the trillion dollar conundrum: complementarities and health
TRANSCRIPT
The Trillion Dollar Conundrum: Complementarities and Health Information Technology
February 2012
David Dranove (Northwestern University) Chris Forman (Georgia Institute of Technology)
Avi Goldfarb (University of Toronto) Shane Greenstein (Northwestern University)
Abstract
We examine the relationship between the adoption of electronic medical records (EMR) and hospital operating costs at thousands of US hospitals between 1996 and 2008. We combine data from the American Hospital Association Annual Survey, the Healthcare Information and Management Systems Society Analytics Database, the US census, and Harte Hanks Market Intelligence information technology survey. We first identify a puzzle that has been seen in prior studies: Adoption of EMR is generally associated with a slight increase in costs. We argue that this average masks important differences over time, across locations, and across hospitals. Drawing on the literature on information technology as a business process innovation, we show that: (1) EMR adoption is initially associated with a rise in costs; (2) the initial increase in costs is mitigated in hospitals with some internal information technology expertise,; (3) Hospitals in locations that have local information technology expertise can experience a decrease in costs after two years; and (4) Hospitals in other locations experience a sharp increase in costs even after six years.
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I. Introduction
As annual U.S. healthcare expenditures climb towards $3 trillion (with spending forecast to exceed $4.5 trillion by 2020), many analysts hope that electronic medical records (EMR) can stem the tide (Centers for Medicare & Medicaid Services). Melinda Beeuwkes Buntin and David Cutler make EMR the centerpiece of their âTwo Trillion Dollarâ solution for modernizing the health care system (Buntin and Cutler 2009). Yet, the Congressional Budget Office states: âNo aspect of health information technology entails as much uncertainty as the magnitude of its potential benefitsâ (Congressional Budget Office 2008).1
A small sampling of research from the last half dozen years provides a sense of the uncertainty. A widely cited 2005 report by the RAND Corporation, published in the leading policy journal Health Affairs, estimates that widespread adoption of EMR by hospitals and doctors could reduce annual health spending by as much as $81 billion while simultaneously leading to better outcomes (Hillestad et al. 2005). Jaan Sidorov, a medical director with the Geisinger Health Plan, an early adopter of EMR, published a response to the RAND report in Health Affairs. Sidorov (2006) notes the high cost of adoption and cites evidence that EMR leads to greater health spending and reduced provider productivity. Other recent studies, cited below, fail to find consistent evidence that EMR savings offset adoption costs. Thus, it remains uncertain whether EMR can fulfill its promise and bring about major reductions in health spending.
This study speaks to this conundrum and reframes this debate. We characterize EMR in terms of the impact new information technology (IT) has on existing enterprises. Specifically, we view EMR as a business process innovation, which is a change in the operational practices inside the adopting organization. Like other business process innovations, the impact of new IT such as EMR depends on its complementary with local labor markets, on its complementary with internal expertise, and on the functional heterogeneity in the new processâ components. We conclude that it is premature to assess the impact of EMR without consideration of these three factors. We apply this reframing to statistical data, examining the impact of EMR adoption on hospital operating costs during the period 1996 to 2008. We demonstrate the importance of complementarities with local conditions and internal expertise by testing for differences across regional labor markets and across hospitals with different prior experiences with management technology. We test for functional heterogeneity by distinguishing between the introduction of the more basic and more advanced types of healthcare information technologies.
We combine several datasets to link hospital costs to EMR adoption and the potential for complementarities.2 The data reveal a complex pattern of expenditures and savings that is remarkably consistent with the framing of the problem. We find that, on average, hospitals that adopted EMR between 1996 and 2008 did not experience a statistically significant increase in operating efficiency. In fact, under many specifications costs rose after EMR adoption. However, this effect is mediated by the availability of technology skills in the local labor market. Specifically, in strong IT locations costs fall sharply after the first year of adoption to below pre-
1 The term Health Information Technology is often used interchangeably with EMR. 2 We use data from the American Hospital Association Annual Survey (hospital characteristics), from the medical costs report (hospital costs), from the Healthcare Information and Management Systems Society Analytics Database (hospital EMR adoption), from the US census (local demographics and industry), and from Harte Hanks Market Intelligence information technology survey (local business IT, hospital IT capabilities in 1996).
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adoption levels. In weak IT locations, costs remain above pre-adoption levels indefinitely. Overall, hospitals in high skill markets enjoyed a statistically significant four percent decrease in costs two years after adoption basic EMR and no increase in costs two years after adoption of advanced EMR. These are significantly lower than the two to three percent rise in costs after adoption by hospitals in other markets. Complementary skills can also be found in the hospital itself. For advanced EMR, the initial increase in costs is mitigated substantially if hospitals already employ a programmer or if hospitals have used a clinical software application prior to adoption.
Figure 1 displays these general patterns, comparing hospitals that adopt basic and advanced EMR before the adoption period, during the adoption period, and after the adoption period. For basic EMR, costs do not fall until two years after adoption. For non-IT intensive locations, costs rise sharply in the year of adoption, and then fall back. For IT intensive locations, costs fall with adoption, and are substantially lower two years after adoption. Hospitals with programmers by 1996 display a similar pattern (though the results on basic EMR adoption are not significant). For advanced EMR, the patterns are similar but sharper: costs rise in the period of adoption for non IT intensive locations and hospitals without programmers and costs fall over time for the other hospitals.
By providing an explanation for the mixed results of past studies, our analysis informs the understanding of EMRâs sluggish diffusion. As of 2009, only about 60-70 percent of Americaâs hospitals have adopted key elements of EMR.3 So sluggish was this adoption that it motivated public intervention. In order to spur EMR adoption, Congress in 2009 passed the Health Information Technology for Economic and Clinical Health Act (EMRECH Act), which provides $20 billion in subsidies for providers who adopt EMR. Two thirds of hospitals say they plan to enroll in the first stage of EMRECH subsidy programs by the end of 2012 (US Department of Health and Human Services 2011). In addition, the 2010 Patient Protection and Affordable Care Act promote EMR adoption. PPACA directs the establishment of quality reporting measures that likely will require providers to adopt EMR in order to comply. PPACA creates a new âshared savingsâ program for Medicare; participation in this program is predicated on the use of EMR. Finally PPACA encourages providers to apply to participate in a range of new programs and gives preference to those that have adopted EMR.
Most importantly, our findings may help resolve the ongoing debate between supporters and detractors of EMR. Both sides have seemed to treat EMR as if its economic impact is independent of other environmental factors, as if it either works or it doesnât. This creates a conundrum for both sides. If EMR is going to save as much as a trillion dollars, as its supporters claim, why isnât it working in obvious ways? If it costs more than it saves, as its detractors argue, why are policy makers so keen to expand adoption? Our results suggest that the debate about EMR can be resolved by drawing on the general literature on business process innovation, where it is very common for successful adoption of enterprise IT to require complementary changes in business processes and related labor inputs. It is also common for new enterprise IT to be more productive when companies have access to these inputs in their local labor market. Using this experience, it is not surprising that EMR can simultaneously have the potential to generate substantial savings yet demonstrate mixed results in practice.
3 Source: Authorsâ calculations based on data supplied by HIMSS.
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Our findings therefore provide a possible resolution to the trillion dollar conundrum. EMR can succeed when the necessary complementarities are present and the complementary components are in place. Until then the results, at best, can be only mixed. Over time, these complementarities are expected to become more widely available, and the various components more widely deployed. If so, EMR may yet fulfill its promise. While EMRâs past mixed performance is no guarantee of a future result, the past experience also is no guarantee of future failure. As these skills spread, hospitals in other markets may eventually enjoy the same benefits.
We proceed as follows. Sections II and III describe the institutional setting for EMR, and some of the prior evidence about its effects on hospitals. This motivates a comparison in Section IV between EMR and the adoption of IT inside organizations. That leads to a reframing of several key hypotheses. Sections V and VI present data and results. Section VII concludes.
II. What is EMR?
It is essential to define terms. EMR is a catchall expression used to characterize a wide range of information technologies used by hospitals to keep track of utilization, costs, outcomes, and billings. In practice, EMR includes, but is not limited to:
A Clinical Data Repository (CDR) is a real time database that combines disparate information about patients into a single file. This information may include test results, drug utilization, pathology reports, patient demographics, and discharge summaries
Clinical Decision Support Systems (CDSS) use clinical information to help providers diagnose patients and develop treatment plans
Order Entry provides electronic forms to streamline hospital operations (replacing faxes and paper forms).
Computerized Physician Order Entry (CPOE) is a more sophisticated type of electronic order entry and involves physician entry of orders into the computer network to medical staff and to departments such as pharmacy or radiology. CPOE systems typically include patient information and clinical guidelines, and can flag potential adverse drug reactions.
Physician Documentation helps physicians use clinical information to generate diagnostic codes that are meaningful for other practitioners and valid for reimbursement
As this list shows, there is no one single technology associated with EMR, and different EMR technologies may perform overlapping tasks. Our data from HIMSS Analytics contain biannual hospital-level adoption data for each of these technologies. Therefore, we are able to explore how different technologies might affect costs in different ways.
Nearly all of the information collected by EMR resides in hospital billing and medical records departments and in physiciansâ offices. EMR automates the collection and reporting of this information, including all diagnostic information, test results, and services and medications received by the patient. EMR can also link this information to administrative data such as insurance information, billing, and basic demographics. EMR can reduce the costs and improve the accuracy of this data collection. Two components of EMR, Clinical Decision Support Systems and Computerized Physician Order Entry, use clinical data to support clinical decision making. If implemented in ideal conditions and executed according to the highest standards,
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EMR can reduce personnel costs while facilitating more accurate diagnoses, fewer unnecessary and duplicative tests, and superior outcomes with fewer costly complications.
Despite these potential savings, EMR adoption has been uneven. Table 1 reports hospital adoption rates for the five components of EMR described above. The data is taken from HIMSS Analytics, which we describe in more detail in section V. Clinical Data Repository, Clinical Decision Support, and Order Entry are older technologies that were present in many hospitals in the 1990s. Even for these older technologies, adoption rates were below 85 percent in 2008. The remaining applications emerge in the early to mid-2000s. Adoption rates for these are below 25 percent.
While informative, Table 1 lacks several crucial pieces of information. It lacks comparable data on physician adoption of EMR, for example. The conventional wisdom is that physician adoption rates are much lower. In addition, adoption of such systems provides no information about intensity of use, or on how close operations come to ideal conditions. Once again, conventional wisdom suggests that many hospitals have experienced a wide range of outcomes, and in some cases this is due to poorly executed installations, or lack of ideal conditions for hiring skilled talent.
Although beyond the scope of this study, compatibility issues also may shape the success of EMR at a regional level, and this too is missing from the table. There are many different EMR vendors and their systems do not easily interoperate. As a result, independent providers cannot always exchange information, which defeats some of the purpose of EMR adoption (Miller and Tucker 2009). The EMRECH Act widens the scope of privacy and security protections and may therefore make it easier for different vendor systems to exchange information in the future.
III. Evidence on the Potential Savings from EMR
Has the adoption of EMR yielded gains to prior adopters? This section reviews prior evidence, stressing the absence of work focusing on operational savings, lack of emphasis on complementarities with the labor market, and the absence of accounting for the functional heterogeneity of EMRâs components. This discussion will motivate our concern with these aspects.
EMR is expensive. One prominent estimate, from the Congressional Budget Office, estimates that the cost of adopting EMR for office-based physicians lay between $25,000 and $45,000 per physician, with annual maintenance costs of $3000 to $9000. For a typical urban hospital, these figures range from $3-9 million for adoption and $700,000-$1.35 million for maintenance. In context these costs are quite significant: If the adoption costs are amortized over ten years, EMR can account for about 1 percent of total provider costs. It would be no surprise, therefore, if available research suggests that EMR may not pay for itself, let alone generate hundreds of millions of dollars in savings.
In their review of 257 studies of EMR effectiveness, Chaudry et al. (2006) note that most studies to date do not focus on cost savings, providing at best just indirect evidence of productivity
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gains.4 Most of the studies they review focus on quality of care. Ten studies examine the effects of EMR on utilization of various services. Eight studies show significant reductions of 8.5-24 percent, mainly in laboratory and radiology testing. While fifteen studies contained some data on costs, none offered reliable estimates of cost savings. Indeed, only three reported the costs of implementing EMR and two of these studies were more than years old.
One of the most widely cited studies about costs savings, Hillestad et al. (2005) (the RAND study cited in our introduction), uses results from studies of EMR and medical utilization up until 2005, and extrapolates the potential cost savings net of adoption costs. They identify several dozen potential areas of cost savings, including reduced drug, radiology, and laboratory usage, reduced nursing time, reductions in clerical staff, fewer medical errors, and shorter inpatient lengths of stay. They estimate that if 90 percent of U.S. hospitals were to adopt EMR, total savings in the first year would equal $41.8 billion, rising to $77.4 billion after fifteen years. They also predict that EMR adoption could eliminate several million adverse drug events annually, and save tens of thousands of lives annually through improve chronic disease management.
Sidorov (2006) challenges these findings, arguing that the projected savings are based on unrealistic assumptions. For example, the RAND study appears to assume that EMR would entirely replace a physicianâs clerical staff. It is well-known, however, that providers who adopt EMR tend to reassign staff rather than replace them. To take another example, EMR is supposed to eliminate duplicate tests, while it is just as likely that, in reality, EMR may allow providers to justify ordering additional tests. Sidorov also questions whether EMR will generate forecasted reductions in medical errors.
Enough time has passed to compare RANDâs forecasts against actual experience. The most recent review of the literature on EMR effectiveness is by Buntin et al. (2011). They identify 73 studies of EMR and medical utilization. EMR is associated with a significant reduction in utilization in 51 (70 percent) of these studies. They do not break these down into specific areas of savings, however. Moreover, Buntin et al. do not identify any studies of EMR and costs. To our knowledge, such studies remain few and far between.
Indeed, we have identified only three focused cost study. Borzokowski (2009) uses fixed effects regression to examine whether early versions of financial and clinical information technology systems generated significant savings between 1987 and 1994. He finds that the most thoroughly automated hospitals generate up to 5 percent savings within five years of adoption. He also finds that hospitals that adopt EMR but are not the most thoroughly automated experience an increase in costs. In this way, his conclusions mirror the popular discussion: there appears to be the potential for savings but there is little understanding of the drivers of the heterogeneity across hospitals. Furukawa, Raghu, and Shao (2010) study the effect of EMR adoption on overall costs among hospitals in California for the period 1998-2007. Using fixed effects regression, they find that EMR adoption is associated with 6-10 percent higher costs per discharge in medical-surgical acute units, in large part because nursing hours per patient day increased by 15-26 percent. This is plausible because nurse use of EMR can be very time consuming. Finally, Agha (2012) uses
4 Chaudry et al state that they are studying Health Information Technology and they do not indicate if they distinguish between HIT and EMR.
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variation in hospitalsâ adoption status over time, analyzing 2.5 million inpatient admissions across 3900 hospitals between the years, 1998-2005. Health IT is associated with an initial 1.3 percent increase in billed charges. She finds no evidence of cost savings, even five years after adoption. Additionally, adoption appears to have little impact on the quality of care, measured by patient mortality, medical complication rates, adverse drug events, and readmission rates.
None of the studies to date frame EMR as a business process innovation. In other words, there is no effort to investigate whether the impact of EMR might vary systematically with the type of EMR adopted, or with the factors that shape availability of complementary components, such as the characteristics of the local settings. This may be due to the absence of a theory that would suggest such differential effects. In the next section, we offer such a theory.
IV. Information Technology and Complementarities
Business process innovations alter organizational practices, generally with the intent of improving services, reducing operational costs, and taking advantage of new opportunities to match new services to new operational practices. Typically this type of innovation involves changes in the discretion given to employees, changes to the knowledge and information that employees are expected to retain and employ, and changes to the patterns of communications between employees and administrators within an organization. Because important business process innovations occur on a large scale, they typically involve a range of investments, both in computing hardware and software, and in communications hardware and software. They also involve retraining of employees, as well as redesign of organizational architecture, such as its hierarchy, lines of control, compensation patterns and oversight norms.
Large scale business process innovations do not necessarily display patterns observed in familiar and standard productivity models for new product or process innovations. Business process innovations within enterprises do not necessarily follow a single diffusion curve, as typically found in a monolithic new product or process replacing an older one. Related, the first investments associated with adoption do not necessarily and reliably generate a productivity impact. This is because the sequence of complementary investments and organizational changes after the initial adoption play such an important role in determining the eventual productivity of the changes. By itself new IT has little impact.
