various data sources to provide a complete picture of hospital ownership transition

17I also use a different market definitionmdashthe hospital referral regions (HRRs)mdashfor a robustness check18Appendix Table A2 provides more detail

of vendors falls into one of the following categories dominating the local market dominating the

parent system or neither The first panel presents the distribution based on all these hospitals

Almost 60 of them follow the parent system whereas only 10 choose the dominant local vendor

This difference is even more significant among hospitals affiliated with chains that had been consol-

idated into a single vendor (as shown in the second panel) About 76 select the system-dominant

vendor whereas merely 59 use the technology popular in the market For hospitals from chains

that are already ldquofragmentedrdquo in IT platforms 127 of them choose the market-dominant vendor

and less than 50 follow the parent system (as shown in the third panel) Cooper et al (2018)

documented consolidation in the US hospital sector in recent years Ownership changes such as

mergers and acquisitions might affect EMR adoption and brand choices The next two panels show

this distribution separately for hospitals from chains that had previously acquired hospitals and

for hospitals that had been acquired into a chain The majority of hospitals from acquiring chains

choose the system-dominant vendor up to 641 whereas only 45 of acquired hospitals follow

the new parent system No more than 10 of these hospitals (that experienced ownership changes)

choose the market-dominant vendor The last panel shows the distribution among hospitals sub-

ject to a high-level IT governance for which the strategic consideration and implementation of IT

adoption might be different Hospitals from these chains demonstrate a pattern similar to that of

acquired hospitals Note that about one third of the overall sample choose the ldquoneitherrdquo category

A potential explanation is that hospitals might be forward-looking in the adoption decision and

thus pick a vendor that they see as being market- andor system-dominant in the future despite

being neither for now

The market for inpatient EMRs have been fairly concentrated Table 1 lists the top 11 vendors

that on average account for more than 90 of the national market share All the remaining

vendors are categorized into one group called ldquoothersrdquo The combination of the leading vendors

in Table 1 with the group ldquoothersrdquo results in 12 options that form a choice set available to all

hospitals in the model I develop in the next section Local markets are even more concentrated

Figure 3 presents the sample cumulative distribution functions (CDFs) of the number of leading

vendors (listed in Table 1) per market In over 80 of the markets the number of existing vendors

that belong to the top list is no more than three per market Figure 4 shows the CDFs of the

market-dominant vendorrsquos local market share for each leading vendor In more than 60 of the

markets the market-dominant vendor usually has at least 50 of the local market share19 Figure

5 shows in a map the geographic distribution of leading vendors across markets Each patch in the

map represents a local market Each color stands for a particular vendor and each dot denotes

approximately 10 of local market share for the represented vendor The clustering of dots of the

same color within a market measures the extent to which the represented vendor dominates the

local market Figure 5 suggests that most markets have a clear dominant vendor but which vendor

dominates varies across geographic areas

Figure 6 shows the CDFs of the number of leading vendors per hospital chain More than 90

of the chains have no more than two leading vendors in place Figure 7 presents the CDFs of

the system share for the system-dominant vendor In about 80 of the chains at least 50 of

adopting members choose the system-dominant vendor suggesting that most of the chains have a

clear dominant vendor20 To summarize the simple statistics suggest that integration occurs both

externally and internally with the latter to a greater extent Also variations in adoption choice

across markets and chains are important sources for identification

Table 2 reports the summary statistics for important variables The first panel shows the

statistics for affiliated hospitals The first two rows suggest that both markets and hospital systems

are becoming more connected Particularly within a hospital chain the choice of more than 70

of adopting members converges to the system-dominant vendor at the end of the sample period

The other hospital characteristics such as profit status percent of teaching hospitals and bed

size remain quite stable over time Like Cooper et al (2018) I also observe a trend whereby

the hospital industry is becoming more consolidated because the number of acquired hospitals

and acquiring chains (in the last row of the third panel) was increasing during the sample period

The next panel summarizes variables at the market level The number of stand-alone hospitals

per market becomes smaller whereas there are an increasing number of affiliated hospitals in each

market The number of leading vendors per market has been increasing since 2005 and the number

of hospital chains varies little over time The last panel reports the statistics at the chain level

19This holds for most of the leading vendors except GE I exclude the vendor Eclipsys here because it merged withanother vendor at the end of 2011

20For the cases of CPSI and HMS in 75 of chains at least half of the adopting members pick the system-dominantvendor

The chain size in terms of the number of members grew during the sample period More leading

vendors exist per chain over time The number of affiliated ambulatory care facilities per chain

doubled during the sample period whereas the number of sub-acute care facilities decreased until

2010 by 25 and then started to rebound This finding is consistent with the recent growth in

the outpatient settingmdashhospital reducing inpatient care by shifting patients to outpatient facilities

I use two variables to measure the level of IT infrastructure of a chain the number of sub-acute

care facilities using the same IS platform as a hospital in the chain and the number of affiliated

data centers21 There are fewer sub-acute care facilities with a compatible IS platform over time

which might be related to the reduced number of these facilities overall Hospital chains have more

affiliated data centers over time Only 10 of hospital chains are featured with a top-level IT

governancemdashan IS Steering Committee

5 Model

I develop a simple model characterizing the choice of EMR vendors for affiliated hospitals There

are M regional markets each of which has Nm affiliated hospitals forallm = 1 2 M These affiliated

hospitals are associated with K hospital systems and each contains Nk members forallk = 1 2 K

To maximize the ex-ante profit every hospital simultaneously decides whether to adopt EMRs and

if yes chooses a vendor j from the set J = 1 2 J which includes the vendors listed in Table

1 plus the additional group ldquoothersrdquo Given that all the vendors are serving the national market

J is fixed for every decision maker The set J plus the outside optionmdashno EMRs denoted as

ldquo0rdquomdashforms a choice set available to all hospitals

Specifically a hospital that has no EMRs either purchases from vendor j or remains with no IT

system A hospital with some on-site system either continues with the current choice or switches to

a different vendor but reversion to non-adoption is not allowed In other words hospitals are not

allowed to unadopt22 I assume that each hospital has the same margin between price and marginal

21IT infrastructure refers to a collection of hardware software networks data centers facilities and relatedequipment that are required for the enterprise IT-enabled operation

22The raw data show less than 1 reversion and most of these hospitals either ldquorestoredrdquo to adoption or wererecorded with a different vendor the next year A conversation with industry experts suggests that reversion is almostimpossible either because of input error or because the hospital is simply in the middle of transition to a new systemTherefore I assume that in this case the hospital maintains the same record as last year

cost and thus captures a fixed portion of consumer surplus As a result oligopoly competition in

medical care will not be explicitly considered in the model I further assume that each decision

period is one year

Let hospital irsquos profit from adopting vendor j at time t πjit have the following form