Prior studies place stress the importance of co-invention, the post-adoption invention of complementary business processes and adaptations aimed at making the adoption useful (Bresnahan and Greenstein, 1996). That is because IT often is affiliated with other infrastructure investments at the organizational level, which enables change, but it is not sufficient for ensuring that the change occurs. The latter depends on whether the employees of the adopting organization â in the case of hospitals, administrative staff, doctors, and nurses â find new uses and invents new services. The latter may depend on the conditions of the local labor market for skilled talent. It can also depend on supply conditions, particularly those practices which shape the ease with which lessons from experiments in one organization becomes widely known. For example, do suppliers or third-party consultants quickly transmit lessons across adopting organizations? In the absence of such practices, high payoff to early adoption may face considerable delays.
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Prior studies also place importance on the time it takes to implement change in large organizations. Interruptions to ongoing operations generate large opportunity costs in foregone services. Such factors may slow down implementation or the realization of returns, breaking the link between the order of initial adoption and the extent of the payoff. Indeed, the earliest adopters may face the largest transition costs because society has not yet gained economies of scale in learning. Therefore, prior studies stress that business process innovations usually do not yield immediate payoffs to adopting organizations (Bresnahan, Brynjolfsson, Hitt, 2002). The importance of complementary factors and co-invention leads to considerable variance in post-adoption utilization and further investment. Hence, the incentives around utilization and investment can change considerably over time due to changes in the supply of complements and differences in the restructuring of organizationâs hierarchy and operational practices. There is often little immediate payoff to adoption, and a strong potential for lagged payoff, if any arises at all.
Where would these factors be most visible? For the most recent vintages of information technology, evidence suggests considerable heterogeneity across US locations in the availability of complementary factors, such as skilled labor (Forman, Goldfarb, and Greenstein, 2005, 2012), third-party software support and service (Arora and Forman, 2007), and infrastructure (Greenstein and McDevitt, 2011). That induces a visible relationship between investment in health IT and geographic location. Large cities may have thicker labor markets for complementary services or for specialized skills. Thicker markets lower the (quality-adjusted) price of obtaining IT services such as contract programming and of hiring workers to perform development activities in-house. Such locations may also have greater availability of complementary information technology infrastructure, such as broadband services. Increases in each of these factors may decrease the costs of adopting complex technologies in some cities and not others, other things being equal. Overall, the presence of thicker labor markets for technical talent, greater input sharing of complex IT processes, and greater knowledge spillovers in cities should lower the costs of successful adoption of frontier technologies in big cities (Henderson 2003; Forman, Goldfarb, Greenstein 2008).
We also expect enterprises with existing IT facilities will be more likely to adopt frontier IT than establishments without an extensive operations. Having more resources elsewhere in the organization means that lower cost resources can be tapped, or loaned between projects of the same firm. Also, the internal firm resources that arise as a result of prior investments in other IT projects may lower adoption costs. Resources and other investments in the organization are already employed in some IT task, and the new technical opportunity leads them to be redeployed for use in advanced Internet applications. Programmers provide experience with IT projects. Prior IT projects may reduce development costs if programmers are able to transfer lessons learned from one project to another.5 Prior work on other IT projects may create learning economies and spillovers that decrease the costs of adapting general purpose IT to organizational needs, reducing the importance of external consultants and local spillovers. For example, Forman, Goldfarb, and Greenstein (2008) documented that, when IT labor forces are mobile, 5 For example, software developers may be able to share common tools for design, development, and testing (Banker and Slaughter, 1997), or they may be able to reuse code (Barnes and Bollinger, 1991). Software development may also have learning economies (Attewell, 1992) that through experience reduce the unit costs of new IT projects. Much prior research in the costs of innovative activity has also long presumed experience with prior related projects can lower the costs of innovation (Cohen and Levinthal, 1990).
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shared human capital at other establishments decreases the value of consultants and thicker labor markets in large cities. This should shape hospital investment due to use of statewide and federal funding mechanisms. Therefore, we expect less connection between local characteristics and the impact of advanced IT at hospitals with prior IT experience because such organizations would have more resources for overcoming the limitations of their locations.
We would add an additional observation, namely, we expect the prevalence of the various local factors that generate complementarities (IT-intensive industries, high population, etc.). This is due to the virtuous cycles â i.e., positive reinforcement â that develop between these factors. For example, a hospital in an urban location may be able to take advantage of a frontier component of EMR due to a thick labor market in a major city, tapping local technical expertise, while one in a suburban location may not have such options.
V. Data
We use a variety of data sources to examine the relationship between EMR adoption and costs. In particular, we match data on EMR adoption from a well-known private data source on health IT investments (HIMSS Analytics) with cost data from the Medicare Hospital Cost Report. We add to this data from the American Hospital Associationâs (AHA) Annual Survey of Hospitals. We obtain regional controls and information on local complementary factors from the decennial U.S. Census and from U.S. County Business Patterns data. We supplement the sources above with information on lagged hospital-level IT capabilities from another private source on IT investment, the Harte Hanks Computer Intelligence Database. Our data are organized as an unbalanced panel, with data available for every other year over 1996 â 2008. Table 2 provides descriptive statistics.6
EMR data. We obtain information about EMR adoption from the Healthcare Information and Management Systems Society (HIMSS) Analytics data base. The HIMMS Annual Study collects information systems data related to software and hardware inventory and relates the current status of EMR implementation of more than 5300 healthcare providers nationwide, including about 2000 hospitals. Organizations that seek access to HIMSS Analytics data must provide their information on software and hardware use. Because most organizations tend to participate for a long period of time, the HIMSS Analytics data closely approximates panel data and can be used for fixed effects regression.
HIMSS reports adoption of 99 different technologies in 18 categories. Examples include Emergency Department Information System in the Emergency Department; Financial Modeling for Financial Decision Support, and a Laboratory Information System for the Laboratory. Following most other studies, we restrict attention to six applications in the category Electronic Medical Records, which we listed above. These come closest to representing the kind of EMR applications that the RAND study and others believe will lead to dramatic cost savings and quality enhancements. We also examine two additional margins of adoption that aggregate these six technologies into broad categories of usage that we label basic and advanced EMR adoption. Applications within each of these categories will involve similar costs of adoption and require 6 The number of observations column in table 1 shows a key challenge within and across data sources: missing data. There is considerable variation across hospitals and years for each of the variables. We simply drop missing observations from our main specifications, but results are robust to alternatives.
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similar types of co-invention to be used successfully. The variable basic EMR is equal to one if the hospital adopts a clinical data repository (CDR), clinical decision support systems (CDS), or order entry/communication. The variable advanced health IT is equal to one when the hospital adopts either computerized practitioner order entry (CPOE) or physician documentation, applications which are more difficult to implement and, for example, are often included in the latter stages of EMR adoption in analyses of health IT adoption like the HIMSS Forecasting Model (HIMSS Analytics 2011).
Our estimation sample is based on the set of hospitals who replied to the HIMSS survey. Thus, we may exclude hospitals who systematically invest little in information systems and so have little incentive to reply to the HIMSS survey.
Table 2 shows that, by 2008, at least 70 percent of responding hospitals had adopted each of the basic EMR technologies and at least 20 percent had adopted the advanced technologies. The bottom of the table shows the changes from 1996 and documents sharp increases in adoption of all of these technologies over the sample period.
Cost Data. We collect data on hospital costs from the Medicare Cost Report. Our primary dependent variable is equal to total operating expenses at the hospital, but we also examine specific cost categories that may be particularly affected by EMR, including total diagnostic radiology costs and total nursing administration costs. The bottom of table 2 shows that, on average, costs rise considerably over the sample period but there is a great deal of variation across hospitals.
AHA Data. We obtain hospital characteristics from the American Hospital Association Annual Survey. The survey contains details about hospital ownership, service offerings, and financials. We match AHA, Cost Report, and HIMSS data using the hospital Medicare ID and retain only matching hospitals. Our final data set contains 4413 hospitals, 97 percent of which are observed in all eight years of the data. Missing data about specific technologies and about hospital characteristics mean that our regressions involve 2596 to 3515 hospitals observed an average of 4 to 5 periods.
We use information from the AHA data and the Medicare Cost Report to exclude several types of hospitals whose costs might be affected by unobservable and/or idiosyncratic factors unrelated to EMR adoption. In particular, we exclude federal hospitals, as well as hospitals that are not defined as short-term general medical and surgical hospitals. (The hospitals that we exclude are not usually considered to be âcommunityâ hospitals). Finally, we dropped a small number of hospitals that report very low total costs (less than $1000) over one or more years over our sample period. After dropping these, the minimum cost is $467,530, the average cost is $108 million and 99% of the data have costs above $2.6 million.
We use the AHA data to compute the following key predictors:
Hospital Size: We include total number of hospitals beds, number of admissions, number of inpatient days, number of full-time physicians and dentists, and the number of discharges (Medicare, Medicaid, and total).
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Hospital Organization: We include indicators of whether the hospital is an independent practice association hospital, or a management service organization hospital. We also include two indicators for whether the hospital is vertically integrated. The first measure is equal to one when the hospital is part of an open hospital physician organization, a closed hospital physician organization, an integrated salary model, or part of a group practice without walls. The second measure is identical but excludes the integrated salary model from the definition of vertical integration in order to allow separate influences on costs for these types of vertical integration.
Service Composition: Including number of births, total number of outpatient visits, and percent births.
Hospital ownership: Including indicators of for-profit ownership, non-secular nonprofit ownership, non-profit church ownership, an equity model hospital, or a foundation hospital.
Other characteristics: Including whether the hospital is a teaching hospital (defined as having a residency program or being a member of the council of teaching hospitals), and the number of physicians divided by the total number of hospital beds.
Census Data. We use U.S. Census data to identify location-level factors that might affect costs independent of information technology and to measure complementary factors that might facilitate process innovation. For controls, we obtain the following variables from the 2000 decennial U.S. Census and match on county: population, percent Hispanic and Black, percent age 65+ and percent age 25-64, percent university and high school education, median home value, and median household income.
To measure the availability of local complementary factors, we use two measures from the Census. First, we include a dummy for whether the hospital is located in an MSA. Urban locations will benefit from additional supply of complementary factors, including thicker labor markets, third party services firms, and better infrastructure.7 Urban location has been shown to be correlated with frontier IT adoption in a variety of settings (Forman, Goldfarb, and Greenstein 2005, 2008).
Our second measure of complementary factors is the percentage of local firms that are in IT-using industries. Like location in an MSA, we use this measure to capture geographic variance in the demand and supply for complementary factors that may affect the returns to EMR adoption. We measure the fraction of firms in IT-using and IT-producing industries in the county as of 1995 from the US Census County Business Patterns data. National aggregate data shows that such industries have unusually high returns from investment in IT in the 1990s. We define these industries using the classification reported in Jorgenson, Ho, and Stiroh (2005, p. 93).8 Table 2
7 Of course, urban location will also have stronger demand for the same factor, making identification of the relationship between IT investment, urban location, and hospital costs difficult. 8 These industries are Communications (SIC 48), Business Services (73), Wholesales Trade (50-51), Finance (60-62, 67), Printing and Publishing (27), Legal Services (81), Instruments and Miscellaneous Manufacturing (38-39), Insurance (63-64), Industrial Machinery and Computing Equipment (35), Gas Utilities (492, 496, and parts of 493), Professional and Social Services (832-839), Other Transportation Equipment (372-379), Other Electrical Machinery (36, ex. 366-267), Communications Equipment (SIC 366), and Electronic Components (367).
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shows that 43 percent of the hospitals in our data are in counties in the top quartile in IT intensity.
Additional IT Data. To obtain measures of historical hospital-level IT capabilities, we gather data from the Harte Hanks Market Intelligence Compute Intelligence Technology Database (hereafter CI database). The CI database contains establishment- and firm-level data on characteristics such as the number of employees, personal computers per employee, number of programmers, and the use of specific software applications. A number of researchers have used this data previously to study adoption of IT (e.g., Bresnahan and Greenstein 1996) and the productivity implications of IT investment (e.g., Bresnahan, Brynjolfsson, and Hitt 2002; Brynjolfsson and Hitt 2003; Bloom et al. 2009). Interview teams survey establishments throughout the calendar year; our main sample contains the most current information as of December 1998. As has been discussed elsewhere (e.g., Forman, Goldfarb, and Greenstein 2005), this data set represents among the best sources of information on the IT investments of private firms available.
We use the CI database to obtain measures of lagged IT capabilities of hospitals and measures of IT-intensive location. For capabilities of hospitals, we gather data on the number of computer programmers at the hospital and whether the hospital had at least one clinical application in 1996. We merge information from the CI database using hospital names. Unfortunately, because the CI database is itself a sample from a broader population of firms, there is a significant loss of data from merging these two data sources: the number of hospitals in our sample falls by more than half in the regressions that use the CI database directly to measure whether the hospital has programmers or clinical applications by 1996. 20 percent of the hospitals in this smaller sample had at least one programmer on staff in 1996 and 67 percent of these hospitals used at least one clinical software application.
For measures of IT-intensive location, we rely on calculations in Forman, Goldfarb, and Greenstein (2012) to identify the percentage of firms in a county that had adopted basic and advanced internet technology by 2000. These calculations use the most current information in the CI database as of December 2000. They represent the average level of internet adoption by firms in the county, weighted so that the Harte Hanks database represents the distribution of firms in the industry as identified in the economic census. Basic internet technology includes simple applications such as web browsing and email. Advanced internet technology is identified by e-commerce or e-business applications that involve frontier technologies and significant adaptation costs. We do not emphasize these measures of local IT-intensity, but use them to show that one particular definition of IT-intensive location does not drive our results.
V. Empirical strategy and results
We perform linear regression with hospital and year fixed effects on an unbalanced panel of hospitals observed on even years from 1996 to 2008. We proceed in four stages. First, we regress costs on different measures of EMR adoption and document that costs appear to rise on average after adoption. Second, we decompose the rise in costs by years since adoption and show that the rise is largest in the first year of adoption. Third, we examine different margins of complementarity: location and internal IT experience. Finally, we examine robustness, identification, and plausibility with a variety of further tests.
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Overall effects: We begin by examining the relationship between total administrative costs and EMR:
(1) Log(cit) =Xit+tZi+EMRit+t+i+it,
Here, t is a time dummy that captures average changes to costs over time; i is a hospital-specific fixed effect that gets differenced out in the estimation; and EMRit is a discrete variable for whether hospital i had adopted a particular EMR technology by time t.9 Thus identifies our main effect of interest. We have assumed that it is a normal i.i.d. variable and calculate heteroskedasticity-robust standard errors. We include two kinds of controls. First, Xit are controls for hospital characteristics that change over time such as inpatient days and outpatient visits (specified using a translog function), beds, type of hospital, etc. Second, Zi are controls for county-specific characteristics (such as population and income) that do not vary sufficiently over time for changes in their values to have much identifying power. However, the location-level characteristics do seem to have power to identify cost trends. Therefore, we interact these characteristics with the time trend to control for these trends. The full list of controls is provided in Table 2.
Table 3 shows the results of this regression. For columns 1 to 7, the dependent variable is total administrative costs, as defined in the AHA data. For column 8 and 9, administrative costs are divided by the number of admittances to the hospital. Columns 1 to 3 use the specific EMR technologies that together we label âbasic EMRâ; columns 4 and 8 use the aggregated basic EMR measure (which whether the hospital has adopted any of the three technologies); column 5 and 6 use the EMR technologies that make up âadvanced EMRâ; columns 7 and 9 use the aggregated advanced EMR measure.
The results suggest that, on average, EMR does not reduce costs. Instead, in most specifications, EMR is associated with a positive and significant increase in costs of about one to three percent. The increase appears to be slightly higher for advanced EMR than for basic EMR.
Effects by time since adoption: As discussed above, a rich literature on IT productivity has documented that IT adoption affects productivity with a lag. Table 4 examines the extent to which the increase in costs is driven by initial adoption costs such as coinvention and learning new processes. Specifically, Table 4 splits the EMR variable into four pieces, based on time since adoption:
(2) Log(cit) =Xit+tZi+EMRit+EMRit-2 +EMRit-4 +EMRit-6 +t+i+it,
Because the data is biannual, the lags are t-2, t-4, and t-6. We therefore identify separate coefficients for the first period observed after adoption and for subsequent periods. These coefficients should be interpreted relative to the period before adoption.
9 As in Athey and Stern (2002), Hubbard (2003), and Forman, Goldfarb, and Greenstein (2012) we treat the diffusion of a new technology as an exogenous factor that leads to a change in economic outcomes, and then examine the plausibility of the exogeneity assumption.
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In all cases, costs are significantly higher immediately after adoption, ranging from 1.1 percent higher for CDR to 3.1 percent higher for physician documentation and advanced EMR. After the first period, costs gradually return to the pre-adoption levels. Generally, the costs return to the pre-adoption levels faster for the basic than for the advanced technologies. In the case of CDR, costs appear to be lower four years after adoption. This is consistent with the literature on IT as a process innovation: initial adoption costs are high because of disruptions to established processes, over time these disruptions diminish, and more complicated technologies take more time to be effectively implemented.
Table 5 sets up the specifications used in the remainder of the paper. Specifically, it focuses on the aggregate measure of basic and advanced EMR and it combines EMRit-2, EMRit-4, and EMRit-6 into one variable âadopt at least 2 years earlierâ. As expected, the results are similar to Table 4.
Effects by location: The literature on IT as a process innovation has emphasized that efficient use of IT based on the availability of complementary factors such as skilled labor, third-party software support and service, and infrastructure. To explore this hypothesis, we interact EMR adoption measures of the IT intensity of a location:
(3) Log(cit)=Xit+tZi+EMRit+EMRit-2+ +IT_INTENSEiEMRit +IT_INTENSEiEMRit-2+ +t+i+it,
where EMRit-2+ is a dummy variable for whether the hospital adopted EMR at least two years earlier and IT_INTENSEi is a measure of whether the location is IT-intensive. Specifically, Table 6 examines four distinct measures of IT-intensity: a dummy variable for whether the hospital is in a county is in the top quartile in terms of IT-using and IT-producing industry, a dummy variable for whether the hospital is in an MSA (found in prior work to be a rough but effective measure of local skills), the percentage of all firms in the county that had adopted basic IT by 2000, and the percentage of all firms in the county that had adopted advanced IT by 2000. Because the measures in columns 5 to 8 are continuous and the measures in the other column are discrete, a direct comparison of coefficient values is not appropriate. As a point of reference, the standard deviation for percentage that adopt basic IT is 0.3 and the standard deviation for advanced IT is 0.1.
The first two rows show that costs generally increase in non-IT intensive counties, even for basic EMR and after two years. This increase is much lower in IT-intensive counties. In the first period after adoption, columns 1 and 9 show no significant rise in costs for basic EMR (using a Wald test of the sum of the coefficients in rows 1 and 3) and column 3 has marginal significance (p=0.09). For columns 5 and 7, significant disappears between one and two standard deviations. The differences increase after the initial adoption period: the sum of coefficients on basic EMR become significantly negative in IT-intensive locations.
Interestingly, for advanced EMR, we see much less difference in the first period after adoption. Columns 1, 4, and 10 show no initial difference in costs between IT-intensive and other locations. Thus, the initial costs of adoption of advanced EMR, even when complementary inputs are available. However, two years after adoption, the impact of advanced EMR on costs is effectively zero in IT-intensive locations while it remains quite high in other locations.
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Effects by hospital IT experience: Internal expertise can also mitigate the costs of adoption of a new process innovation. Table 7 examines the interaction in the following format:
(4) Log(cit)=Xit+tZi+EMRit+EMRit-2+ +HIT_EXPERIENCEiEMRit +HIT_EXPERIENCEiEMRit-2+ +t+i+it,
We use two measures of hospital IT experience: whether the hospital employed any programmers at the beginning of the sample (columns 1, 2, 5, and 6) and whether the hospital used at least one clinical software application at the beginning of our sample. Our results suggest a striking contrast to the effects of local expertise. Internal expertise appears to have little impact on the relationship between basic EMR and costs. Instead, it appears to have an substantial impact on reducing the cost increases from advanced EMR, particularly in the first period after adoption. Internal expertise therefore seems particularly important for the most advanced applications that might involve a great deal of coinvention to be successfully employed.
Table 8 columns 1 and 2 show the results including the local IT intensity and hospital IT experience results in the same regression. Generally, the results of the separate analyses hold. For basic EMR adopters, (i) operating costs fall for hospitals in locations with high IT intensity, (ii) operating costs rise for other hospitals, and (iii) hospital IT experience has no significant relationship with costs. For advanced EMR adopters, (iv) operating costs do not rise significantly for hospitals in locations with high IT intensity, (v) operating costs rise for other hospitals, and (vi) the initial increase in operating costs after adoption is muted for hospitals with IT experience. The only qualitative change is that, for advanced EMR, the difference between IT intensive locations and other locations is not significant (though the magnitude of the coefficient is similar).
Columns 3 and 4 of Table 8 show the three-way interaction between adoption, local IT intensity, and hospital IT experience. The many interactions introduce a great deal of noise to the estimation and little of significance appears. Mainly, we include these columns for completeness. Still, the signs in both columns, and the marginal significance in column 3 are somewhat suggestive that having IT experience is most effective in IT intensive locations (i.e. that they are complements). This contrasts with our prior work (Forman, Goldfarb, and Greenstein 2008) on the incentives to adopt frontier IT by businesses that suggests that urban locations can substitute for internal IT experience.
Robustness, identification, and plausibility: Next, we explore the degree to which we can claim our main results are causal and general. There are three potential types of concerns. First, there might be an omitted variable correlated with EMR adoption and with costs. Second, it is possible that anticipated changes in costs drive EMR adoption (rather than EMR adoption driving changes in costs). Third, the large amount of missing data may mean that our sample is not representative.
So far, to address these concerns, we have included hospital and time fixed effects as well as a very large set of covariates as controls. In order to address additional concerns we conduct three types of analyses, examining (i) the timing of the relationship between EMR adoption and cost changes, (ii) different types of costs, and (iii) robustness to alternative treatments of the missing variables.
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In figure 2, we examine the timing of the relationship between EMR adoption and changes in costs. Specifically, we run the equation (2) above, but add variables for 2 years before adoption, 4 years before adoption, and 8 years before adoption. Figure 2a shows that costs seem to peak in the adoption period, and then subsequently fall. More interestingly, figures 2b and 2c demonstrate distinct effects for IT intensive and non IT intensive locations, defined by the top quartile of counties in terms of IT-intensive industry. Figure 2b examines basic EMR adoption and figure 2c examines advanced EMR adoption. Prior to adoption, the costs follow similar patterns. During and after the initial adoption, however, the costs in non-IT intensive locations rise while the costs in IT-intensive locations fall substantially. The full results for these regressions are shown in appendix table 1. Figure 2 therefore suggests that the timing of the impact of EMR is appropriate: there does not appear to be a noticeable omitted variable driving
In table 9, we compare diagnostic radiology costs and nursing costs. Many observers have argued the CDR, CDS, and CPOE should be particularly effective at reducing diagnostic radiology costs (e.g. Wager, Wickham Lee, and Glaser 2009) The first five columns document that, even without the interactions with local or internal expertise, such costs experience no initial increase and then appear to fall.
In contrast, some costs might be expected to rise as a consequence of particular EMR technologies, most notably nursing costs through increase in administration and clerical work (Sidorov 2006). Columns 6 through 10 of Table 9 confirm this hypothesis: nursing costs generally rise, particularly a few years after adoption when the EMRs generally reduce other costs. Broadly, we view these results as consistent with the impact of EMR being driven by the places where we would expect to see an impact, rather than a spurious relationship to all costs.
Third, address the concern that several of our key covariates and controls are missing in some years for some hospitals. In particular, we lack data on EMR adoption for 14 to 35 percent of the sample, depending on the specification. In addition, we lack data on discharges and hospital types for a similar fraction of the data. In the main specification, we drop observations that are missing, implicitly assuming that they are missing-at-random. We conduct two types of analysis in order to determine whether the missing data generates a bias. First, for the missing controls (on discharges and hospital types), we create a new dummy variable for when these observations are missing. While Allison (2002) points out that this method can lead to biased coefficients if the missing data covariate does not represent a real characteristic of the hospitals, even then the signs do not change in linear models. Second, to address the concerns on the missing EMR data, we show that the results hold with two opposite assumptions: if we set all missing values to zero or if we set all missing values to one. This is suggestive that the missing data does not create a systematic bias. Results are reported in the appendix.
VI. Conclusion
Drawing on a variety of data sources on IT, EMR, local demographics, and hospital characteristics, we have shown evidence of complementarities between EMR adoption and other inputs. Specifically, while on average, EMR adoption seems be to associated with an increase in costs, this hides important heterogeneity over time, across technologies, across locations, and across hospitals. We find that both basic and especially advanced EMR adoption are initially
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associated with a rise in costs but this initial increase in costs is mitigated in hospitals with some internal information technology expertise. After two years, hospitals in locations that have local information technology expertise experience a decrease in costs after adopting basic EMR, and no increase or decrease in costs after adopting advanced EMR. In contrast, hospitals in other locations experience a sharp increase in costs, even after several years.
As with any empirical work, our analysis has a number of limitations. First, we observe only a subset of the medical practices in the United States. Smaller doctorsâ offices, outpatient clinics, nursing homes, and other medical practices may have had a different experience. While we believe it is likely that the general principles of business process innovation would apply broadly, our evidence is specific to hospitals. Second, we do not directly address the question of why hospitals adopt if their costs do not fall. It might be due to misconceptions, expected benefits that we do not measure, or something else. We have tried to address the endogeneity of this adoption through various techniques, but we cannot completely rule out the possibility that adopting hospitals in IT-intensive locations adopt because they expect their costs to fall and therefore they have resources. Third, and relatedly, it is possible that hospitals outside IT-intensive locations experience a sharp increase in benefits such as clinical outcomes and reduced errors (though the evidence in the literature is mixed on whether such benefits accrue). Thus our findings on reduced costs only tell part of the story.
Despite these limitations, we believe our results linking the mixed evidence on the success of EMR in reducing costs with the literature on IT as a business process innovation help inform the discussion on the âtrillion dollar conundrumâ: the (perhaps missing) link between healthcare IT and healthcare costs.
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Table 1: Types of EMR and Hospital Adoption Rates
EMR Description % of Hospitals
Adopting
1996 2008
Clinical Data Repository Real time database that consolidates clinical data to create a
unified patient medical record
.134 .775
Clinical Decision Support Uses patient data to generate diagnostic and/or treatment
advice
.109 .716
Order Entry Provides electronic forms to streamline hospital operations
(replacing faxes and paper forms
.166 .823
Computerized Physician
Order Entry
Electronic entry of physician treatment orders that can be
communicated to the pharmacy, lab, and other departments
.009 .222
Physician Documentation Allows physicians to transition from written to electronic
notes
.026 .211
Table 2a: Summary statistics for 2008
Variable Mean Std. Dev. Min Max # obs.
EMR MEASURES CDR 0.775 0.418 0 1 2982CDSS 0.716 0.451 0 1 2704Order entry 0.823 0.382 0 1 3163Basic EMR adoption (CDR, CDSS, or order entry) 0.835 0.372 0 1 2255CPOE 0.222 0.415 0 1 3648Physician documentation 0.211 0.408 0 1 3593Advanced EMR adoption (CPOE or Physician docân) 0.283 0.451 0 1 3305
COST MEASURES Log total costs 17.765 1.347 14.366 21.873 3510Log total costs per admit 9.372 0.372 7.008 13.812 2295Log lab diagnostic costs 15.203 1.158 9.821 18.650 2938Log nursing costs 13.225 1.362 4.820 17.243 3438
HOSPITALâLEVEL CONTROLS Log inpatient days 9.834 1.424 2.398 13.434 4247Log outpatient visits 11.118 1.265 0.693 15.022 4232Log total hospital beds 4.598 1.090 0.693 7.698 4247Independent practice association hospital 0.111 0.314 0 1 3466Management service organization hospital 0.062 0.241 0 1 3469Equity model hospital 0.014 0.119 0 1 3467Foundation hospital 0.031 0.173 0 1 3467Log admissions 8.110 1.463 1.099 11.619 4247Births (000s) 0.893 1.371 0 17.203 4247Full time physicians and dentists (000s) 0.019 0.088 0 2.170 4247Percent births 0.101 0.104 0 2.241 4247Forâprofit ownership 0.142 0.349 0 1 4323Nonâsecular nonprofit ownership 0.489 0.500 0 1 4323Nonâprofit church ownership 0.109 0.312 0 1 4323Number of discharges Medicare (000s) 2.324 2.825 1 22.063 2329Number of discharges Medicaid (000s) 0.925 1.646 1 29.479 2329Number of discharges total (000s) 6.608 8.773 1 70.133 2329Residency or Member of Council Teaching Hospitals 0.171 0.377 0 1 4323Vertically integrated with doctors 0.602 0.490 0 1 4323Vert. integ. with doctors (excl. integrated salary model) 0.357 0.479 0 1 4323FT physicians / total hospital beds 0.094 0.174 0 2.531 4247
LOCATIONâLEVEL CONTROLS MSA dummy 0.527 0.499 0 1 4283Log population in 2000 census 11.566 1.848 7.336 16.069 4283% Hispanic in 2000 census 0.091 0.136 0 0.981 4283% Black in 2000 census 0.101 0.138 0 0.861 4283% age 65+ in 2000 census 0.139 0.040 0.028 0.347 4283% age 25â64 in 2000 census 0.851 0.047 0.455 1.051 4283% university education in 2000 census 0.134 0.059 0.037 0.402 4283Log median home value in 2000 census 148.431 6.137 131.170 170.513 4283Log median household income in 2000 census 136.982 3.146 126.063 147.235 4283
OTHER VARIABLES USED Top quartile county IT intensive industry 0.428 0.495 0 1 4107% local businesses adopted basic internet by 2000 0.804 0.315 0 1 4107% local businesses adopted advanced internet by 2000 0.104 0.101 0 1 4107Had at least one programmer in 1996 0.195 0.396 0 1 1563Had at least one clinical software application in 1996 0.670 0.470 0 1 1556
Table 2b: Summary statistics on changes from 1996 to 2008
Variable Mean Std. Dev. Min Max # obs.