πjit = Xjitα+ Zitγ

j + εjit (1)

where Xjit denotes a set of variables that vary by vendors and hospitals such as a vendorrsquos local

market share and system share Zit represents variables that are constant across vendors but vary

by hospitals such as hospital-specific features and εjit denotes profit shocks that are unobservable

to econometricians Let ait denote the adoption choice of hospital i at time t Thus hospital i

will pick vendor k that is ait = k if πkit ge πjit forallj isin J k Let δkit denote the mean profit from

choosing vendor k at time t that is

δkit = Xkitα+ Zitγ

I assume that ε follows the Type I extreme value distribution The profit from non-adoption is

normalized to zero Therefore the probability for hospital i without EMRs to choose vendor k

has the following form

Prob(ait = k) =exp(δkit)sum

jisinJcup0 exp(δjit) (3)

By the same logic I can write down the probability for hospital i that has already adopted EMRs

Prob(ait = k) =exp(δkit)sumjisinJ exp(δjit)

Several points are worth noting Due to data limitation (eg lacking information on price) I

construct the model from the perspective of hospitalsmdashthe demand side of the market assuming

that hospitals take as given what is offeredhas occurred on the supply side However the adoption

choice is likely to be an outcome from the interactionmdashsuch as negotiationmdashbetween the hospital

and vendor To reduce this concern I use IVs to control for these unobserved factors when I estimate

the model23 Also I develop a static instead of dynamic model here considering that the choice

between market leaders and system-dominant brands might require less dynamic optimization than

the fundamental decision of adopting an EMR system Moreover a member hospital could make

the decision on its own follow the instruction of the managing party or choose some solution in

between Whoever makes the decision I assume that the action taken by the individual hospital

reflects the chainrsquos adoption incentive Last but not least following the literature on EMR adoption

built on network effects theory I use a vendorrsquos popularity in the local market and parent system

as primary determinants in the profit from adoption The effects from other IT capabilities that

might be important in vendor selection decisions are not explicitly modeled in the profit function

but they will be captured in the vendor fixed effects The way I model adoption here is similar to

other empirical studies on adopting network technology in different industries (Gowrisankaran and

Stavins 2004)

6 Estimation Strategy

61 Non-IV estimation

This section describes the empirical approach to estimate how affiliated hospitals evaluate vendor

characteristics and how the effect varies across different types of hospital systems I regress the

choice of vendors on vendor characteristics and hospital features using a conditional logit regres-

sion In the main specification the dependent variable is a vector of all available choices each

of which equals one if the corresponding option is chosen and zero otherwise The choice set in-

cludes the leading vendors as listed in Table 1 the ldquoothersrdquo category and the outside optionmdashno

EMRsmdashfor hospitals without EMRs I exclude the outside option from the choice set for hospitals

that have already adopted the technology The independent variables include vendor and hospital

characteristics A vendorrsquos local market share and system share are the key variables of interest

and enter into the value function as vendor characteristics The estimated coefficients for the key

variables of interest measure the extent to which hospitals are more or less likely to choose a vendor

with greater systemlocal market share that is the network effect within the systemmarket A

23The next section provides more detail

positive and significant coefficient for market share implies that affiliated hospitals have a tendency

to integrate into the local community By contrast if a hospitalrsquos concerns about losing patients

exceed the benefit from external complementarities or if the requirement of centralized IT structure

dominates I expect the desirability to choose the dominant local vendor to diminish or the effect

to even turn negative A positive coefficient for system share implies that member hospitals benefit

from an internally-standardized IT platform

As noted in Section 3 selecting an EMR vendor is a complicated process depending on a variety

of factors IT capabilities at the vendor level namely system quality usability functionality and

technical support among others are captured by vendor fixed effects Due to limited access to

data I only use controls for the following hospital and chain characteristics hospital profit status

hospital bed size whether a hospital is a teaching hospital the number of affiliated members in the

parent system and the indicator for post-HITECT Act periods24 I further interact the indicator

for teaching hospital number of member hospitals and hospital bed size with vendor dummies

to allow the effect from these variables to vary by vendors By assuming Type I extreme value

distribution on the error term I use the maximum likelihood estimator (MLE) to estimate the

conditional logit model Each observation corresponds to a hospital and year combination

62 IV estimation

Concerns may exist about endogeneity for the market and system share variables such as unob-

served characteristics at the market or system level At the market level these variables might

include market-wise promotion and special preferences of local physicians both of which could

affect market share and the choice of vendors at the same time For instance a vendor based

in a particular market may provide promotion to all local hospitals It thus becomes the most

adopted simply because of price advantage rather than the benefit from coordination Similarly

aggressive discounts offered to all chain members could also affect system share and the choice of

vendors simultaneously resulting in the same endogeneity issue Moreover the chain-wise unob-

served characteristics could also be correlated with market share and the choice of vendors as well

24I tested what to include in the controls from a set of relevant variables in the literature using a shorter period ofthe sample including total outpatient visits total inpatient admissions the number of full-time physicians percentageof Medicare and Medicaid discharges number of competitors and fraction of competitors with EMRs The resultsare very similar to those from the reported specification

For instance special terms such as the most favored nation clause in the negotiation between the

chain and vendor might restrict the vendor from working with other providers (eg the chainrsquos

competitors) or even entering certain markets Thus the vendor could have been more popular in

a region due to coordination benefits and not in fact due to contract restrictions As a result it

is hard to theoretically predict the direction of bias from endogeneity

Instruments for market share

To construct the instrument for the market share variable I exploit cross-market spillover within

a hospital chain to identify the network effect Specifically I first find out all outside associated

marketsmdashexternal markets that are important to the hospital chains that the focal hospitalrsquos local

competitors are affiliated withmdashand then average the market share and market dominance indicator

for each vendor across these markets Consider hospital A1 a member of chainA located in market

m1 in Figure 8 I call the market where the focal hospital A1 is the focal market The endogenous

variable for A1 is a vector with each entry indicating the market share for the corresponding vendor

in m1 Suppose that B1 is affiliated with chain B the rest of whose members B2 B3 B4 B5 are

equally distributed in m2 and m3 The instruments for A1 are the average of the market dominance

indicator and market share for each vendor across m2 and m3 This instrument is relevant in the

sense that the managing party of B may take into account the market conditions in m2 and m3

and thus affect the choice of B1 and ultimately A1 It is a clean measure because these outside

markets plausibly have little relation with the unobservables in m1

The exclusion restriction in this instrument boils down to vendor market share in the outside

associated markets only affecting the focal hospitalrsquos vendor choice via its effect on the local vendor

market share In other words no spillover in unobservables occurs between the focal market m1

and the outside associated markets m2 and m3 A threat to identification could occur if a vendor

offers market-wise promotion or establishes a large-scale sales network in m1 m2 and m3 at the

same time because all the members of B are located in these markets To assuage these concerns I

base the IV analysis on the sample satisfying the following conditions First I focus on the sample

where the focal hospital is located in a market that is treated as ldquosmallrdquo in the overall decision-

making of the associated chainmdashthe parent system for the focal hospitalrsquos local competitors such

as m1 for B I construct the instruments using markets that are likely to play an important role in

the decision process for the associated chain such as m2 and m3 for B By doing so I reduce the

likelihood that affiliated hospitals in the outside associated markets respond to the variations in

the focal market I view a market as ldquoimportantrdquo in the overall decision-making of a hospital chain

if among all the markets where the chain is located the market includes most of its members (eg

m2 and m3 for B in Figure 8) and as ldquosmallrdquo otherwise (eg m1 for B in Figure 8) In addition