Log total costs 0.791 0.386 â1.316 3.016 2218CDR 0.661 0.474 0 1 2976CDSS 0.607 0.488 0 1 2700Order entry 0.657 0.475 0 1 3156Basic EMR adoption (CDR, CDSS, or order entry) 0.680 0.467 0 1 2251CPOE 0.213 0.410 0 1 3643Physician documentation 0.185 0.388 0 1 3587Advanced EMR adoption (CPOE or Physician docân) 0.255 0.436 0 1 3300
Table 3: Main effects by technology
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Log total costs
Log total costs
Log total costs
Log total costs
Log total costs
Log total costs Log total costs
Log total costs per admit
Log total costs per admit
Technology CDR CDSS Order entry Basic EMRadoption
CPOE Physician documentation
Advanced EMR adoption
Basic EMRadoption
Advanced EMR adoption
Adopted EMR 0.0080 0.0181 0.0095 0.0127 0.0126 0.0295 0.0230 0.0115 0.0258(0.0057) (0.0061)*** (0.0055)* (0.0066)* (0.0076)* (0.0083)*** (0.0076)*** (0.0068)* (0.0078)***
Observations 12070 10828 12877 9122 14670 14446 13193 7687 11082# of hospitals 2885 2596 3057 2178 3515 3462 3175 1564 2268Râsquared 0.80 0.80 0.80 0.80 0.79 0.79 0.79 0.78 0.77
CONTROLS Log inpatient days â0.3178 â0.2661 â0.3499 â0.2939 â0.3807 â0.3851 â0.3953 â0.4715 â0.4493
(0.0727)*** (0.0880)*** (0.1032)*** (0.0895)*** (0.0779)*** (0.0782)*** (0.0794)*** (0.1337)*** (0.0921)***Log outpatient visits â0.0745 â0.0857 â0.1654 â0.1389 â0.1177 â0.1577 â0.1599 â0.0814 â0.0820
(0.0519) (0.0599) (0.0738)** (0.0629)** (0.0622)* (0.0609)*** (0.0621)** (0.0894) (0.0898)Log inpatient days x Log inpatient days
0.0188 0.0156 0.0187 0.0197 0.0198 0.0187 0.0193 0.0293 0.0239(0.0045)*** (0.0047)*** (0.0051)*** (0.0055)*** (0.0039)*** (0.0040)*** (0.0041)*** (0.0081)*** (0.0047)***
Log outpatient visits x Log outpatient visits
0.0061 0.0060 0.0090 0.0104 0.0059 0.0072 0.0071 0.0099 0.0065(0.0015)*** (0.0014)*** (0.0036)** (0.0040)*** (0.0015)*** (0.0016)*** (0.0015)*** (0.0049)** (0.0015)***
Log inpatient days x Log outpatient visits
â0.0006 0.0009 0.0016 â0.0036 0.0037 0.0055 0.0060 â0.0079 â0.0001(0.0044) (0.0049) (0.0066) (0.0067) (0.0050) (0.0050) (0.0051) (0.0090) (0.0072)
Log total hospital beds 0.0868 0.0918 0.0908 0.0851 0.0901 0.0879 0.0903 0.0978 0.1046 (0.0158)*** (0.0163)*** (0.0176)*** (0.0179)*** (0.0159)*** (0.0159)*** (0.0166)*** (0.0192)*** (0.0171)***Independent practice association hospital
0.0090 0.0023 0.0024 0.0016 0.0031 0.0065 0.0047 0.0037 0.0093(0.0073) (0.0071) (0.0072) (0.0078) (0.0067) (0.0067) (0.0071) (0.0080) (0.0072)
Management service organization hospital
0.0161 0.0176 0.0086 0.0187 0.0098 0.0088 0.0091 0.0166 0.0082(0.0076)** (0.0073)** (0.0075) (0.0080)** (0.0071) (0.0072) (0.0076) (0.0079)** (0.0076)
Equity model hospital
â0.0046 â0.0178 â0.0018 â0.0046 â0.0132 â0.0047 â0.0107 â0.0083 â0.0148(0.0179) (0.0198) (0.0177) (0.0182) (0.0176) (0.0169) (0.0194) (0.0180) (0.0192)
Foundation hospital â0.0449 â0.0370 â0.0328 â0.0333 â0.0391 â0.0354 â0.0316 â0.0366 â0.0336(0.0145)*** (0.0135)*** (0.0143)** (0.0151)** (0.0143)*** (0.0122)*** (0.0140)** (0.0160)** (0.0150)**
Log admissions 0.0099 0.0078 0.0201 0.0124 â0.0002 â0.0052 â0.0006 â0.9432 â0.9642(0.0254) (0.0264) (0.0259) (0.0263) (0.0236) (0.0236) (0.0244) (0.0374)*** (0.0327)***
Births (000s) 0.0050 0.0028 0.0049 0.0033 0.0065 0.0064 0.0061 â0.0032 0.0012(0.0074) (0.0086) (0.0078) (0.0084) (0.0071) (0.0072) (0.0074) (0.0093) (0.0080)
Full time physicians and dentists (000s)
â0.2123 â0.3271 â0.2402 â0.3757 â0.3492 â0.2632 â0.3627 â0.3180 â0.3567(0.1214)* (0.1401)** (0.1171)** (0.1860)** (0.1134)*** (0.1121)** (0.1440)** (0.2232) (0.1647)**
Percent births 0.1413 0.2361 0.1752 0.1159 0.1549 0.1762 0.1752 0.1793 0.1954(0.1092) (0.1308)* (0.1183) (0.1155) (0.0950) (0.0926)* (0.0968)* (0.1327) (0.1118)*
Forâprofit ownership â0.0282 â0.0367 â0.0136 â0.0216 0.0026 â0.0071 â0.0046 â0.0065 â0.0080(0.0316) (0.0363) (0.0284) (0.0371) (0.0268) (0.0277) (0.0287) (0.0366) (0.0299)
Nonâsecular nonprofit ownership
0.0314 0.0172 0.0347 0.0177 0.0392 0.0325 0.0345 0.0322 0.0219(0.0229) (0.0250) (0.0213) (0.0276) (0.0206)* (0.0207) (0.0218) (0.0281) (0.0224)
Nonâprofit church ownership
0.0730 0.0586 0.0909 0.0701 0.0825 0.0793 0.0829 0.0834 0.0750(0.0321)** (0.0345)* (0.0293)*** (0.0372)* (0.0293)*** (0.0290)*** (0.0301)*** (0.0380)** (0.0310)**
Log number of discharges Medicare
0.1511 0.1506 0.1429 0.1374 0.1269 0.1371 0.1333 0.1356 0.1299(0.0413)*** (0.0445)*** (0.0350)*** (0.0444)*** (0.0296)*** (0.0332)*** (0.0338)*** (0.0468)*** (0.0350)***
Log number of discharges Medicaid
0.0050 0.0077 0.0057 0.0076 0.0054 0.0040 0.0044 0.0071 0.0037(0.0023)** (0.0026)*** (0.0023)** (0.0025)*** (0.0022)** (0.0022)* (0.0022)** (0.0025)*** (0.0023)
Log number of discharges total
0.2141 0.2421 0.2133 0.2331 0.2235 0.2243 0.2196 0.2384 0.2437(0.0445)*** (0.0498)*** (0.0396)*** (0.0519)*** (0.0341)*** (0.0367)*** (0.0376)*** (0.0581)*** (0.0428)***
Residency or Member of Council Teaching Hosps
â0.0023 â0.0007 â0.0033 0.0072 0.0015 0.0030 0.0067 0.0076 0.0052(0.0118) (0.0111) (0.0102) (0.0119) (0.0105) (0.0100) (0.0104) (0.0120) (0.0102)
Vertically integrated with doctors
0.0038 0.0110 0.0057 0.0111 0.0056 0.0045 0.0053 0.0065 0.0008(0.0073) (0.0074) (0.0072) (0.0082) (0.0066) (0.0066) (0.0069) (0.0086) (0.0074)
Vert. integ. w drs (excl. integrated salary mdl)
0.0063 0.0023 0.0034 â0.0018 0.0058 0.0108 0.0102 0.0017 0.0122(0.0092) (0.0094) (0.0090) (0.0105) (0.0082) (0.0082) (0.0086) (0.0110) (0.0090)
FT physicians / total hospital beds
0.1915 0.2458 0.2018 0.2470 0.2614 0.2188 0.2595 0.2177 0.2542(0.0595)*** (0.0700)*** (0.0591)*** (0.0817)*** (0.0650)*** (0.0568)*** (0.0713)*** (0.1015)** (0.0832)***
Year 1998 â0.0488 â0.0540 â0.0625 â0.0288 â0.0770 â0.0886 â0.1035 â0.0146 â0.0837(0.0740) (0.0747) (0.0703) (0.0803) (0.0668) (0.0677) (0.0712) (0.0804) (0.0715)
Year 2000 â0.1071 â0.1150 â0.1333 â0.0630 â0.1590 â0.1833 â0.2116 â0.0357 â0.1736(0.1478) (0.1489) (0.1404) (0.1598) (0.1335) (0.1355) (0.1424) (0.1599) (0.1430)
Year 2002 â0.1298 â0.1417 â0.1691 â0.0650 â0.2071 â0.2425 â0.2853 â0.0241 â0.2281(0.2211) (0.2229) (0.2101) (0.2393) (0.1999) (0.2028) (0.2132) (0.2394) (0.2141)
Year 2004 â0.1791 â0.1917 â0.2329 â0.0926 â0.2798 â0.3308 â0.3848 â0.0280 â0.2987(0.2949) (0.2974) (0.2803) (0.3193) (0.2666) (0.2705) (0.2844) (0.3194) (0.2855)
Year 2006 â0.1967 â0.2151 â0.2664 â0.0924 â0.3267 â0.3875 â0.4568 â0.0243 â0.3618(0.3689) (0.3720) (0.3506) (0.3994) (0.3334) (0.3382) (0.3556) (0.3996) (0.3571)
Year 2008 â0.2267 â0.2497 â0.3104 â0.1010 â0.3853 â0.4566 â0.5419 â0.0365 â0.4452
(0.4436) (0.4475) (0.4213) (0.4804) (0.4006) (0.4063) (0.4272) (0.4810) (0.4290)MSA dummy x year â0.0063 â0.0049 â0.0056 â0.0051 â0.0051 â0.0057 â0.0059 â0.0046 â0.0057
(0.0018)*** (0.0018)*** (0.0017)*** (0.0019)*** (0.0017)*** (0.0017)*** (0.0018)*** (0.0019)** (0.0018)***Log population in 2000 census x year
â0.0008 â0.0020 â0.0011 â0.0017 â0.0015 â0.0017 â0.0018 â0.0013 â0.0014(0.0007) (0.0007)*** (0.0007)* (0.0007)** (0.0006)** (0.0006)*** (0.0007)*** (0.0007)* (0.0007)**
% Hispanic in 2000 census x year
â0.0095 â0.0086 â0.0091 â0.0091 â0.0061 â0.0064 â0.0058 â0.0093 â0.0066(0.0054)* (0.0054) (0.0052)* (0.0057) (0.0050) (0.0050) (0.0053) (0.0058) (0.0053)
% Black in 2000 census x year
â0.0149 â0.0143 â0.0155 â0.0149 â0.0126 â0.0122 â0.0101 â0.0154 â0.0109(0.0046)*** (0.0045)*** (0.0045)*** (0.0049)*** (0.0043)*** (0.0044)*** (0.0045)** (0.0049)*** (0.0045)**
% age 65+ in 2000 census x year
â0.0470 â0.0310 â0.0400 â0.0388 â0.0382 â0.0407 â0.0373 â0.0462 â0.0448(0.0200)** (0.0207) (0.0194)** (0.0221)* (0.0187)** (0.0191)** (0.0198)* (0.0229)** (0.0203)**
% age 25â64 in 2000 census x year
â0.0294 â0.0359 â0.0280 â0.0383 â0.0316 â0.0151 â0.0157 â0.0361 â0.0135(0.0157)* (0.0154)** (0.0154)* (0.0158)** (0.0140)** (0.0145) (0.0150) (0.0161)** (0.0152)
% university education in 2000 census x year
â0.0088 â0.0107 â0.0116 â0.0066 â0.0088 â0.0063 â0.0045 â0.0067 â0.0051(0.0150) (0.0154) (0.0139) (0.0166) (0.0133) (0.0137) (0.0144) (0.0168) (0.0146)
Log median home value in 2000 census x year
0.0098 0.0121 0.0108 0.0102 0.0123 0.0124 0.0130 0.0093 0.0124(0.0028)*** (0.0027)*** (0.0026)*** (0.0030)*** (0.0025)*** (0.0025)*** (0.0026)*** (0.0030)*** (0.0026)***
Log median hh income in 2000 census x year
0.0017 0.0009 0.0014 0.0017 0.0009 0.0003 0.0004 0.0014 â0.0006(0.0048) (0.0046) (0.0045) (0.0050) (0.0044) (0.0043) (0.0046) (0.0051) (0.0047)
Constant 15.2452 14.7712 15.9365 15.3679 15.9130 16.1138 16.1379 15.4462 15.4295 (0.4685)*** (0.6793)*** (0.8237)*** (0.5676)*** (0.6475)*** (0.6382)*** (0.6438)*** (0.7754)*** (0.8342)***
Unit of observation is a hospitalâyear. Data biannually from 1996 to 2008. Regressions include hospitalâspecific fixed effects, differenced out at means.
Table 4: Main effects by technology, by years since adoption
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Log total costs
Log total costs
Log total costs
Log total costs
Log total costs
Log total costs Log total costs Log total costs per admit
Log total costs per admit
Technology CDR CDSS Order entry Basic EMRadoption
CPOE Physiciandocumentation
Advanced EMRadoption
Basic EMRadoption
Advanced EMR adoption
Adopt in previous 2 year period
0.0108 0.0169 0.0101 0.0207 0.0154 0.0313 0.0312 0.0194 0.0329(0.0059)* (0.0063)*** (0.0059)* (0.0056)*** (0.0077)** (0.0082)*** (0.0073)*** (0.0058)*** (0.0075)***
Adopt 2 to 4 years earlier â0.0078 0.0101 â0.0021 0.0065 0.0073 0.0325 0.0196 0.0030 0.0201(0.0073) (0.0077) (0.0073) (0.0060) (0.0097) (0.0103)*** (0.0093)** (0.0061) (0.0094)**
Adopt 4 to 6 years earlier â0.0168 0.0049 â0.0123 0.0012 0.0112 0.0177 0.0180 0.0003 0.0213(0.0091)* (0.0098) (0.0091) (0.0059) (0.0123) (0.0128) (0.0109)* (0.0059) (0.0110)*
Adopt at least 6 years earlier
â0.0218 â0.0052 â0.0135 0.0040 â0.0139 â0.0105 â0.0112 0.0022 â0.0056 (0.0123)* (0.0124) (0.0120) (0.0068) (0.0189) (0.0179) (0.0143) (0.0069) (0.0141)
Observations 12070 10828 12877 9122 14670 14446 13193 7687 11082# of hospitals 2885 2596 3057 2178 3515 3462 3175 1564 2268Râsquared 0.80 0.80 0.80 0.80 0.79 0.79 0.79 0.78 0.77
CONTROLS Log inpatient days â0.3160 â0.2667 â0.3505 â0.2959 â0.3807 â0.3851 â0.3959 â0.4765 â0.4502
(0.0722)*** (0.0880)*** (0.1031)*** (0.0897)*** (0.0779)*** (0.0782)*** (0.0793)*** (0.1342)*** (0.0920)*** Log outpatient visits â0.0745 â0.0846 â0.1674 â0.1380 â0.1190 â0.1579 â0.1607 â0.0803 â0.0834
(0.0516) (0.0597) (0.0734)** (0.0623)** (0.0622)* (0.0605)*** (0.0618)*** (0.0888) (0.0894) Log inpatient days x Log inpatient days
0.0187 0.0156 0.0187 0.0198 0.0198 0.0186 0.0192 0.0297 0.0238(0.0045)*** (0.0047)*** (0.0051)*** (0.0055)*** (0.0039)*** (0.0040)*** (0.0041)*** (0.0081)*** (0.0047)***
Log outpatient visits x Log outpatient visits
0.0062 0.0060 0.0091 0.0104 0.0059 0.0072 0.0071 0.0099 0.0065(0.0015)*** (0.0014)*** (0.0036)** (0.0040)*** (0.0015)*** (0.0015)*** (0.0015)*** (0.0048)** (0.0015)***
Log inpatient days x Log outpatient visits
â0.0006 0.0009 0.0016 â0.0037 0.0038 0.0057 0.0062 â0.0081 0.0002(0.0044) (0.0049) (0.0065) (0.0067) (0.0050) (0.0050) (0.0051) (0.0090) (0.0072)
Log total hospital beds 0.0876 0.0923 0.0906 0.0854 0.0901 0.0887 0.0908 0.0978 0.1049 (0.0158)*** (0.0163)*** (0.0176)*** (0.0179)*** (0.0159)*** (0.0159)*** (0.0166)*** (0.0193)*** (0.0171)*** Independent practice association hospital