I remove an outside associated market from the IV sample if it contains members from the focal

chainmdashthe parent system for the focal hospitalmdashand if the focal market is important to the focal

system By doing so I reduce the probability of spillover (eg market-wise promotion) from the

outside associated market to the focal market25 Moreover the exclusion restriction also requires

that this instrument is conditional mean independent of the unobservables at the system level

I further remove an outside associated market if it plays a significant role for the focal chain26

Finally I only keep outside associated markets that are at least 300 miles away from the focal

hospital to avoid spillover across markets due to proximity

Table A3 summarizes the key statistics separately for the focal and outside associated markets

On average each focal hospital corresponds to five to six outside associated markets Both the focal

and outside associated markets are larger in terms of the number of hospitals with the latter even

three times the size of an average market in the overall sample This observation is consistent with

how I construct the instrumentsmdashusing the important (ie big) markets of an associated chain

as instruments for its ldquofringerdquo markets27 Figure A1 shows an example of a focal hospital and its

outside associated markets in the data The focal hospital is located in Florida for which the three

outside associated markets are located in Mississippi New York and Pennsylvania

25Note that the exclusion restriction still holds as long as the unobservables in the outside associated markets are notcarried over to the focal market but it becomes more likely in this case because both markets contain members fromthese chains and both are important markets For instance a vendor is likely to extend a market-wise promotion inthis direction However if the focal market is small to both chains the incentive to extend such a promotion becomessmaller given that offering large-scale promotions is costly

26The focal chain will likely choose a vendor that offers market-wise promotions in the important markets Includingit will result in endogeneity for the system share variable

27The total number of outside associated markets is smaller than that of focal markets suggesting that some ofthe focal hospitals may share common outside associated markets

Instruments for system share

I construct the instrument for the system share variable based on associated chains that share

common markets with the focal chain Specifically I first find out all associated chains given a

focal chain and then calculate the average of the system share and system dominance indicator

for each vendor across these chains For instance in Figure 9 B is an associated chain for A It

satisfies the relevance condition because the adoption choice of all members in B will affect the

choice of B4 and hence A2 and ultimately the system share for A B is exogenous to A in that

the interaction between B and its chosen vendors is plausibly private and thus will be independent

of that between A and its vendors

However there may be concerns about unobserved spillovers via the shared markets I construct

the instruments from a subsample that meets the following criteria First I keep an associated

chain if the focal chain is not operated in any of its important markets such as B for A in Figure

9 As a result B places much less weight on the markets in which A is located in the decision-

making Second I exclude an associated chain if it contains constituent units in all the important

markets for the focal chain to reduce the likelihood that the unobservables at the chain level are

correlated between the two chains28 Finally I construct the instruments based on chains whose

average distance to the focal chain is at least 300 miles Since none of the important markets for

the associated chain contain members from the focal chain and since a chain generally considers

the system as a whole in making decisions barely reacting to variations in a fringe market the

constructed instruments for the system share variable is plausibly exogenous to the unobserved

characteristics in the focal market

Table A4 summarizes the key statistics separately for the focal and associated chains The IV

sample includes larger chains in terms of the number of member hospitals with the focal chains

four times bigger than the overall sample average Figure A2 presents an example of a focal chain

and its associated chains from the data The focal chain has two important markets one in the

northwest coast (shown in the left subfigure) and the other in the southwest coast (shown in the

28For instance a vendor is likely chosen by the associated chain due to promotions in all the involved markets andchosen by the focal chain due to promotions in all of its important markets In this case if both chains overlap inall the important markets for the focal chain the contract termsnegotiation details might be similar between thetwo chains I also raise the bar by requiring more important markets for the focal chain unrelated to the associatedchain The results are generally similar with less significance due to further reduced size of the IV sample

right subfigure) which also contains affiliated hospitals from the associated chains In all the IV

sample focuses on affiliated hospitals from larger chains and thus the results should be interpreted

with this caveat in mind

A market corresponds to an HSA and year combination Profit shocks are assumed to be iid across

years and hospitals I pool all of the observations together and estimate the model using the CF

approach because of the nonlinear relationship The first step is a regression using the endogenous

variable as the dependent variable and the instruments as explanatory variables Specifically I

regress the market (system) share on the corresponding instruments and obtain the error term emkt

(esys) Next I estimate the main regression additionally including the first-stage residuals and

a polynomial expansion up to a third degree29 The IV sample is restricted to the observations

from which I can construct the instruments I also examine whether and how the results change by

different types of chains by running the main specification on different subsamples or interacting the

key variables of interest with variables for different chain characteristics For robustness checks I

also use alternative definitions for the market and system shares and try different market definitions

7 Results

71 Evidence from overall sample

I now discuss the empirical results Because hospitals with EMRs face a different choice set than

those without EMRs I run the conditional logit regression separately for each of them Table 3

reports the coefficients for the key variables of interest market share system share and the

indicator for whether a particular vendor is chosen previously based on the sample of all affiliated

hospitals The left (right) panel shows the results for affiliated hospitals without (with) EMRs

The first column in Table 3 suggests that both the market and system shares have positive

impacts on the adoption choice Hospitals benefit from choosing a vendor that is popular in the

local market suggesting that coordination benefits exceeds the competitive pressure Moreover the

29The consistency results for CF estimation only guarantee that some function of the first-stage residuals will makethe second stage produce unbiased estimate Thus I include the first-stage residuals with a high-degree polynomialexpansion in the second stage

benefit from coordinating with the market is less than that from coordinating within the hospital

chain Specifically the value from choosing a local popular vendor is merely one fourth of that

from a vendor with an equal amount of share in the parent system The estimation model seems to

fit with the data reasonably well with the pseudo R2 being 058 The next two columns show the

coefficients based on the IV sample for which the sample size drops by over 70 but the findings

based on the regular logit regression remain similar in the reduced sample30 The third column

shows the estimated coefficients using the CF approach Both share variables show a positive and

significant effect each with greater magnitude Moreover the difference between the two effects

becomes smaller the coefficient on market share is about half of that on system share Assuming

that the IVs are able to control for the unobserved characteristics at the market and chain levels

the estimated coefficients on the share variables measure the network effects at both levels The

results suggest that hospitals are willing to coordinate both externally and internally with stronger

incentives for the latter The CF approach enables a heteroskedasticity-robust Hausman test on

whether the suspected variables are endogenous I conduct this test by testing the joint significance

of the error terms in the second stage The last row of the left panel shows that the null hypothesis

of no endogeneity is rejected

The right panel shows the same set of results based on experienced adopters In the first

column of this panel the coefficient on system share remains positive and significant despite a

smaller magnitude than that based on the previous sample whereas the coefficient on market

share becomes insignificant I include in this analysis the indicator for whether a particular vendor

is chosen previously to evaluate the extent to which a hospital sticks to the current vendor The

significantly positive estimate suggests that hospitals are very ldquoloyalrdquo to the chosen vendor implying

a potentially large cost barrier involved in switching The effects from all these variables are stronger

based on the IV sample as shown in the next column The coefficient on market share becomes

significant at the 10 level with a much larger magnitude and the impact from choosing a vendor

with greater system share increases by two thirds The last column presents the results based on

the CF approach The coefficients for both share variables are significantly positive with greater

magnitudes than those from the non-IV estimation The gap between the two coefficients also