0.0085 0.0020 0.0016 0.0012 0.0030 0.0063 0.0045 0.0032 0.0091(0.0072) (0.0071) (0.0072) (0.0078) (0.0067) (0.0067) (0.0071) (0.0079) (0.0072)
Management service organization hospital
0.0154 0.0171 0.0080 0.0182 0.0100 0.0087 0.0090 0.0162 0.0081(0.0076)** (0.0073)** (0.0075) (0.0080)** (0.0071) (0.0072) (0.0076) (0.0079)** (0.0076)
Equity model hospital
â0.0046 â0.0177 â0.0019 â0.0046 â0.0131 â0.0041 â0.0110 â0.0084 â0.0152 (0.0180) (0.0199) (0.0178) (0.0184) (0.0176) (0.0169) (0.0195) (0.0181) (0.0193)
Foundation hospital â0.0465 â0.0372 â0.0335 â0.0337 â0.0394 â0.0361 â0.0325 â0.0369 â0.0346 (0.0145)*** (0.0135)*** (0.0143)** (0.0151)** (0.0143)*** (0.0123)*** (0.0140)** (0.0160)** (0.0150)**
Log admissions 0.0107 0.0082 0.0205 0.0129 â0.0001 â0.0046 â0.0003 â0.9425 â0.9639 (0.0254) (0.0265) (0.0259) (0.0264) (0.0236) (0.0236) (0.0244) (0.0375)*** (0.0327)***
Births (000s) 0.0050 0.0031 0.0048 0.0033 0.0067 0.0062 0.0060 â0.0033 0.0012(0.0074) (0.0086) (0.0078) (0.0084) (0.0071) (0.0072) (0.0074) (0.0093) (0.0080)
Full time physicians and dentists (000s)
â0.2093 â0.3238 â0.2419 â0.3792 â0.3481 â0.2629 â0.3643 â0.3213 â0.3571 (0.1210)* (0.1399)** (0.1170)** (0.1856)** (0.1133)*** (0.1119)** (0.1438)** (0.2226) (0.1644)**
Percent births 0.1412 0.2371 0.1789 0.1177 0.1545 0.1781 0.1762 0.1794 0.1955(0.1095) (0.1312)* (0.1183) (0.1157) (0.0949) (0.0926)* (0.0968)* (0.1330) (0.1118)*
Forâprofit ownership â0.0306 â0.0365 â0.0146 â0.0215 0.0022 â0.0054 â0.0036 â0.0069 â0.0068 (0.0315) (0.0362) (0.0284) (0.0371) (0.0268) (0.0276) (0.0287) (0.0365) (0.0298)
Nonâsecular nonprofit ownership
0.0306 0.0179 0.0347 0.0185 0.0392 0.0330 0.0348 0.0326 0.0223(0.0227) (0.0249) (0.0212) (0.0275) (0.0206)* (0.0206) (0.0217) (0.0280) (0.0224)
Nonâprofit church ownership
0.0718 0.0582 0.0905 0.0701 0.0824 0.0792 0.0827 0.0829 0.0751(0.0321)** (0.0344)* (0.0294)*** (0.0371)* (0.0293)*** (0.0290)*** (0.0300)*** (0.0379)** (0.0309)**
Log number of discharges Medicare
0.1507 0.1503 0.1429 0.1373 0.1268 0.1375 0.1333 0.1354 0.1298(0.0413)*** (0.0444)*** (0.0351)*** (0.0444)*** (0.0295)*** (0.0330)*** (0.0337)*** (0.0469)*** (0.0350)***
Log number of discharges Medicaid
0.0050 0.0077 0.0056 0.0075 0.0054 0.0038 0.0043 0.0071 0.0036(0.0023)** (0.0026)*** (0.0023)** (0.0025)*** (0.0022)** (0.0022)* (0.0022)* (0.0025)*** (0.0023)
Log number of discharges total
0.2155 0.2427 0.2135 0.2332 0.2237 0.2238 0.2196 0.2387 0.2439(0.0445)*** (0.0497)*** (0.0396)*** (0.0519)*** (0.0341)*** (0.0366)*** (0.0375)*** (0.0582)*** (0.0428)***
Residency or Member of Council Teaching Hosps
â0.0012 0.0001 â0.0027 0.0082 0.0015 0.0028 0.0068 0.0085 0.0054(0.0118) (0.0112) (0.0102) (0.0119) (0.0105) (0.0100) (0.0103) (0.0120) (0.0102)
Vertically integrated with doctors
0.0038 0.0104 0.0058 0.0108 0.0057 0.0042 0.0051 0.0063 0.0005(0.0073) (0.0073) (0.0072) (0.0082) (0.0066) (0.0066) (0.0069) (0.0086) (0.0074)
Vert. integ. w drs (excl. integrated salary mdl)
0.0058 0.0023 0.0032 â0.0018 0.0055 0.0108 0.0100 0.0017 0.0121(0.0092) (0.0094) (0.0090) (0.0105) (0.0082) (0.0082) (0.0086) (0.0110) (0.0090)
FT physicians / total hospital beds
0.1904 0.2443 0.2022 0.2479 0.2613 0.2172 0.2592 0.2185 0.2536(0.0592)*** (0.0699)*** (0.0590)*** (0.0817)*** (0.0651)*** (0.0566)*** (0.0713)*** (0.1014)** (0.0831)***
Year 1998 â0.0447 â0.0487 â0.0579 â0.0230 â0.0763 â0.0887 â0.1032 â0.0089 â0.0835 (0.0739) (0.0745) (0.0703) (0.0800) (0.0668) (0.0678) (0.0711) (0.0802) (0.0714)
Year 2000 â0.0975 â0.1036 â0.1225 â0.0502 â0.1578 â0.1831 â0.2114 â0.0239 â0.1738 (0.1475) (0.1485) (0.1403) (0.1593) (0.1335) (0.1356) (0.1422) (0.1594) (0.1428)
Year 2002 â0.1152 â0.1233 â0.1533 â0.0473 â0.2053 â0.2415 â0.2844 â0.0080 â0.2277
(0.2206) (0.2222) (0.2098) (0.2384) (0.2000) (0.2030) (0.2130) (0.2386) (0.2139) Year 2004 â0.1579 â0.1660 â0.2117 â0.0684 â0.2770 â0.3294 â0.3839 â0.0066 â0.2983
(0.2943) (0.2966) (0.2800) (0.3181) (0.2667) (0.2707) (0.2841) (0.3182) (0.2852) Year 2006 â0.1700 â0.1828 â0.2401 â0.0623 â0.3228 â0.3853 â0.4553 0.0028 â0.3607
(0.3681) (0.3710) (0.3501) (0.3978) (0.3336) (0.3385) (0.3553) (0.3982) (0.3568) Year 2008 â0.1939 â0.2110 â0.2789 â0.0646 â0.3801 â0.4536 â0.5395 â0.0031 â0.4429
(0.4426) (0.4462) (0.4208) (0.4785) (0.4008) (0.4066) (0.4268) (0.4792) (0.4286) MSA dummy x year â0.0063 â0.0049 â0.0057 â0.0051 â0.0051 â0.0057 â0.0058 â0.0047 â0.0057
(0.0018)*** (0.0018)*** (0.0017)*** (0.0019)*** (0.0017)*** (0.0017)*** (0.0018)*** (0.0019)** (0.0018)*** Log population in 2000 census x year
â0.0006 â0.0019 â0.0010 â0.0016 â0.0015 â0.0017 â0.0018 â0.0012 â0.0014 (0.0007) (0.0007)*** (0.0007) (0.0007)** (0.0006)** (0.0006)*** (0.0007)*** (0.0007)* (0.0007)**
% Hispanic in 2000 census x year
â0.0098 â0.0088 â0.0092 â0.0092 â0.0063 â0.0065 â0.0059 â0.0093 â0.0067 (0.0054)* (0.0055) (0.0053)* (0.0057) (0.0050) (0.0050) (0.0053) (0.0058) (0.0053)
% Black in 2000 census x year
â0.0150 â0.0142 â0.0157 â0.0150 â0.0125 â0.0118 â0.0098 â0.0155 â0.0106 (0.0046)*** (0.0045)*** (0.0045)*** (0.0049)*** (0.0043)*** (0.0044)*** (0.0045)** (0.0049)*** (0.0045)**
% age 65+ in 2000 census x year
â0.0473 â0.0317 â0.0411 â0.0398 â0.0375 â0.0408 â0.0369 â0.0469 â0.0442 (0.0200)** (0.0206) (0.0194)** (0.0220)* (0.0187)** (0.0191)** (0.0198)* (0.0228)** (0.0202)**
% age 25â64 in 2000 census x year
â0.0303 â0.0361 â0.0281 â0.0391 â0.0314 â0.0145 â0.0150 â0.0368 â0.0127 (0.0157)* (0.0154)** (0.0154)* (0.0158)** (0.0140)** (0.0145) (0.0150) (0.0161)** (0.0152)
% university education in 2000 census x year
â0.0089 â0.0104 â0.0119 â0.0066 â0.0083 â0.0065 â0.0047 â0.0067 â0.0053 (0.0150) (0.0154) (0.0139) (0.0166) (0.0133) (0.0137) (0.0144) (0.0168) (0.0146)
Log median home value in 2000 census x year
0.0097 0.0121 0.0107 0.0103 0.0123 0.0125 0.0131 0.0093 0.0126(0.0028)*** (0.0027)*** (0.0027)*** (0.0030)*** (0.0025)*** (0.0025)*** (0.0026)*** (0.0030)*** (0.0026)***
Log median hh income in 2000 census x year
0.0015 0.0006 0.0013 0.0015 0.0009 0.0001 0.0002 0.0012 â0.0008 (0.0048) (0.0046) (0.0045) (0.0050) (0.0044) (0.0043) (0.0046) (0.0051) (0.0047)
Constant
15.2279 14.7670 15.9504 15.3689 15.9295 16.1173 16.1412 15.4592 15.4361 (0.4685)*** (0.6778)*** (0.8226)*** (0.5672)*** (0.6478)*** (0.6382)*** (0.6425)*** (0.7763)*** (0.8327)***
Unit of observation is a hospitalâyear. Data biannually from 1996 to 2008. Regressions include hospitalâspecific fixed effects, differenced out at means.
Table 5: Main effects by technology, by years since adoption
(1) (2) (3) (4)
Log total costs Log total costs Log total costs per admit
Log total costs per admit
Technology Basic EMRadoption
Advanced EMRadoption
Basic EMRadoption
Advanced EMRadoption
Adopt in previous 2 year period 0.0170 0.0310 0.0159 0.0336 (0.0067)** (0.0075)*** (0.0069)** (0.0078)***
Adopt at least 2 years earlier â0.0042 0.0112 â0.0054 0.0151 (0.0083) (0.0097) (0.0085) (0.0099)
Observations 9122 13193 7687 11082 # of hospitals 2178 3175 1564 2268 Râsquared 0.80 0.79 0.78 0.77
CONTROLS Log inpatient days â0.2988 â0.3961 â0.4804 â0.4497
(0.0899)*** (0.0793)*** (0.1345)*** (0.0920)***Log outpatient visits â0.1385 â0.1614 â0.0806 â0.0837
(0.0626)** (0.0619)*** (0.0890) (0.0897)Log inpatient days x Log inpatient days
0.0201 0.0193 0.0299 0.0238 (0.0055)*** (0.0041)*** (0.0082)*** (0.0047)***
Log outpatient visits x Log outpatient visits
0.0105 0.0071 0.0100 0.0065 (0.0040)*** (0.0015)*** (0.0048)** (0.0015)***
Log inpatient days x Log outpatient visits
â0.0038 0.0061 â0.0082 0.0001 (0.0067) (0.0051) (0.0090) (0.0072)
Log total hospital beds 0.0849 0.0905 0.0974 0.1046 (0.0180)*** (0.0166)*** (0.0192)*** (0.0171)***Independent practice association hospital
0.0008 0.0045 0.0029 0.0091 (0.0078) (0.0071) (0.0079) (0.0072)
Management service organization hospital
0.0185 0.0091 0.0163 0.0082 (0.0080)** (0.0076) (0.0079)** (0.0076)
Equity model hospital
â0.0048 â0.0115 â0.0084 â0.0156(0.0184) (0.0196) (0.0181) (0.0193)
Foundation hospital â0.0337 â0.0320 â0.0371 â0.0340(0.0151)** (0.0139)** (0.0160)** (0.0149)**
Log admissions 0.0132 â0.0006 â0.9422 â0.9642(0.0264) (0.0244) (0.0375)*** (0.0327)***
Births (000s) 0.0032 0.0062 â0.0033 0.0013 (0.0084) (0.0074) (0.0093) (0.0080)
Full time physicians and dentists (000s)
â0.3804 â0.3646 â0.3211 â0.3574(0.1857)** (0.1436)** (0.2225) (0.1643)**
Percent births 0.1205 0.1763 0.1817 0.1958 (0.1158) (0.0968)* (0.1331) (0.1118)*
Forâprofit ownership â0.0220 â0.0041 â0.0073 â0.0074(0.0370) (0.0287) (0.0365) (0.0298)
Nonâsecular nonprofit ownership 0.0180 0.0351 0.0323 0.0224 (0.0275) (0.0218) (0.0279) (0.0224)
Nonâprofit church ownership 0.0700 0.0837 0.0828 0.0758 (0.0372)* (0.0301)*** (0.0379)** (0.0310)**Log number of discharges Medicare
0.1371 0.1332 0.1353 0.1298 (0.0446)*** (0.0339)*** (0.0470)*** (0.0351)***
Log number of discharges 0.0075 0.0044 0.0070 0.0037
Medicaid (0.0025)*** (0.0022)** (0.0025)*** (0.0023)Log number of discharges total 0.2337 0.2198 0.2390 0.2440
(0.0519)*** (0.0376)*** (0.0582)*** (0.0429)***Residency or Member of Council Teaching Hospitals
0.0081 0.0073 0.0085 0.0057 (0.0119) (0.0104) (0.0120) (0.0102)
Vertically integrated with doctors 0.0110 0.0052 0.0064 0.0007 (0.0082) (0.0069) (0.0086) (0.0074)
Vert. integ. w drs (excl. integrated salary model)
â0.0019 0.0101 0.0016 0.0122 (0.0105) (0.0086) (0.0109) (0.0090)
FT physicians / total hospital beds 0.2485 0.2597 0.2184 0.2539 (0.0817)*** (0.0714)*** (0.1013)** (0.0832)***
Year 1998 â0.0219 â0.1021 â0.0075 â0.0824(0.0801) (0.0711) (0.0802) (0.0714)
Year 2000 â0.0496 â0.2094 â0.0221 â0.1717(0.1595) (0.1422) (0.1596) (0.1428)
Year 2002 â0.0465 â0.2823 â0.0053 â0.2255(0.2388) (0.2129) (0.2389) (0.2137)
Year 2004 â0.0675 â0.3805 â0.0030 â0.2951(0.3187) (0.2840) (0.3187) (0.2851)
Year 2006 â0.0615 â0.4510 0.0072 â0.3567(0.3986) (0.3551) (0.3988) (0.3566)
Year 2008 â0.0636 â0.5346 0.0021 â0.4387(0.4794) (0.4266) (0.4800) (0.4283)
MSA dummy x year â0.0051 â0.0059 â0.0047 â0.0057(0.0019)*** (0.0018)*** (0.0019)** (0.0018)***
Log population in 2000 census x year
â0.0016 â0.0018 â0.0012 â0.0014(0.0007)** (0.0007)*** (0.0007) (0.0007)**
% Hispanic in 2000 census x year
â0.0093 â0.0059 â0.0094 â0.0067(0.0057) (0.0053) (0.0058) (0.0053)
% Black in 2000 census x year
â0.0154 â0.0102 â0.0158 â0.0109(0.0049)*** (0.0045)** (0.0049)*** (0.0045)**
% age 65+ in 2000 census x year â0.0402 â0.0370 â0.0473 â0.0444(0.0221)* (0.0199)* (0.0228)** (0.0203)**
% age 25â64 in 2000 census x year â0.0388 â0.0151 â0.0366 â0.0129(0.0158)** (0.0150) (0.0160)** (0.0151)
% university education in 2000 census x year
â0.0065 â0.0043 â0.0066 â0.0050(0.0166) (0.0143) (0.0168) (0.0145)
Log median home value in 2000 census x year
0.0102 0.0130 0.0093 0.0125 (0.0030)*** (0.0026)*** (0.0030)*** (0.0026)***
Log median hh income in 2000 census x year
0.0015 0.0002 0.0012 â0.0007(0.0050) (0.0046) (0.0051) (0.0047)
Constant
15.3830 16.1381 15.4783 15.4264(0.5693)*** (0.6436)*** (0.7786)*** (0.8342)***
Unit of observation is a hospitalâyear. Data biannually from 1996 to 2008. Regressions include hospitalâspecific fixed effects, differenced out at means.