30Table A5 in Appendix reports the results from the first stage of the CF approach suggesting that the instrumentsare strongly correlated with the share variables

shrinks For hospitals with EMRs the impact on choice from a change in system share is 30

greater than that from the same variation in market share The pseudo R2 suggests that the

estimated model provides a better fit to the sample of experienced adopters The last row also

confirms the existence of endogeneity in the share variables For a robustness check I use other

definitions for the share variablesmdashsuch as the shares with bed size adjustment (results in Table

A6) or based on hospital and non-hospital providers (results in Table A7)mdashand different market

definitions such as hospital referral regions (results in Table A8) The findings are generally

consistent31

To summarize EMR adoption exhibits positive network effects both at the market and system

levels and affiliated hospitals value internal integration to a greater extent The difference in the

impact between these two characteristics (market share vs system share) becomes smaller for

hospitals that already have EMRs suggesting that a vendorrsquos prevalence at the local market may

be more important for hospitals that seek alternative options for replacement than for first-time

adopters

72 Evidence by system heterogeneity

The way hospital systems evaluate internal verus external coordination could vary by different types

of chains In this subsection I examine how their assessment differs according to the following chain

characteristics (1) the level of IT standardization (2) experience of ownership changes (3) the

presence of top-level IT governance and support (4) the level of vertical integration and (5) the

presence of certain in-house IT infrastructure

Level of chain-wise IT standardization

The benefits from internal consolidation in IT systems include increased efficiency coordination

and organization agility whereas the decentralized approach may be more responsive to local

(community) needs and more likely to encourage innovation As a result some hospital chains

31As a non-hospital provider is not equivalent to a hospital the alternative measures for shares are calculated asan average weighted by bed size Because ambulatory care facilities do not have beds the non-hospital providershere include only sub-acute care facilities One way to include both ambulatory and sub-cute care facilities is to usethe number of physicians as a weight However neither the HIMSS nor AHA datasets provide enough informationon physicians

could be stricter about the decision to adopt the same system in all affiliated hospitals whereas

the others could be more permissive I define a chain as IT-standardized if the entire system has

been working with a single vendor and as IT-fragmented if the chain adopted EMRs from multiple

vendors The specification is the same as before except that I estimate the regression separately

for chains that are IT-standardized and that are IT-fragmented

Table 4 presents the results for this specification The first column in Table 4 reports the

coefficients for the sample of new adopters from chains that are IT-standardized The coefficients

for the share variables are positively significant both with much greater magnitudes than the

estimates from the overall sample (shown in Table 3) Moreover the value from coordinating

within the hospital chain is more than three times as large as that from coordinating with the local

market The third column shows the results for the sample of experienced adopters The effect from

coordinating with the local market becomes significantly negative whereas the coefficient for system

share remains positive but loses its significance Also the previous choice plays a significant role in

affecting the current decision The results suggest that chains that are already IT-consolidated tend

to continue with the single vendor strategy for IT procurement The second and fourth columns

present the coefficients for the sample of IT-fragmented chains one for new adopters and the other

for hospitals with EMRs The difference in coefficient magnitudes is much smaller between the two

share variables implying that among chains whose IT structure is already fragmented the choice

for subsidiary members might be less restricted and members could be more responsive to external

market mechanisms In all the subsamples the estimation model provides a reasonable fit with the

data and the Hausman test rejects the hypothesis of exogeneity in the share variables in all of the

Ownership changes

In the last decade the US hospital industry has been experiencing profound organizational

changes namely consolidation of hospitals through mergers and acquisitions One argument in

favor of the ldquobigger is betterrdquo statement is that the integrated organization enables greater effi-

ciency and more coordinated patient care which entails an interoperable IT system However it

remains uncertain whether the merging entities have incentives to move to a common technology

platform given the potential substantial amount of switching costs Holmgren and Adler-Milstein

(2019) summarized the distribution of acquired hospitalsrsquo switching decision on EMR vendors and

found that 35 of acquired hospitals switched to the dominant vendor of the acquiring hospital

post acquisition whereas 44 of them were not using the dominant vendor and did not switch32

In a similar vein I seek to identify hospital incentives in choosing IT systems from the sample of

hospitals that underwent an ownership change

Table 5 shows the results for hospitals that have previously engaged in merger or acquisition

activities The first column reports the coefficients for acquired hospitals that are new adopters

Despite a small sample the estimated network effects remain significantly positive both at the

market and system levels but the intention to follow the ldquonewrdquo parent system is much stronger

The third column shows the results for acquired hospitals with EMRs The coefficients on the share

variables become less significant but the magnitude in the coefficient on system share remains much

larger than that on market share The second and fourth columns present the results based on the

sample of acquiring systems for hospitals with and without EMRs respectively In both columns

the coefficient on market share is significantly positive with a larger magnitude than that on system

share which also loses its significance The results suggest that hospitals from acquiring systems

might have more discretion in responding to the variations in the local market One potential

explanation is that a lot of these systems are IT-fragmented Only 20 of the acquiring chains

are IT-standardized and this fraction goes up to 56 for chains that did not experience merges or

acquisitions during the sample period

Top-level IT governance and support

An IS Steering Committee is a group of key IT and business stakeholders deciding on IT-related

matters for the entire enterprise such as strategic planning program planning project management

and resources allocation Its presence emphasizes the high-level top-down strategies and goal

setting for the overall IT environment to ensure strategic alignment between IT and other business

components as well as the effectiveness of IT investment and services Therefore chains subject

to this governance may have different incentives and implementation plans for EMR adoption

32Holmgren and Adler-Milstein (2019) also define a vendor to be system-dominant if the majority of memberhospitals adopt it

Table 6 presents the results separately for hospitals with and without an IS Steering Committee

The first and third columns based on the sample of chains with the Committee suggest that

hospitals affiliated with these chains tend to follow closely the parent system and stay away from

the vendor that is widely adopted in the local market The second and fourth columns based

on the sample of chains without such a Committee suggest that the effect from choosing a local

popular vendor is significantly positive Among new adopters in this group the coefficient on

system share is insignificant with a smaller magnitude than that on market share Baird et al

(2014) studied the correlation between corporate governance and health IT adoption and found

that strategic alignment and centralization of IT decision rights are associated with higher adoption

rates I further examine the tradeoff between internal constraints and external market forces in the

adoption decision and find that internal consistency dominates in the choice of vendors for chains

characterized by centralized IT governance

Level of vertical integration

Health care is generally characterized as information-intensive and requires cross-functional and

cross-organizational interaction and coordination On one hand chains that are more vertically-

integrated may face higher costs in information transition between units and thus strive for IT

consolidation internally On the other hand they may actively respond to the local environment

if a large number of affiliated non-hospital facilities also serve the local community McCullough

and Snir (2010) studied the effect of physician-hospital integration on (IT-related) monitoring

technology demand and found that the vertical integration is complementary to the adoption of

monitoring technology I explore the incentives in IT adoption for hospital systems with different

levels of vertical integration measured by the number of affiliated ambulatory or sub-acute care

facilities The specification is similar to the main one except that I interact the key variables of

interestmdashthe share variablesmdashwith the measure for vertical integration

Table 7 reports the results for this specification The first column shows the coefficients based

on the sample of new adopters with the share variables interacted with the number of affiliated

ambulatory care facilities The estimates for the baseline variables are similar to those in the

main specification (reported in Table 3) Both interaction terms are significantly positive with

opposite signs Affiliated hospitals enjoy network benefits at the market level which diminish

with the number of ambulatory care facilities associated with the parent system whereas the gains

from choosing the system-dominant vendor increase with this number Hospital chains that own

more units offering complementary services seem to emphasize internal integration to a higher

degree The main results hold in other subsamples as shown in the remaining columns but

the interaction terms are insignificant The finding that network effects vary with the number of

affiliated ambulatory care facilities rather than sub-acute care facilities may be due to the increasing

importance of the outpatient setting

In-house IT infrastructure

In this subsection I identify hospital incentives according to whether a chain is equipped with cer-

tain IT infrastructure that supports the management and usability of data and information I focus

on the following two measures the number of sub-acute care facilities using the same IS platform

as a hospital in the chain and the number of data centers owned by the chain The former pertains

to network enablement for internal connectivity and the latter measures the capability of hardware

andor facilities for data storage processing management and distribution The specification is

the same as the main one except that I interact the variables related to IT infrastructure with the

share variables

Table 8 reports the results for this specification The first column shows the coefficients for the

specification with the share variables interacted with the number of sub-acute care facilities using a

compatible software platform The main results hold except that the baseline coefficient for system

share is a bit smaller than that in the main specification (shown in Table 3) Only the interaction

term involving system share is significantly positive suggesting that parts of the network effect

within the chain are driven by the IT infrastructure that helps reduce the cost of information

acquisition and communication The second column reports the coefficients for the specification

where shares are interacted with the number of owned data centers The negative estimate on the

interaction term with market share suggests that chains that possess sufficient capacity in storing

and managing data enjoy less benefits from adopting a local popular vendor The investment on IT

infrastructure involves a substantial amount of fixed costs As a result chains that have established

infrastructure facilitating IT services are likely to emphasize internal integration to a much greater

extent in order to achieve economies of scale

8 Conclusion and Future Work

In the presence of various incompatible IT systems a hospitalrsquos adoption choice may reveal its

preference between different networks which might reflect a strategic consideration Whether to

integrate into the local market depends on the net effect between complementarities and competition

concerns If benefits from coordinating with nearby hospitals dominates an affiliated hospital

needs to further consider whether to follow the parent system to maintain system consistency or

actively respond to the local business environment I use a simple discrete-choice model to examine

the economic tradeoffs in the adoption decision With further understanding of the underlying

incentives driving hospital behavior policy makers will be better positioned to promote the adoption

of health IT

The preliminary analysis suggests that when the local market leader is different from the system-

dominant vendor a greater number of hospitals choose to follow the parent system This finding is

confirmed by the results from the econometric analysis To control for the unobserved characteristics

at the market and system levels I construct instruments by exploiting the two kinds of spillovers

related to multi-region chains The finding of positive network effects holds at both levels after

addressing endogeneity I further analyze how affiliated hospitals assess the internal and external

network effects across different types of hospital chains I find that affiliated hospitals generally

appreciate a vendorrsquos prevalence in the local market and parent system (with the latter to a greater

extent) except those from chains that are inherently IT-standardized or subject to a high-level IT

governance which might have incentives to deviate from the local market The healthcare sector has

been calling for standardization of health IT systems Policymakers might consider these elements

in future policy designs and in improving subsidy mechanisms to build an integrated information

network

This study has a few limitations First due to data limitation I model the choice of ven-

dors by abstracting away many specifics in the adoption decision However I view most of the

assumptions as reasonable given the focus and scope of this paper Also I estimate the net effect

between complementarities and competition concerns Future studies with access to direct data

on information exchange can uncover the pattern of information transfer between providers and

discuss how complementarities and the competition effect interact with each other Moreover the

model on technology adoption is built on a simple static framework However using a static model

here may still be sensible because the tradeoff between the market-leading and system-dominant

brands might require less dynamic optimization than the fundamental choice of adopting an EMR

system I hope that this paper has provided a building block that can be used in future research

to depict a more complete picture of the strategic consideration of hospital systems and how they

affect market structure and ultimately welfare

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strategies can address it The Milbank Quarterly 95(1)117ndash135

Agha L (2014) The effects of health information technology on the costs and quality of medical

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Angst C M Agarwal R Sambamurthy V and Kelley K (2010) Social contagion and in-

formation technology diffusion The adoption of electronic medical records in US hospitals

Management Science 56(8)1219ndash1241

Atasoy H Greenwood B N and McCullough J S (2019) The digitization of patient care A

review of the effects of electronic health records on health care quality and utilization Annual

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Baird A Furukawa M F Rahman B and Schneller E S (2014) Corporate governance and

the adoption of health information technology within integrated delivery systems Health Care

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Bardey D Cremer H and Lozachmeur J-M (2014) Competition in two-sided markets with

common network externalities Review of Industrial Organization 44(4)327ndash345

Belleflamme P and Toulemonde E (2009) Negative intra-group externalities in two-sided mar-

kets International Economic Review 50(1)245ndash272

Chiou J-S and Droge C (2015) The effects of standardization and trust on franchiseersquos per-

formance and satisfaction a study on franchise systems in the growth stage Journal of Small

Business Management 53(1)129ndash144

Cochet O Dormann J and Ehrmann T (2008) Capitalizing on franchisee autonomy Relational

forms of governance as controls in idiosyncratic franchise dyads Journal of Small Business

Management 46(1)50ndash72

Cooper Z Craig S V Gaynor M and Van Reenen J (2018) The price aint right hospi-

tal prices and health spending on the privately insured The Quarterly Journal of Economics

134(1)51ndash107

Cox J and Mason C (2007) Standardisation versus adaptation geographical pressures to deviate

from franchise formats The Service Industries Journal 27(8)1053ndash1072

Dant R P and Gundlach G T (1999) The challenge of autonomy and dependence in franchised

channels of distribution Journal of Business Venturing 14(1)35ndash67

Desai S (2016) The role of interoperability in hospital digitization Network effects and informa-

tion blocking Working paper NYU School of Medicine available at httpscholarharvard

edufilesdesaifilesInteroperabilityWP_Desaipdf

EDM Forum (2013) Informatics tools and approaches to facilitate the use of electronic data for

CER PCOR and QI Resources developed by the PROSPECT DRN and enhanced registry

projects Issue Briefs and Reports (11)