Table 6: Interactions with ITâintensive location
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Log total costs Log total costs per admit
Definition of ITâintensive location
Top quartile county IT intensive industries
MSA % of all local firms that had adopted basic IT by
2000
% of all local firms that had adopted advanced IT
by 2000
Top quartile county IT using industries
Technology Basic EMR adoption
Advanced EMR adoption
Basic EMRadoption
Advanced EMR adoption
Basic EMRadoption
Advanced EMR adoption
Basic EMRadoption
Advanced EMR adoption
Basic EMRadoption
Advanced EMR adoption
Adopt in previous 2 year period
0.0241 0.0336 0.0201 0.0304 0.0991 0.1381 0.0234 0.0500 0.0245 0.0375(0.0088)*** (0.0114)*** (0.0101)** (0.0124)** (0.0380)*** (0.0525)*** (0.0113)** (0.0151)*** (0.0091)*** (0.0119)***
Adopt at least 2 years earlier
0.0188 0.0331 0.0153 0.0551 0.0732 0.1244 0.0107 0.0426 0.0221 0.0383(0.0110)* (0.0145)** (0.0129) (0.0181)*** (0.0464) (0.0590)** (0.0131) (0.0199)** (0.0113)* (0.0149)**
Adopt in previous 2 yr pd x ITâintensive location
â0.0212 â0.0068 â0.0054 0.0009 â0.1085 â0.1353 â0.0891 â0.1802 â0.0217 â0.0091(0.0134) (0.0152) (0.0132) (0.0154) (0.0467)** (0.0622)** (0.0857) (0.1039)* (0.0137) (0.0156)
Adopt at least 2 yrs earlier x ITâintensive location
â0.0574 â0.0388 â0.0312 â0.0618 â0.1041 â0.1390 â0.1856 â0.2744 â0.0626 â0.0391(0.0166)*** (0.0193)** (0.0166)* (0.0214)*** (0.0577)* (0.0705)** (0.0901)** (0.1418)* (0.0169)*** (0.0195)**
Observations 8778 12778 9122 13193 8778 12778 8778 12778 7577 10958# of hospitals 2062 3033 2178 3175 2062 3033 2062 3033 1539 2240Râsquared 0.81 0.79 0.80 0.79 0.81 0.79 0.81 0.79 0.78 0.77
CONTROLS ITâintensive location x year
0.0035 â0.0003 â0.0032 â0.0049 0.0046 0.0011 â0.0030 â0.0038 0.0040 â0.0002(0.0021)* (0.0015) (0.0022) (0.0018)*** (0.0042) (0.0028) (0.0089) (0.0067) (0.0021)* (0.0015)
Log inpatient days â0.4044 â0.4792 â0.3020 â0.3956 â0.4092 â0.4781 â0.4008 â0.4765 â0.5269 â0.4787(0.0954)*** (0.0847)*** (0.0899)*** (0.0791)*** (0.0949)*** (0.0844)*** (0.0948)*** (0.0846)*** (0.1317)*** (0.0906)***
Log outpatient visits â0.1085 â0.1593 â0.1368 â0.1621 â0.1062 â0.1583 â0.1114 â0.1594 â0.0715 â0.0895(0.0709) (0.0702)** (0.0630)** (0.0618)*** (0.0707) (0.0701)** (0.0705) (0.0703)** (0.0888) (0.0908)
Log inpatient days x Log inpatient days
0.0270 0.0235 0.0203 0.0192 0.0272 0.0235 0.0268 0.0234 0.0339 0.0253(0.0056)*** (0.0037)*** (0.0055)*** (0.0041)*** (0.0056)*** (0.0037)*** (0.0056)*** (0.0037)*** (0.0082)*** (0.0045)***
Log outpatient visits x Log outpatient visits
0.0103 0.0071 0.0104 0.0072 0.0102 0.0071 0.0104 0.0071 0.0104 0.0066(0.0043)** (0.0015)*** (0.0040)*** (0.0015)*** (0.0042)** (0.0015)*** (0.0042)** (0.0015)*** (0.0049)** (0.0015)***
Log inpatient days x Log outpatient visits
â0.0064 0.0060 â0.0038 0.0061 â0.0064 0.0059 â0.0064 0.0059 â0.0102 0.0004(0.0073) (0.0058) (0.0067) (0.0051) (0.0072) (0.0058) (0.0072) (0.0058) (0.0090) (0.0073)
Log total hospital beds 0.0849 0.0921 0.0849 0.0915 0.0838 0.0918 0.0842 0.0922 0.0976 0.1044 (0.0177)*** (0.0170)*** (0.0180)*** (0.0166)*** (0.0178)*** (0.0169)*** (0.0177)*** (0.0170)*** (0.0184)*** (0.0168)***Independent practice 0.0007 0.0052 0.0004 0.0040 0.0007 0.0053 0.0008 0.0053 0.0010 0.0083
association hospital (0.0079) (0.0072) (0.0078) (0.0071) (0.0079) (0.0072) (0.0079) (0.0072) (0.0079) (0.0073)Management service organization hospital
0.0197 0.0092 0.0185 0.0088 0.0196 0.0090 0.0196 0.0097 0.0179 0.0087(0.0079)** (0.0077) (0.0080)** (0.0076) (0.0079)** (0.0077) (0.0080)** (0.0077) (0.0078)** (0.0076)
Equity model hospital
â0.0038 â0.0102 â0.0055 â0.0117 â0.0048 â0.0104 â0.0021 â0.0086 â0.0077 â0.0144(0.0179) (0.0195) (0.0182) (0.0194) (0.0181) (0.0193) (0.0180) (0.0193) (0.0175) (0.0193)
Foundation hospital â0.0333 â0.0320 â0.0336 â0.0316 â0.0342 â0.0322 â0.0347 â0.0331 â0.0338 â0.0321(0.0150)** (0.0140)** (0.0150)** (0.0139)** (0.0151)** (0.0140)** (0.0150)** (0.0139)** (0.0158)** (0.0148)**
Log admissions 0.0131 0.0006 0.0123 â0.0007 0.0121 â0.0002 0.0114 â0.0006 â0.9415 â0.9626(0.0269) (0.0249) (0.0264) (0.0244) (0.0267) (0.0248) (0.0268) (0.0249) (0.0373)*** (0.0329)***
Births (000s) â0.0015 0.0034 0.0032 0.0065 â0.0011 0.0031 â0.0009 0.0035 â0.0074 â0.0008(0.0083) (0.0073) (0.0083) (0.0074) (0.0083) (0.0073) (0.0083) (0.0073) (0.0092) (0.0080)
Full time physicians and dentists (000s)
â0.3418 â0.3431 â0.3767 â0.3651 â0.3456 â0.3440 â0.3400 â0.3413 â0.3087 â0.3441(0.1847)* (0.1430)** (0.1850)** (0.1432)** (0.1834)* (0.1426)** (0.1837)* (0.1429)** (0.2229) (0.1638)**
Percent births 0.1259 0.1684 0.1224 0.1721 0.1257 0.1703 0.1261 0.1680 0.1878 0.1905(0.1165) (0.0978)* (0.1156) (0.0968)* (0.1165) (0.0976)* (0.1162) (0.0978)* (0.1300) (0.1113)*
Forâprofit ownership 0.0003 0.0070 â0.0219 â0.0041 0.0004 0.0074 â0.0032 0.0070 0.0036 â0.0030(0.0355) (0.0286) (0.0371) (0.0287) (0.0355) (0.0286) (0.0358) (0.0287) (0.0348) (0.0295)
Nonâsecular nonprofit ownership
0.0214 0.0366 0.0185 0.0347 0.0209 0.0371 0.0191 0.0370 0.0305 0.0202(0.0276) (0.0220)* (0.0276) (0.0217) (0.0273) (0.0219)* (0.0276) (0.0220)* (0.0277) (0.0224)
Nonâprofit church ownership
0.0745 0.0858 0.0704 0.0838 0.0734 0.0858 0.0734 0.0864 0.0820 0.0748(0.0372)** (0.0304)*** (0.0373)* (0.0301)*** (0.0370)** (0.0304)*** (0.0373)** (0.0304)*** (0.0375)** (0.0309)**
Log number of discharges Medicare
0.1306 0.1274 0.1373 0.1341 0.1309 0.1274 0.1314 0.1281 0.1259 0.1220(0.0443)*** (0.0335)*** (0.0443)*** (0.0337)*** (0.0439)*** (0.0333)*** (0.0437)*** (0.0332)*** (0.0453)*** (0.0338)***
Log number of discharges Medicaid
0.0069 0.0037 0.0074 0.0044 0.0069 0.0038 0.0068 0.0038 0.0065 0.0030(0.0026)*** (0.0022)* (0.0025)*** (0.0022)* (0.0026)*** (0.0022)* (0.0026)*** (0.0022)* (0.0025)** (0.0023)
Log number of discharges total
0.2620 0.2374 0.2341 0.2196 0.2618 0.2374 0.2616 0.2369 0.2578 0.2559(0.0555)*** (0.0387)*** (0.0518)*** (0.0375)*** (0.0554)*** (0.0386)*** (0.0552)*** (0.0385)*** (0.0599)*** (0.0430)***
Residency or Member of Council Teaching Hosps
0.0110 0.0088 0.0082 0.0080 0.0101 0.0086 0.0104 0.0087 0.0114 0.0074(0.0115) (0.0101) (0.0119) (0.0103) (0.0114) (0.0101) (0.0114) (0.0101) (0.0117) (0.0100)
Vertically integrated with doctors
0.0107 0.0043 0.0113 0.0053 0.0103 0.0044 0.0105 0.0045 0.0066 0.0001(0.0082) (0.0070) (0.0082) (0.0069) (0.0082) (0.0070) (0.0082) (0.0070) (0.0084) (0.0073)
Vert. integ. w drs (excl. integrated salary mdl)
â0.0013 0.0113 â0.0018 0.0104 â0.0010 0.0111 â0.0007 0.0112 0.0019 0.0133(0.0103) (0.0086) (0.0105) (0.0086) (0.0104) (0.0086) (0.0104) (0.0086) (0.0107) (0.0089)
FT physicians / total hospital beds
0.2274 0.2471 0.2467 0.2599 0.2306 0.2492 0.2277 0.2475 0.2098 0.2464(0.0813)*** (0.0713)*** (0.0814)*** (0.0714)*** (0.0802)*** (0.0703)*** (0.0807)*** (0.0708)*** (0.1015)** (0.0830)***
Year 1998 â0.0053 â0.0914 â0.0232 â0.1003 â0.0101 â0.0899 â0.0025 â0.0891 0.0060 â0.0742(0.0794) (0.0719) (0.0802) (0.0710) (0.0789) (0.0706) (0.0790) (0.0709) (0.0795) (0.0723)
Year 2000 â0.0183 â0.1884 â0.0518 â0.2060 â0.0288 â0.1853 â0.0133 â0.1840 0.0041 â0.1553(0.1582) (0.1438) (0.1596) (0.1419) (0.1572) (0.1411) (0.1573) (0.1418) (0.1583) (0.1446)
Year 2002 0.0012 â0.2504 â0.0500 â0.2772 â0.0144 â0.2459 0.0086 â0.2441 0.0347 â0.2005(0.2369) (0.2154) (0.2389) (0.2126) (0.2353) (0.2114) (0.2356) (0.2124) (0.2370) (0.2165)
Year 2004 â0.0026 â0.3371 â0.0722 â0.3735 â0.0231 â0.3313 0.0074 â0.3288 0.0514 â0.2611(0.3161) (0.2873) (0.3188) (0.2835) (0.3140) (0.2819) (0.3142) (0.2832) (0.3161) (0.2888)
Year 2006 0.0200 â0.3966 â0.0667 â0.4417 â0.0057 â0.3895 0.0325 â0.3862 0.0747 â0.3149(0.3954) (0.3593) (0.3987) (0.3546) (0.3927) (0.3526) (0.3931) (0.3542) (0.3955) (0.3612)
Year 2008 0.0295 â0.4740 â0.0696 â0.5233 â0.0022 â0.4658 0.0440 â0.4616 0.0799 â0.3907(0.4757) (0.4316) (0.4796) (0.4259) (0.4725) (0.4236) (0.4729) (0.4256) (0.4762) (0.4340)
MSA dummy x year â0.0051 â0.0059 Above Above â0.0053 â0.0062 â0.0052 â0.0060 â0.0046 â0.0058(0.0019)*** (0.0018)*** (0.0020)*** (0.0018)*** (0.0019)*** (0.0018)*** (0.0019)** (0.0018)***
Log population in 2000 census x year
â0.0020 â0.0019 â0.0016 â0.0018 â0.0018 â0.0019 â0.0018 â0.0019 â0.0017 â0.0015(0.0008)** (0.0007)*** (0.0007)** (0.0007)*** (0.0008)** (0.0007)*** (0.0007)** (0.0007)*** (0.0008)** (0.0007)**
% Hispanic in 2000 census x year
â0.0082 â0.0061 â0.0088 â0.0057 â0.0085 â0.0063 â0.0084 â0.0059 â0.0083 â0.0065(0.0057) (0.0053) (0.0057) (0.0053) (0.0058) (0.0053) (0.0056) (0.0053) (0.0058) (0.0053)
% Black in 2000 census x year
â0.0160 â0.0101 â0.0153 â0.0099 â0.0160 â0.0103 â0.0156 â0.0102 â0.0163 â0.0108(0.0049)*** (0.0045)** (0.0049)*** (0.0045)** (0.0049)*** (0.0045)** (0.0049)*** (0.0045)** (0.0048)*** (0.0045)**
% age 65+ in 2000 census x year
â0.0405 â0.0397 â0.0387 â0.0369 â0.0431 â0.0425 â0.0425 â0.0416 â0.0460 â0.0444(0.0224)* (0.0203)* (0.0220)* (0.0199)* (0.0221)* (0.0199)** (0.0222)* (0.0200)** (0.0229)** (0.0205)**
% age 25â64 in 2000 census x year
â0.0399 â0.0167 â0.0388 â0.0153 â0.0393 â0.0161 â0.0405 â0.0162 â0.0369 â0.0141(0.0158)** (0.0151) (0.0158)** (0.0148) (0.0158)** (0.0150) (0.0159)** (0.0151) (0.0159)** (0.0152)
% university education in 2000 census x year
â0.0001 0.0005 â0.0053 â0.0031 â0.0005 â0.0017 0.0018 â0.0009 â0.0002 0.0015(0.0170) (0.0145) (0.0165) (0.0143) (0.0168) (0.0145) (0.0168) (0.0146) (0.0172) (0.0147)
Log median home value in 2000 census x year
0.0094 0.0125 0.0100 0.0129 0.0094 0.0127 0.0093 0.0125 0.0086 0.0119(0.0030)*** (0.0026)*** (0.0030)*** (0.0026)*** (0.0030)*** (0.0026)*** (0.0030)*** (0.0026)*** (0.0031)*** (0.0026)***
Log median hh income in 2000 census x year
0.0019 0.0005 0.0016 0.0002 0.0018 0.0001 0.0020 0.0004 0.0016 â0.0003(0.0050) (0.0046) (0.0050) (0.0045) (0.0050) (0.0046) (0.0050) (0.0046) (0.0051) (0.0047)
Constant
15.5781 16.4562 15.3866 16.1276 15.6001 16.4525 15.6004 16.4546 15.5312 15.5346(0.6426)*** (0.7445)*** (0.5705)*** (0.6430)*** (0.6374)*** (0.7410)*** (0.6360)*** (0.7434)*** (0.7725)*** (0.8451)***
Unit of observation is a hospitalâyear. Data biannually from 1996 to 2008. Regressions include hospitalâspecific fixed effects, differenced out at means.