Freedman S Lin H and Prince J (2018a) Does competition lead to agglomeration or dispersion

in EMR vendor decisions Review of Industrial Organization 53(1)57ndash79

Freedman S Lin H and Prince J (2018b) Information technology and patient health Analyz-

ing outcomes populations and mechanisms American Journal of Health Economics 4(1)51ndash79

Furukawa M F Patel V Charles D Swain M and Mostashari F (2013) Hospital electronic

health information exchange grew substantially in 2008ndash12 Health Affairs 32(8)1346ndash1354

Gowrisankaran G and Stavins J (2004) Network externalities and technology adoption Lessons

from electronic payments The RAND Journal of Economics 35(2)260ndash276

Holmgren A J and Adler-Milstein J (2019) Does electronic health record consolidation follow

hospital consolidation Health Affairs Blog Available at httpswwwhealthaffairsorg

do101377hblog20150304

Jha A DesRoches C Campbell E Donelan K Rao S Ferris T Shields A Rosenbaum

S and Blumenthal D (2009) Use of electronic health records in US hospitals New England

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Kaufmann P J and Eroglu S (1999) Standardization and adaptation in business format fran-

chising Journal of Business Venturing 14(1)69ndash85

Kazley A S and Ozcan Y A (2007) Organizational and environmental determinants of hospital

EMR adoption A national study Journal of Medical Systems 31(5)375ndash384

Kellermann A and Jones S (2013) What it will take to achieve the as-yet-unfulfilled promises

of health information technology Health Affairs 32(1)63ndash68 Available at httpswww

healthaffairsorgdoifull101377hlthaff20120693

Kurucu G (2007) Negative network externalities in two-sided markets A competition approach

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Lee J McCullough J S and Town R J (2013) The impact of health information technology

on hospital productivity The RAND Journal of Economics 44(3)545ndash568

Lin J and Olson M K (2019) Does health IT save money and lives New evidence from vendor

heterogeneity Working Paper Rensselaer Polytechnic Institute

Makuc D Haglund B Ingram D Kleinman J and Feldman J (1991) Health service areas for

the United States Vital and Health Statistics Series 2 Data Evaluation and Methods Research

(112)1

McCullough J Casey M Moscovice I and Prasad S (2010) The effect of health information

technology on quality in US hospitals Health Affairs 29(4)647ndash654

McCullough J S Parente S T and Town R (2016) Health information technology and patient

outcomes The role of information and labor coordination The RAND Journal of Economics

47(1)207ndash236

McCullough J S and Snir E M (2010) Monitoring technology and firm boundaries Physicianndash

hospital integration and technology utilization Journal of Health Economics 29(3)457ndash467

Miller A and Tucker C (2009) Privacy protection and technology diffusion The case of electronic

medical records Management Science 55(7)1077ndash1093

Miller A and Tucker C (2011) Can health care information technology save babies Journal of

Political Economy 119(2)289

Miller A R and Tucker C (2014) Health information exchange system size and information

silos Journal of Health Economics 3328ndash42

Najaftorkaman M Ghapanchi A H Talaei-Khoei A and Ray P (2015) A taxonomy of an-

tecedents to user adoption of health information systems A synthesis of thirty years of research

Journal of the Association for Information Science and Technology 66(3)576ndash598

Orszag P (2008) Evidence on the costs and benefits of health information technology Testimony

before Congress 241ndash37

Rochet J-C and Tirole J (2002) Cooperation among competitors Some economics of payment

card associations RAND Journal of Economics 33(4)549ndash570

Rochet J-C and Tirole J (2003) Platform competition in two-sided markets Journal of the

European Economic Association 1(4)990ndash1029

Rochet J-C and Tirole J (2006) Two-sided markets A progress report The RAND Journal of

Economics 37(3)645ndash667

Simon S R Kaushal R Cleary P D Jenter C A Volk L A Poon E G Orav E J Lo

H G Williams D H and Bates D W (2007) Correlates of electronic health record adoption

in office practices A statewide survey Journal of the American Medical Informatics Association

14(1)110ndash117

Sorenson O and Soslashrensen J B (2001) Finding the right mix Franchising organizational learn-

ing and chain performance Strategic Management Journal 22(6-7)713ndash724

Tucker C (2018) Network effects and market power What have we learned in the last decade

Antitrust Spring pages 72ndash79

Van Der Meijden M Tange H J Troost J and Hasman A (2003) Determinants of success

of inpatient clinical information systems A literature review Journal of the American Medical

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Vest J R and Simon K (2018) Hospitalsrsquo adoption of intra-system information exchange is

negatively associated with inter-system information exchange Journal of the American Medical

Wang Y (2019) Competition and multilevel technology adoption A dynamic analysis of electronic

medical records adoption in US hospitals Working paper Renmin University of China

Figure 1 Adoption and switching rates over time

Note Upper panel shows the adoption rate and lower panel shows the rate of switching Appendix

Table A1 provides more detail on these variables

Figure 2 Frequency distribution of the choice of vendors

Note Each panel shows the frequency distribution of the choice of vendors for a particular type of

chains Appendix Table A2 provides more detail Calculation is based on affiliated hospitals that

are new adopters and for which no overlap exists between the set of market- and system-dominant

vendors

Figure 3 CDFs of number of leading vendors per market

Note Leading vendors per market taken from Table 1

Figure 4 CDFs of market share for dominant local vendor

Note Each panel shows the CDFs of a particular vendorrsquos local market share among all the markets

where this vendor is the local dominant vendor that is has the highest local market share

Figure 5 Vendor distribution across markets in 2009

Note Distribution of vendors across geographic areas in 2009 Each patch represents a local

market and each color represents a particular vendor Each dot stands for approximately 10 of

local market share for the represented vendor The clustering of dots of the same color within

a market measures the extent to which the represented vendor dominates the local market Note

that the location of dots is randomly assigned by the software and does not pinpoint exact locations

Figure 6 CDFs of number of leading vendors per chain

Figure 7 CDFs of system share for system-dominant vendor

Note Each panel shows the CDFs of a particular vendorrsquos system share among all the systems

where this vendor is the dominant vendor that is has the highest system share

Figure 8 Example of how to construct instruments for market share

Note Example of how to construct the instruments for the market share variable There are three

markets m1 m2 and m3 Hospital A1 is the focal hospital Hospital B1 belongs to chain B the

rest of whose members B2 B3 B4 B5 are located in markets m2 and m3

Figure 9 Example of how to construct instruments for system share

Note Example of how to construct the instruments for the system share variable There are three

markets m1 m2 and m3 Hospital A1 is the focal hospital along with A2 belonging to chain A

whereas B1 B2 B3 and B4 belong to chain B

Table 1 Top 11 EMR vendors and their national market share ()