Table 7: Interactions with internal HIT experience
(1) (2) (3) (4) (5) (6)
Log total costs Log total costs per admit
Definition of HIT experience Employ computer programmers in 1996
Have at least 1 clinical application in 1996
Employ computer programmers in 1996
Technology Basic EMRadoption
Advanced EMR adoption
Basic EMRadoption
Advanced EMR adoption
Basic EMR adoption
Advanced EMR adoption
Adopt in previous 2 year period
0.0042 0.0400 0.0020 0.0599 0.0030 0.0399(0.0094) (0.0115)*** (0.0168) (0.0253)** (0.0094) (0.0116)***
Adopt at least 2 years earlier
â0.0169 0.0180 â0.0125 0.0289 â0.0180 0.0174(0.0108) (0.0160) (0.0194) (0.0335) (0.0108)* (0.0162)
Adopt in previous 2 year period x HIT experience
0.0053 â0.0425 0.0007 â0.0443 0.0060 â0.0427(0.0224) (0.0211)** (0.0191) (0.0271) (0.0225) (0.0212)**
Adopt at least 2 years earlier x HIT experience
0.0007 â0.0176 â0.0069 â0.0238 0.0013 â0.0168(0.0328) (0.0298) (0.0231) (0.0366) (0.0330) (0.0299)
Observations 3976 5581 5550 3966 3899 5451# of hospitals 816 1162 1156 814 784 1108Râsquared 0.84 0.82 0.82 0.84 0.80 0.78
CONTROLS HIT experience x year â0.0034 â0.0012 0.0023 0.0012 â0.0035 â0.0012
(0.0034) (0.0024) (0.0018) (0.0024) (0.0034) (0.0024)Log inpatient days â0.9459 â0.9678 â0.9233 â0.9722 â0.9725 â0.9625
(0.2977)*** (0.2446)*** (0.3038)*** (0.2447)*** (0.3160)*** (0.2676)***Log outpatient visits â0.3104 â0.3453 â0.3227 â0.3419 â0.2913 â0.3507
(0.2060) (0.1563)** (0.2064) (0.1552)** (0.2150) (0.1648)**Log inpatient days x Log inpatient days
0.0560 0.0349 0.0553 0.0351 0.0581 0.0343(0.0154)*** (0.0117)*** (0.0156)*** (0.0118)*** (0.0176)*** (0.0133)**
Log outpatient visits x Log outpatient visits
0.0250 0.0073 0.0258 0.0071 0.0250 0.0072(0.0105)** (0.0014)*** (0.0105)** (0.0013)*** (0.0106)** (0.0013)***
Log inpatient days x Log outpatient visits
â0.0198 0.0228 â0.0206 0.0227 â0.0216 0.0234(0.0165) (0.0134)* (0.0165) (0.0133)* (0.0177) (0.0142)*
Log total hospital beds 0.1213 0.1005 0.1252 0.1030 0.1179 0.0965 (0.0279)*** (0.0263)*** (0.0278)*** (0.0264)*** (0.0283)*** (0.0268)***Independent practice association hospital
0.0044 0.0087 0.0022 0.0065 0.0054 0.0096(0.0102) (0.0097) (0.0101) (0.0096) (0.0102) (0.0096)
Management service organization hospital
0.0203 0.0082 0.0206 0.0117 0.0212 0.0083(0.0098)** (0.0102) (0.0097)** (0.0101) (0.0098)** (0.0103)
Equity model hospital
0.0211 â0.0032 0.0217 â0.0058 0.0211 â0.0035(0.0327) (0.0321) (0.0323) (0.0319) (0.0324) (0.0320)
Foundation hospital â0.0441 â0.0647 â0.0442 â0.0629 â0.0439 â0.0647(0.0151)*** (0.0206)*** (0.0154)*** (0.0205)*** (0.0152)*** (0.0209)***
Log admissions 0.0198 â0.0551 0.0162 â0.0582 â0.9583 â1.0434(0.0615) (0.0473) (0.0615) (0.0475) (0.0619)*** (0.0479)***
Births (000s) â0.0141 â0.0026 â0.0125 â0.0014 â0.0187 â0.0049(0.0181) (0.0120) (0.0180) (0.0120) (0.0183) (0.0119)
Full time physicians and dentists (000s)
â0.2257 â0.3507 â0.2283 â0.3478 â0.2067 â0.3214(0.2765) (0.2731) (0.2809) (0.2787) (0.2895) (0.2858)
Percent births 0.3931 0.2002 0.3573 0.1843 0.4594 0.2445(0.2285)* (0.1720) (0.2274) (0.1730) (0.2341)* (0.1731)
Forâprofit ownership â0.0724 â0.0258 â0.0743 â0.0266 â0.0715 â0.0260(0.0773) (0.0527) (0.0774) (0.0530) (0.0766) (0.0524)
Nonâsecular nonprofit ownership
0.0180 0.0172 0.0166 0.0178 0.0188 0.0176(0.0669) (0.0451) (0.0673) (0.0454) (0.0660) (0.0448)
Nonâprofit church ownership
0.0049 0.0443 0.0063 0.0464 0.0021 0.0425(0.0721) (0.0517) (0.0722) (0.0520) (0.0712) (0.0516)
Log number of discharges Medicare
0.3078 0.3105 0.3073 0.3108 0.3000 0.3074(0.0415)*** (0.0360)*** (0.0416)*** (0.0360)*** (0.0418)*** (0.0370)***
Log number of discharges Medicaid
0.0080 0.0040 0.0093 0.0054 0.0073 0.0037(0.0033)** (0.0030) (0.0031)*** (0.0030)* (0.0032)** (0.0030)
Log number of discharges total
0.1857 0.2091 0.1840 0.2078 0.1820 0.2062(0.0439)*** (0.0384)*** (0.0439)*** (0.0383)*** (0.0439)*** (0.0388)***
Residency or Member of Council Teaching Hospitals
0.0009 â0.0058 0.0012 â0.0052 0.0014 â0.0055(0.0125) (0.0115) (0.0125) (0.0115) (0.0126) (0.0115)
Vertically integrated with doctors
0.0182 0.0124 0.0153 0.0095 0.0180 0.0128(0.0105)* (0.0095) (0.0104) (0.0093) (0.0105)* (0.0095)
Vert. integ. w drs (excl. integrated salary model)
â0.0123 0.0036 â0.0103 0.0054 â0.0128 0.0025(0.0123) (0.0111) (0.0121) (0.0111) (0.0124) (0.0113)
FT physicians / total hospital beds
0.2222 0.2690 0.2245 0.2703 0.2143 0.2551(0.1258)* (0.1262)** (0.1266)* (0.1275)** (0.1327) (0.1333)*
Year 1998 0.1132 â0.0018 0.0843 â0.0235 0.1143 â0.0046(0.1076) (0.1057) (0.1067) (0.1056) (0.1080) (0.1059)
Year 2000 0.2097 â0.0127 0.1572 â0.0515 0.2109 â0.0184(0.2141) (0.2113) (0.2123) (0.2112) (0.2148) (0.2118)
Year 2002 0.3481 0.0066 0.2699 â0.0509 0.3499 â0.0021(0.3206) (0.3165) (0.3177) (0.3162) (0.3216) (0.3172)
Year 2004 0.4724 0.0176 0.3677 â0.0602 0.4742 0.0054(0.4277) (0.4224) (0.4239) (0.4221) (0.4290) (0.4233)
Year 2006 0.6014 0.0355 0.4706 â0.0615 0.6033 0.0203(0.5345) (0.5279) (0.5297) (0.5276) (0.5361) (0.5292)
Year 2008 0.7259 0.0386 0.5689 â0.0767 0.7273 0.0186(0.6427) (0.6340) (0.6370) (0.6336) (0.6447) (0.6355)
MSA dummy x year â0.0056 â0.0066 â0.0060 â0.0066 â0.0053 â0.0064(0.0023)** (0.0023)*** (0.0024)** (0.0023)*** (0.0024)** (0.0023)***
Log population in 2000 census x year
â0.0015 â0.0011 â0.0017 â0.0013 â0.0014 â0.0011(0.0010) (0.0010) (0.0010)* (0.0009) (0.0010) (0.0010)
% Hispanic in 2000 census x year
â0.0112 â0.0045 â0.0105 â0.0048 â0.0108 â0.0041(0.0078) (0.0079) (0.0078) (0.0080) (0.0078) (0.0079)
% Black in 2000 census x year
â0.0160 â0.0116 â0.0163 â0.0118 â0.0163 â0.0120(0.0070)** (0.0067)* (0.0070)** (0.0067)* (0.0069)** (0.0067)*
% age 65+ in 2000 census x year
â0.0424 0.0051 â0.0455 0.0007 â0.0444 0.0039(0.0287) (0.0301) (0.0285) (0.0299) (0.0286) (0.0301)
% age 25â64 in 2000 census x year
â0.0043 â0.0070 â0.0064 â0.0092 â0.0057 â0.0078(0.0204) (0.0232) (0.0200) (0.0229) (0.0204) (0.0233)
% university education in 2000 census x year
0.0091 â0.0057 0.0041 â0.0108 0.0078 â0.0071(0.0218) (0.0198) (0.0214) (0.0197) (0.0219) (0.0198)
Log median home value in 2000 census x year
0.0073 0.0108 0.0077 0.0113 0.0071 0.0108(0.0039)* (0.0037)*** (0.0039)** (0.0038)*** (0.0040)* (0.0038)***
Log median hh income in 2000 census x year
â0.0048 â0.0040 â0.0036 â0.0033 â0.0046 â0.0038(0.0068) (0.0074) (0.0067) (0.0074) (0.0068) (0.0074)
Constant
19.3883 20.0020 20.0309 19.3676 19.0042 19.6471(1.6507)*** (1.7325)*** (1.7203)*** (1.6923)*** (1.6232)*** (1.8005)***
Unit of observation is a hospitalâyear. Data biannually from 1996 to 2008. Regressions include hospitalâspecific fixed effects, differenced out at means.
Table 8: Interactions of adoption with top quartile ITâusing county and with programmers in 1996
(1) (2) (3) (4)
Technology Basic EMRadoption
Advanced EMR adoption
Basic EMRadoption
Advanced EMR adoption
Adopt in previous 2 year period 0.0140 0.0376 0.0052 0.0301 (0.0113) (0.0158)** (0.0117) (0.0171)*
Adopt at least 2 years earlier 0.0054 0.0379 â0.0061 0.0336 (0.0149) (0.0214)* (0.0150) (0.0233)
Adopt in previous 2 year period x IT intensive county
â0.0155 0.0087 0.0016 0.0234 (0.0167) (0.0206) (0.0186) (0.0238)
Adopt at least 2 years earlier x IT intensive county
â0.0407 â0.0270 â0.0181 â0.0195 (0.0208)* (0.0272) (0.0211) (0.0314)
Adopt in previous 2 year period x Programmer 1996
0.0040 â0.0475 0.0573 0.0012 (0.0224) (0.0217)** (0.0332)* (0.0412)
Adopt at least 2 years earlier x Programmer 1996
0.0040 â0.0167 0.0765 0.0076 (0.0325) (0.0294) (0.0573) (0.0499)
Adopt in previous 2 year period x IT intensive county x Programmer 1996
â0.0845 â0.0700 (0.0436)* (0.0487)
Adopt at least 2 years earlier x IT intensive county x Programmer 1996
â0.1137 â0.0336 (0.0681)* (0.0610)
Observations 3936 5539 3936 5539 # of hospitals 805 1150 805 1150 Râsquared 0.85 0.82 0.85 0.82
CONTROLS Top quartile IT using county x year â0.0036 â0.0010 â0.0072 â0.0012
(0.0034) (0.0024) (0.0072) (0.0042) Programmer in 1996 x year 0.0015 â0.0017 0.0006 â0.0017
(0.0025) (0.0018) (0.0025) (0.0018) Top quartile IT using county x Programmer x year
0.0058 0.0003 (0.0077) (0.0050)
Log inpatient days â1.0341 â1.0137 â1.0317 â1.0096 (0.2548)*** (0.2285)*** (0.2532)*** (0.2281)***
Log outpatient visits â0.2336 â0.3332 â0.2313 â0.3354 (0.1892) (0.1612)** (0.1891) (0.1615)**
Log inpatient days x Log inpatient days 0.0610 0.0379 0.0614 0.0376 (0.0141)*** (0.0108)*** (0.0140)*** (0.0108)***
Log outpatient visits x Log outpatient visits
0.0222 0.0073 0.0224 0.0073 (0.0098)** (0.0013)*** (0.0097)** (0.0013)***
Log inpatient days x Log outpatient visits â0.0207 0.0218 â0.0215 0.0219 (0.0165) (0.0138) (0.0164) (0.0138)
Log total hospital beds 0.1140 0.0980 0.1135 0.0983 (0.0262)*** (0.0260)*** (0.0259)*** (0.0258)*** Independent practice association hospital
0.0054 0.0095 0.0055 0.0091 (0.0103) (0.0097) (0.0103) (0.0097)
Management service organization hospital
0.0193 0.0075 0.0189 0.0077 (0.0098)** (0.0102) (0.0099)* (0.0102)
Equity model hospital
0.0218 â0.0018 0.0239 â0.0013 (0.0324) (0.0325) (0.0325) (0.0327)
Foundation hospital â0.0418 â0.0636 â0.0415 â0.0639 (0.0150)*** (0.0204)*** (0.0149)*** (0.0204)***
Log admissions 0.0340 â0.0506 0.0331 â0.0512 (0.0590) (0.0462) (0.0589) (0.0462)
Births (000s) â0.0212 â0.0054 â0.0212 â0.0056 (0.0165) (0.0111) (0.0166) (0.0111)
Full time physicians and dentists (000s) â0.1994 â0.3355 â0.2030 â0.3395 (0.2724) (0.2692) (0.2722) (0.2687)
Percent births 0.4145 0.2061 0.4079 0.2053 (0.2156)* (0.1661) (0.2163)* (0.1660)
Forâprofit ownership â0.0463 â0.0111 â0.0470 â0.0111 (0.0747) (0.0511) (0.0745) (0.0511)
Nonâsecular nonprofit ownership 0.0156 0.0151 0.0153 0.0148 (0.0672) (0.0449) (0.0670) (0.0449)
Nonâprofit church ownership 0.0064 0.0458 0.0031 0.0453 (0.0721) (0.0516) (0.0719) (0.0515)
Log number of discharges Medicare 0.2833 0.2911 0.2833 0.2907 (0.0362)*** (0.0322)*** (0.0362)*** (0.0323)***
Log number of discharges Medicaid 0.0072 0.0036 0.0071 0.0037 (0.0033)** (0.0030) (0.0033)** (0.0030)
Log number of discharges total 0.2176 0.2323 0.2175 0.2326 (0.0372)*** (0.0343)*** (0.0372)*** (0.0343)***
Residency or Member of Council Teaching Hospitals
0.0020 â0.0055 0.0038 â0.0052 (0.0125) (0.0114) (0.0124) (0.0113)
Vertically integrated with doctors 0.0168 0.0117 0.0169 0.0114 (0.0106) (0.0095) (0.0107) (0.0095)
Vert. integ. w drs (excl. integrated salary model)
â0.0127 0.0037 â0.0121 0.0038 (0.0124) (0.0112) (0.0124) (0.0112)
FT physicians / total hospital beds 0.2106 0.2617 0.2099 0.2629 (0.1245)* (0.1251)** (0.1239)* (0.1251)**
Year 1998 0.1133 â0.0110 0.1168 â0.0122 (0.1073) (0.1060) (0.1078) (0.1061)
Year 2000 0.2102 â0.0310 0.2166 â0.0332 (0.2136) (0.2121) (0.2145) (0.2123)
Year 2002 0.3503 â0.0202 0.3601 â0.0234 (0.3199) (0.3178) (0.3213) (0.3181)
Year 2004 0.4759 â0.0177 0.4887 â0.0225 (0.4268) (0.4242) (0.4286) (0.4246)
Year 2006 0.6061 â0.0084 0.6225 â0.0140 (0.5334) (0.5302) (0.5356) (0.5308)
Year 2008 0.7283 â0.0174 0.7476 â0.0244 (0.6413) (0.6367) (0.6439) (0.6373)
MSA dummy x year â0.0056 â0.0065 â0.0055 â0.0066 (0.0023)** (0.0023)*** (0.0023)** (0.0023)***
Log population in 2000 census x year â0.0014 â0.0009 â0.0015 â0.0009 (0.0010) (0.0010) (0.0011) (0.0010)
% Hispanic in 2000 census x year
â0.0122 â0.0063 â0.0121 â0.0061 (0.0078) (0.0078) (0.0079) (0.0078)
% Black in 2000 census x year
â0.0165 â0.0113 â0.0167 â0.0112 (0.0069)** (0.0067)* (0.0069)** (0.0067)*
% age 65+ in 2000 census x year â0.0447 0.0044 â0.0469 0.0045 (0.0281) (0.0298) (0.0282)* (0.0299)
% age 25â64 in 2000 census x year â0.0078 â0.0102 â0.0060 â0.0091 (0.0212) (0.0238) (0.0212) (0.0238)
% university education in 2000 census x year
0.0154 0.0002 0.0146 0.0000 (0.0220) (0.0199) (0.0219) (0.0199)
Log median home value in 2000 census x year
0.0071 0.0106 0.0070 0.0103 (0.0039)* (0.0037)*** (0.0039)* (0.0037)***
Log median hh income in 2000 census x year
â0.0046 â0.0034 â0.0046 â0.0031 (0.0067) (0.0074) (0.0068) (0.0074)
Constant 19.1783 20.0544 19.1640 20.0472 (1.4847)*** (1.7318)*** (1.4685)*** (1.7302)***
Unit of observation is a hospitalâyear. Data biannually from 1996 to 2008. Regressions include hospitalâspecific fixed effects, differenced out at means. Dependent variable is logged total costs