2005 2006 2007 2008 2009 2010 2011 2012 2013

Meditech 3025 291 2807 284 2691 2587 2499 2362 226McKessons 1391 1264 1107 1113 1202 1123 1095 1032 1007Siemens 1201 1185 1061 961 915 865 79 706 633Cerner 1088 1167 1127 1125 1158 1194 1241 1291 14Self-developed 887 763 629 546 476 467 436 457 407CPSI 476 568 805 842 823 88 909 897 886Eclipsys 335 39 333 339 348 35 ndash ndash ndashHMS 162 231 309 301 426 431 461 471 505Epic 157 2 329 395 572 76 102 1269 1628Healthland 087 191 346 395 431 421 456 469 452GE 022 333 279 251 23 207 111 08 066

Total percentage 8831 9201 9132 9109 9271 9286 9017 9033 9244

Note Calculation is based on all hospitals including stand-alone and affiliated hospitals in 2005-2013 Thevendor ldquoEclipsysrdquo merged with another vendor at the end of 2010 Thus I exclude this vendor in years2011-2013

Table 2 Summary of key variables

Hospital characteristics

2005 2006 2007 2008 2009 2010 2011 2012 2013 whose chosen vendor ismarket-dominant

200 247 278 301 318 330 342 345 363

whose chosen vendor issystem-leading

359 460 531 598 648 633 650 672 726

ever not-for-profit hos-pitals

660 662 665 667 671 683 688 680 682

ever teaching hospitals 87 86 86 85 84 85 84 83 83 beds 222 216 215 214 216 217 217 215 216 acquired hospitals 39 62 101 68 72 79 99 94 255

Total affiliated hospi-tals

2423 2458 2467 2488 2514 2508 2543 2608 2645

Market characteristics2005 2006 2007 2008 2009 2010 2011 2012 2013

stand-alone hospitals 22 23 23 22 22 22 21 21 20 affiliated hospitals 26 27 27 27 27 27 28 28 29 leading vendors 10 12 16 18 20 21 21 22 22 chains 16 16 16 16 17 16 16 17 17

Total markets 915 921 921 921 921 921 920 920 920

Chain characteristics2005 2006 2007 2008 2009 2010 2011 2012 2013

members 58 58 58 59 59 60 60 63 65 markets 36 36 35 36 36 36 36 38 39 leading vendors 08 10 11 13 13 14 14 14 14 ambulatory facilities 279 288 301 316 319 375 430 483 635 sub-acute care facilities 48 46 45 40 38 36 36 39 42 sub-acute care facilitiesusing the same IS plat-form

19 14 14 12 13 11 09 10 11

data centers 07 08 08 08 08 09 09 10 11 systems with IS Steer-ing Committee

107 120 108 95 82 100 93 99 105

acquiring systems 38 83 92 102 94 114 116 118 132

Total chains 421 424 426 421 425 420 421 415 410

Note Table reports the mean value of statistics over the years 2005-2013

Table 3 Effects from market- or system-dominant vendor

Hospitals without EMRs Hospitals with EMRsAll IV sample All IV sample

Logit Logit CF Logit Logit CF

Market share 0585lowastlowastlowast 0732lowastlowast 2424lowastlowast 00253 0462lowast 2127lowastlowastlowast

(0122) (0300) (1177) (00876) (0245) (0731)

System share 2629lowastlowastlowast 2926lowastlowastlowast 4929lowastlowastlowast 1591lowastlowastlowast 2634lowastlowastlowast 2863lowastlowastlowast

(00757) (0155) (1605) (00601) (0138) (1022)

Chosen previously ndash ndash ndash 3891lowastlowastlowast 3939lowastlowastlowast 3966lowastlowastlowast

ndash ndash ndash (00403) (0102) (0107)

N 55627 15704 219432 52776Pseudo R2 0584 0646 0651 0828 0882 0883P-value for joint significance of ndash ndash 0000714 ndash ndash 000000174error terms from the first stage

Note Other regressors include not-for-profit indicator post-HITECH Act indicator vendor fixed effects and vendordummies interacting with the following controls bed size number of member hospitals and teaching hospital indicatorStandard errors in parentheseslowast p lt 010 lowastlowast p lt 005 lowastlowastlowast p lt 001

Table 4 Evidence by level of IT standardization

Hospitals without EMRs Hospitals with EMRsIT-standardized

IT-fragmented

IT-standardized

IT-fragmented

Market share 8513lowastlowastlowast 2069 -6358lowastlowastlowast 2173lowastlowastlowast

(3058) (1659) (1620) (0772)

System share 2959lowastlowastlowast 4253lowastlowast 4007 2331lowastlowast

(7403) (1934) (3704) (1035)

Chosen previously 5929lowastlowastlowast 3980lowastlowastlowast

(1891) (0114)

N 4303 8931 13580 35820Pseudo R2 0775 0627 0976 0847P-value for joint signifi-cance of error terms fromthe first stage

000674 0000195 355e-16 905e-10

Note Other regressors include not-for-profit indicator post-HITECH Act indicator vendor fixed effectsand vendor dummies interacting with the following controls bed size number of member hospitals andteaching hospital indicator Standard errors in parentheseslowast p lt 010 lowastlowast p lt 005 lowastlowastlowast p lt 001

Table 5 Evidence by ownership changes

Hospitals without EMRs Hospitals with EMRsAcquiredhospitals

Acquiringsystems

Acquiredhospitals

Acquiringsystems

Market share 9516lowastlowast 6494lowast 0914 3541lowastlowastlowast

(3797) (3458) (3002) (1352)

System share 8036lowastlowastlowast 1552 1119lowast 0846(3075) (3797) (6256) (1384)

(0579) (0202)

00239 000000267 284e-10 0000261

Note Other regressors include not-for-profit indicator post-HITECH Act indicator vendor fixedeffects and vendor dummies interacting with the following controls bed size number of memberhospitals and teaching hospital indicator Standard errors in parentheseslowast p lt 010 lowastlowast p lt 005 lowastlowastlowast p lt 001

Table 6 Evidence by top-level IT support

Hospitals without EMRs Hospitals with EMRsWith ISSteeringCommittee

WithoutIS SteeringCommittee

With ISSteeringCommittee

Market share -2039 4039lowastlowastlowast -3708 2227lowastlowastlowast

(2392) (1327) (2721) (0756)

System share 1307lowastlowast 3592 7210lowastlowastlowast 4494lowastlowastlowast

(5084) (2237) (2480) (1549)

(0525) (0120)

0126 0000101 0000261 0000000138

Table 7 Evidence by level of vertical integration

Hospitals without EMRs Hospitals with EMRs ambula-tory facili-ties

sub-acutecare facilities

ambula-tory facili-ties

Market share 3292lowastlowast 2727lowastlowast 2175lowastlowastlowast 2095lowastlowastlowast

(1311) (1163) (0756) (0754)

System share 4343lowastlowastlowast 4301lowastlowastlowast 2764lowastlowastlowast 2802lowastlowastlowast

(1635) (1565) (1043) (1017)

(0110) (0109)

Interacted with market minus000724lowast minus00281 0000401 00118share indicator (000401) (00182) (000138) (000724)

Interacted with system 000507lowastlowast 000832 0000553 0000924share indicator (000246) (000968) (000156) (000599)

0000779 0000173 00000183 000000224

Note Other regressors include not-for-profit indicator post-HITECH Act indicator vendor fixed effects andvendor dummies interacting with the following controls bed size number of member hospitals and teachinghospital indicator Standard errors in parentheseslowast p lt 010 lowastlowast p lt 005 lowastlowastlowast p lt 001

Table 8 Evidence by in-house IT infrastructure

Hospitals without EMRs Hospitals with EMRs sub-acute carefacilities using thesame IT softwareplatform

datacenters

sub-acute carefacilities using thesame IT softwareplatform

datacenters

Market share 2579lowastlowast 3047lowastlowast 2280lowastlowastlowast 1846lowastlowast

(1217) (1210) (0731) (0773)

System share 3791lowastlowast 4900lowastlowastlowast 2830lowastlowastlowast 3026lowastlowastlowast

(1649) (1632) (1012) (1072)

(0107) (0110)

Interacted with market 00151 minus0424lowastlowast minus000152 000720share indicator (00263) (0181) (000620) (00770)

Interacted with system 00617lowastlowastlowast minus0111 -000658 minus00238share indicator (00118) (0130) (000479) (00896)

0000654 000172 0000000772 00000597

Appendix I

Figure A1 Example of outside associated markets in the data

Note Example of outside associated markets for a focal hospital located in Florida represented by a

star in the subfigure The subfigure shows the area after zooming in on the focal market in the map

Figure A2 Example of associated chains in the data

Note Example of associated chains for a focal chain Each subfigure represents an important

market for the focal chain with the right one showing the overlapped market that is important for

the focal chain but small for the associated chains

Table A1 Adoption and switching rates

Adoption rate Switching rateStand-alone Affiliated Stand-alone Affiliated

hospitals hospitals hospitals hospitals

2005 330 485 11 432006 403 572 20 442007 605 706 32 472008 702 791 38 702009 770 899 37 662010 801 925 72 1102011 846 941 48 812012 883 947 54 942013 917 965 56 95

Table A2 Percentage distribution of choosing between market- and system-dominant vendors

Overall Chains withsingle vendor

Chains withmultiplevendors

Acquiringsystems

Acquiredhospitals

Systems withIS SteeringCommittee

Market-dominant

101 594 127 769 10 804

System-dominant

574 767 449 641 45 464

Neither 326 173 424 282 45 455

N 516 202 314 78 40 112

Note Calculation is based on affiliated hospitals that are new adopters and for which no overlap exists between theset of market- and system-dominant vendors Therefore in each column percentage sums to one

Table A3 Summary statistics for IV samplemdashmarket characteristics

Focal markets2005 2006 2007 2008 2009 2010 2011 2012 2013

outside associated mar-kets per focal hospital

56 57 57 61 6 61 6 53 51

Adoption rate () 416 502 645 698 841 878 869 909 950 stand-alone hospitals 29 30 30 30 29 27 27 27 25 affiliated hospitals 50 50 50 51 50 51 51 52 52 leading vendors 15 18 23 26 29 29 29 30 30 chains 30 30 30 30 30 30 30 30 31

Total markets 339 329 330 334 339 328 329 341 363

Outside associated markets2005 2006 2007 2008 2009 2010 2011 2012 2013

Total markets 48 48 49 49 51 50 54 50 47

Table A4 Summary statistics for IV samplemdashchain characteristics

Focal chains2005 2006 2007 2008 2009 2010 2011 2012 2013

associated chains perfocal chain

67 68 68 70 68 63 61 66 69

Adoption rate () 325 439 625 744 897 900 875 930 950 members 265 262 268 276 274 266 271 270 288 markets 185 182 183 190 188 178 180 180 193 leading vendors 13 15 20 22 25 27 27 25 26 ambulatory facilities 681 721 599 745 770 1127 1293 1339 1797 sub-acute care facilities 182 180 180 181 174 157 165 168 193 sub-acute care facilitiesusing the same IS plat-form

108 100 108 100 103 83 82 77 81

175 341 250 205 179 250 225 163 175

Total chains 40 41 40 39 39 40 40 43 40

Associated chains2005 2006 2007 2008 2009 2010 2011 2012 2013

54 46 46 42 43 35 36 36 35

160 198 181 128 128 165 144 141 131

Total chains 94 96 94 94 94 91 90 99 107

Table A5 First-stage results in the CF

Dependent variable Market shareHospitals without EMRs Hospitals with EMRs

Share in outside associated 0394lowastlowastlowast 0499lowastlowastlowast

markets (00115) (000561)

Dominance indicator in outside 0046lowastlowastlowast minus00201lowastlowastlowast

associated markets (000599) (000277)

N 29029 122148F -statistics 2056 9007

Dependent variable System shareHospitals without EMRs Hospitals with EMRs

Share in associated chains 0287lowastlowastlowast 0127lowastlowastlowast

(0025) (00162)

Dominance indicator in 000309 0169lowastlowastlowast

associated chains (00208) (0013)

N 29458 90468F -statistics 566 1728

Note The constant term is not reported lowast p lt 010 lowastlowast p lt 005 lowastlowastlowast p lt 001

Table A6 Effects from market- or system-dominant vendor weighted by bed size

Market share 0529lowastlowastlowast 0608lowastlowast 1300 00892 0293 1682lowastlowast

(0114) (0272) (1203) (00820) (0228) (0750)

System share 2604lowastlowastlowast 2896lowastlowastlowast 4490lowastlowast 1559lowastlowastlowast 2502lowastlowastlowast 4300lowastlowastlowast

(00748) (0153) (1893) (00585) (0134) (1226)

Chosen previously 3911lowastlowastlowast 3981lowastlowastlowast 3968lowastlowastlowast

(00398) (0100) (0104)

N 55627 15834 219432 52896Pseudo R2 0584 0643 0647 0829 0880 0881P-value for joint significance of 000289 00000351error terms from the first stage

Table A7 Effects from market- or system-dominant vendor accounting for non-hospital facilities

Market share 0607lowastlowastlowast 0741lowast 1992 0254lowastlowastlowast 0613lowastlowast 2296lowastlowast

(0118) (0382) (1523) (00899) (0302) (1078)

(00769) (0187) (0614) (00608) (0180) (0291)

(00422) (0121) (0124)

N 50154 10478 10478 196956 37884 37884Pseudo R2 0572 0612 0622 0826 0882 0883P-value for joint significance of 790e-08 00123error terms from the first stage

Table A8 Effects from market- or system-dominant vendor based on HRRs

Market share 0611lowastlowastlowast 0555lowastlowast 0728 0130 0720lowastlowastlowast 2444lowastlowastlowast

(0153) (0281) (0939) (0120) (0266) (0727)

(00769) (0134) (1101) (00607) (0107) (0692)

(00404) (00816) (00836)

N 55601 23374 23374 219240 70944 70944Pseudo R2 0584 0646 0650 0828 0869 0870P-value for joint significance of 00000201 00000112error terms from the first stage

Introduction

Relation to the Literature

Industry Background

Data sources and important variable definitions

Summary statistics and suggested evidence

Model

Estimation Strategy

Non-IV estimation

IV estimation

Results

Evidence from overall sample

Evidence by system heterogeneity

Conclusion and Future Work