Table 9: Impact of specific technologies on different types of costs
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Total diagnostic radiology costs Total nursing administration costs
Technology CDR CDSS Basic EMR CPOE Advanced EMR
CDR CDSS Basic EMR CPOE Advanced EMR
Adopt in previous 2 year period
0.0101 0.0034 0.0001 0.0141 0.0063 0.0238 0.0191 0.0249 0.0137 0.0173(0.0087) (0.0090) (0.0099) (0.0127) (0.0123) (0.0142)* (0.0151) (0.0167) (0.0202) (0.0213)
Adopt at least 2 years earlier
â0.0294 â0.0193 â0.0312 â0.0105 â0.0231 0.0337 0.0379 0.0407 0.0309 0.0362(0.0111)*** (0.0119) (0.0126)** (0.0166) (0.0159) (0.0195)* (0.0211)* (0.0224)* (0.0277) (0.0283)
Observations 11119 9914 12139 13516 12139 11802 10598 8929 14358 12918# of hospitals 2551 2278 2800 3101 2800 2839 2555 2143 3463 3129Râsquared 0.67 0.68 0.66 0.66 0.66 0.36 0.35 0.35 0.35 0.34
CONTROLS Log inpatient days â0.6522 â0.1212 â0.1856 â0.5997 â0.6734 â0.3817 â0.3220 â0.4034 â0.1386 â0.1261
(0.2085)*** (0.1244) (0.1444) (0.2254)*** (0.2274)*** (0.2058)* (0.2150) (0.2333)* (0.1807) (0.1905)Log outpatient visits 0.2182 0.0046 â0.0191 0.1042 0.1057 â0.0936 â0.0630 â0.0903 â0.0010 â0.0068
(0.1464) (0.0972) (0.1086) (0.1475) (0.1530) (0.1180) (0.1272) (0.1440) (0.1137) (0.1180)Log inpatient days x Log inpatient days
0.0460 0.0088 0.0075 0.0372 0.0408 0.0171 0.0140 0.0139 0.0076 0.0068(0.0144)*** (0.0070) (0.0084) (0.0158)** (0.0160)** (0.0124) (0.0127) (0.0150) (0.0108) (0.0115)
Log outpatient visits x Log outpatient visits
0.0053 0.0066 0.0041 0.0056 0.0058 0.0005 â0.0006 â0.0043 0.0006 0.0009(0.0017)*** (0.0015)*** (0.0034) (0.0018)*** (0.0018)*** (0.0026) (0.0028) (0.0063) (0.0023) (0.0024)
Log inpatient days x Log outpatient visits
â0.0270 â0.0088 â0.0012 â0.0169 â0.0173 0.0095 0.0082 0.0191 0.0000 0.0001(0.0117)** (0.0082) (0.0098) (0.0119) (0.0123) (0.0096) (0.0102) (0.0140) (0.0093) (0.0096)
Log total hospital beds 0.0759 0.0791 0.0810 0.0764 0.0771 0.0241 0.0693 0.0578 0.0699 0.0664 (0.0224)*** (0.0216)*** (0.0245)*** (0.0225)*** (0.0241)*** (0.0354) (0.0359)* (0.0390) (0.0421)* (0.0447)Independent practice association hospital
0.0166 0.0064 0.0096 0.0180 0.0191 â0.0067 â0.0053 â0.0076 â0.0209 â0.0160(0.0113) (0.0110) (0.0124) (0.0097)* (0.0104)* (0.0159) (0.0162) (0.0175) (0.0147) (0.0156)
Management service organization hospital
0.0037 0.0081 0.0090 0.0090 0.0150 â0.0036 â0.0061 â0.0054 â0.0362 â0.0255(0.0120) (0.0122) (0.0138) (0.0110) (0.0119) (0.0189) (0.0201) (0.0226) (0.0188)* (0.0196)
Equity model hospital
0.0234 â0.0047 â0.0100 0.0193 0.0185 0.0832 0.0693 0.0633 0.0603 0.0865(0.0393) (0.0208) (0.0217) (0.0398) (0.0437) (0.0393)** (0.0389)* (0.0437) (0.0381) (0.0381)**
Foundation hospital â0.0413 â0.0382 â0.0300 â0.0405 â0.0330 â0.0113 â0.0023 0.0047 â0.0166 â0.0095(0.0175)** (0.0170)** (0.0187) (0.0172)** (0.0182)* (0.0362) (0.0363) (0.0413) (0.0316) (0.0338)
Log admissions 0.0120 0.0228 0.0477 0.0215 0.0268 0.0162 0.0191 â0.0065 0.0186 0.0159(0.0431) (0.0413) (0.0457) (0.0392) (0.0407) (0.0529) (0.0556) (0.0581) (0.0488) (0.0499)
Births (000s) â0.0068 0.0021 0.0005 â0.0044 â0.0021 â0.0025 â0.0049 â0.0152 0.0155 0.0148
(0.0134) (0.0135) (0.0146) (0.0116) (0.0115) (0.0208) (0.0231) (0.0247) (0.0182) (0.0189)Full time physicians and dentists (000s)
â0.0776 â0.1165 â0.0847 â0.2072 â0.1312 0.1700 0.0947 0.0458 0.0346 0.0876(0.1170) (0.1176) (0.1558) (0.0985)** (0.1185) (0.3061) (0.2665) (0.3629) (0.2245) (0.2879)
Percent births 0.1280 0.0771 0.0399 0.1363 0.1519 0.2186 0.2184 0.2076 â0.0876 â0.0544(0.1943) (0.1909) (0.2150) (0.1568) (0.1627) (0.2420) (0.2554) (0.2715) (0.2321) (0.2460)
Forâprofit ownership â0.0664 â0.0609 â0.0361 â0.0422 â0.0363 â0.0099 0.0161 0.0872 0.0771 0.0885(0.0463) (0.0523) (0.0549) (0.0414) (0.0445) (0.0835) (0.0971) (0.1035) (0.0683) (0.0736)
Nonâsecular nonprofit ownership
0.0392 0.0356 0.0687 0.0456 0.0554 0.0233 0.0455 0.1019 0.0931 0.0935(0.0311) (0.0337) (0.0357)* (0.0302) (0.0315)* (0.0703) (0.0808) (0.0874) (0.0582) (0.0625)
Nonâprofit church ownership
0.0240 0.0235 0.0600 0.0385 0.0448 0.1188 0.1574 0.2148 0.1206 0.1330(0.0433) (0.0467) (0.0500) (0.0393) (0.0408) (0.0802) (0.0914)* (0.0984)** (0.0697)* (0.0739)*
Log number of discharges Medicare
0.2059 0.1945 0.1785 0.1836 0.1995 0.1800 0.1810 0.1624 0.1269 0.1324(0.0555)*** (0.0517)*** (0.0513)*** (0.0440)*** (0.0510)*** (0.0387)*** (0.0408)*** (0.0392)*** (0.0303)*** (0.0315)***
Log number of discharges Medicaid
0.0055 0.0093 0.0082 0.0065 0.0058 0.0028 0.0037 0.0022 0.0102 0.0080(0.0041) (0.0042)** (0.0044)* (0.0038)* (0.0040) (0.0054) (0.0061) (0.0063) (0.0054)* (0.0057)
Log number of discharges total
0.1438 0.1727 0.1595 0.1878 0.1842 0.1881 0.1931 0.1935 0.2096 0.2130(0.0538)*** (0.0563)*** (0.0585)*** (0.0443)*** (0.0498)*** (0.0518)*** (0.0558)*** (0.0581)*** (0.0419)*** (0.0447)***
Residency or Member of Council Teaching Hosps
0.0008 â0.0060 0.0010 0.0039 0.0015 â0.0155 â0.0219 â0.0113 0.0071 0.0140(0.0176) (0.0179) (0.0196) (0.0161) (0.0167) (0.0316) (0.0336) (0.0372) (0.0286) (0.0303)
Vertically integrated with doctors
â0.0049 â0.0031 0.0010 â0.0107 â0.0138 â0.0324 â0.0347 â0.0427 â0.0057 â0.0065(0.0111) (0.0112) (0.0125) (0.0101) (0.0106) (0.0189)* (0.0199)* (0.0227)* (0.0172) (0.0185)
Vert. integ. w drs (excl. integrated salary model)
0.0024 â0.0018 â0.0093 0.0061 0.0098 0.0480 0.0532 0.0516 0.0158 0.0214(0.0129) (0.0133) (0.0149) (0.0116) (0.0124) (0.0240)** (0.0258)** (0.0286)* (0.0214) (0.0234)
FT physicians / total hospital beds
0.0833 0.1198 0.1038 0.1334 0.1050 0.0030 â0.0107 â0.0148 â0.0133 â0.0306(0.0567) (0.0614)* (0.0709) (0.0558)** (0.0604)* (0.1211) (0.1254) (0.1492) (0.1154) (0.1296)
Year 1998 â0.0617 â0.0578 â0.1523 â0.0763 â0.1316 0.0768 0.0087 0.1260 â0.1029 â0.1512(0.1339) (0.1222) (0.1337) (0.1175) (0.1290) (0.2264) (0.2503) (0.2649) (0.1987) (0.2208)
Year 2000 â0.0815 â0.0786 â0.2691 â0.1097 â0.2227 0.1408 â0.0038 0.2458 â0.2156 â0.3179(0.2678) (0.2440) (0.2671) (0.2346) (0.2575) (0.4526) (0.5003) (0.5295) (0.3971) (0.4412)
Year 2002 â0.0891 â0.0850 â0.3744 â0.1299 â0.2980 0.3111 0.0905 0.4696 â0.2366 â0.3889(0.4011) (0.3657) (0.4004) (0.3515) (0.3858) (0.6807) (0.7523) (0.7968) (0.5966) (0.6631)
Year 2004 â0.1653 â0.1527 â0.5438 â0.2159 â0.4422 0.4187 0.1226 0.6284 â0.3159 â0.5186(0.5346) (0.4877) (0.5339) (0.4685) (0.5141) (0.9072) (1.0028) (1.0616) (0.7955) (0.8840)
Year 2006 â0.2229 â0.2079 â0.6992 â0.2867 â0.5676 0.5960 0.2248 0.8447 â0.3259 â0.5873(0.6695) (0.6100) (0.6678) (0.5862) (0.6434) (1.1342) (1.2538) (1.3267) (0.9944) (1.1047)
Year 2008 â0.3030 â0.2937 â0.8833 â0.3836 â0.7206 0.7360 0.2889 1.0349 â0.3802 â0.6875(0.8052) (0.7323) (0.8016) (0.7046) (0.7732) (1.3622) (1.5056) (1.5936) (1.1942) (1.3267)
MSA dummy x year â0.0010 0.0003 0.0010 â0.0009 â0.0012 â0.0063 â0.0043 â0.0038 â0.0065 â0.0071(0.0027) (0.0027) (0.0030) (0.0024) (0.0026) (0.0047) (0.0049) (0.0054) (0.0044) (0.0047)
Log population in 2000 census x year
â0.0042 â0.0055 â0.0061 â0.0051 â0.0055 0.0027 0.0022 0.0013 0.0015 0.0013(0.0012)*** (0.0010)*** (0.0012)*** (0.0010)*** (0.0011)*** (0.0017) (0.0018) (0.0019) (0.0016) (0.0017)
% Hispanic in 2000 census x year
â0.0070 â0.0016 0.0003 â0.0019 â0.0013 â0.0044 â0.0009 â0.0054 0.0044 0.0057(0.0077) (0.0073) (0.0077) (0.0068) (0.0073) (0.0131) (0.0139) (0.0144) (0.0132) (0.0146)
% Black in 2000 census x year
â0.0221 â0.0221 â0.0216 â0.0179 â0.0162 â0.0076 0.0025 â0.0008 0.0032 0.0026(0.0073)*** (0.0073)*** (0.0080)*** (0.0067)*** (0.0072)** (0.0116) (0.0126) (0.0134) (0.0112) (0.0119)
% age 65+ in 2000 census x year
â0.0204 â0.0135 â0.0080 0.0053 0.0114 â0.0867 â0.0224 â0.0578 â0.0387 â0.0311(0.0283) (0.0292) (0.0312) (0.0264) (0.0278) (0.0546) (0.0540) (0.0574) (0.0466) (0.0499)
% age 25â64 in 2000 census x year
â0.0174 â0.0060 â0.0038 â0.0177 0.0122 â0.0084 â0.0271 â0.0375 â0.0280 0.0012(0.0230) (0.0235) (0.0251) (0.0208) (0.0213) (0.0410) (0.0431) (0.0443) (0.0362) (0.0390)
% university education in 2000 census x year
â0.0168 â0.0270 â0.0344 â0.0025 â0.0067 0.0802 0.0960 0.0928 0.0437 0.0407(0.0248) (0.0251) (0.0277) (0.0219) (0.0241) (0.0480)* (0.0532)* (0.0580) (0.0421) (0.0468)
Log median home value in 2000 census x year
0.0127 0.0134 0.0135 0.0124 0.0137 0.0000 â0.0013 0.0015 0.0032 0.0074(0.0041)*** (0.0039)*** (0.0045)*** (0.0036)*** (0.0038)*** (0.0071) (0.0074) (0.0081) (0.0067) (0.0071)
Log median hh income in 2000 census x year
0.0014 0.0010 0.0061 0.0026 0.0019 â0.0021 0.0036 â0.0029 0.0059 0.0015(0.0076) (0.0073) (0.0079) (0.0069) (0.0073) (0.0132) (0.0142) (0.0151) (0.0121) (0.0130)
Constant 13.1213 11.4766 11.9272 13.2580 13.4514 11.6707 10.9682 11.7732 9.9951 9.8916 (1.0397)*** (0.9232)*** (1.0187)*** (0.9890)*** (0.9949)*** (1.2025)*** (1.2904)*** (1.3846)*** (1.1350)*** (1.1594)***
Unit of observation is a hospitalâyear. Data biannually from 1996 to 2008. Regressions include hospitalâspecific fixed effects, differenced out at means.
Appendix Table 1: Leads and lags to get timing of impact (Dependent variable is log total costs)
(1) (2) (3) (4) (5) (6)
Technology Basic EMR adoption Advanced EMR adoption
Sample All firms Bottom 3 quartiles IT intensive counties
Top quartile IT intensive counties
All firms Bottom 3 quartiles IT intensive counties
Top quartile IT intensive counties
Will adopt in 4 to 6 years 0.0296 0.0296 0.0236 0.0349 0.0393 0.0296(0.0102)*** (0.0122)** (0.0164) (0.0094)*** (0.0127)*** (0.0135)**
Will adopt in 2 to 4 years 0.0326 0.0337 0.0227 0.0222 0.0267 0.0188(0.0128)** (0.0161)** (0.0201) (0.0100)** (0.0139)* (0.0141)
Will adopt within 2 years 0.0404 0.0392 0.0260 0.0376 0.0401 0.0339(0.0153)*** (0.0191)** (0.0239) (0.0111)*** (0.0161)** (0.0152)**
Adopt in previous 2 year period
0.0541 0.0609 0.0181 0.0557 0.0597 0.0458(0.0184)*** (0.0225)*** (0.0298) (0.0124)*** (0.0176)*** (0.0175)***
Adopt 2 to 4 years earlier 0.0438 0.0601 â0.0064 0.0453 0.0612 0.0292(0.0216)** (0.0261)** (0.0353) (0.0145)*** (0.0206)*** (0.0200)
Adopt 4 to 6 years earlier 0.0422 0.0701 â0.0254 0.0444 0.0703 0.0167(0.0248)* (0.0303)** (0.0403) (0.0165)*** (0.0235)*** (0.0232)
Adopt at least 6 years earlier 0.0431 0.0699 â0.0340 0.0132 0.0491 â0.0219(0.0299) (0.0368)* (0.0484) (0.0213) (0.0273)* (0.0333)
Observations 9122 4810 3968 13193 7244 5534# of hospitals 2178 1161 901 3175 1757 1276Râsquared 0.80 0.83 0.80 0.79 0.79 0.80
CONTROLS Log inpatient days â0.2965 â0.4140 â0.3154 â0.3952 â0.2448 â0.5733
(0.0896)*** (0.1088)*** (0.1519)** (0.0796)*** (0.1107)** (0.1208)***Log outpatient visits â0.1283 â0.1247 â0.0684 â0.1541 â0.2008 â0.0984
(0.0626)** (0.0800) (0.1062) (0.0619)** (0.1049)* (0.0922)Log inpatient days x Log inpatient days
0.0201 0.0217 0.0281 0.0193 0.0100 0.0303(0.0055)*** (0.0070)*** (0.0090)*** (0.0041)*** (0.0056)* (0.0044)***
Log outpatient visits x Log outpatient visits
0.0101 0.0066 0.0124 0.0068 0.0084 0.0056(0.0040)** (0.0040)* (0.0059)** (0.0015)*** (0.0040)** (0.0015)***
Log inpatient days x Log outpatient visits
â0.0040 0.0038 â0.0155 0.0060 0.0075 0.0027(0.0067) (0.0084) (0.0124) (0.0051) (0.0085) (0.0078)
Log total hospital beds 0.0848 0.0825 0.0851 0.0911 0.1340 0.0457
(0.0180)*** (0.0209)*** (0.0275)*** (0.0167)*** (0.0192)*** (0.0240)*Independent practice association hospital
0.0007 â0.0028 0.0027 0.0039 â0.0022 0.0115
(0.0078) (0.0114) (0.0111) (0.0071) (0.0104) (0.0096)Management service organization hospital
0.0180 â0.0009 0.0322 0.0083 â0.0023 0.0179(0.0079)** (0.0116) (0.0104)*** (0.0076) (0.0114) (0.0099)*
Equity model hospital
â0.0035 â0.0290 0.0166 â0.0112 â0.0460 0.0095(0.0188) (0.0291) (0.0218) (0.0196) (0.0287) (0.0237)
Foundation hospital â0.0328 â0.0192 â0.0397 â0.0323 â0.0365 â0.0154(0.0150)** (0.0175) (0.0244) (0.0140)** (0.0187)* (0.0198)
Log admissions 0.0132 0.0144 â0.0170 â0.0008 â0.0010 â0.0032(0.0264) (0.0304) (0.0526) (0.0244) (0.0280) (0.0451)
Births (000s) 0.0034 0.0204 â0.0024 0.0060 0.0302 0.0038(0.0084) (0.0192) (0.0105) (0.0074) (0.0207) (0.0087)
Full time physicians and dentists (000s)
â0.3722 â0.7511 0.0915 â0.3658 â0.8041 0.0428(0.1855)** (0.4187)* (0.1847) (0.1447)** (0.4535)* (0.1432)
Percent births 0.1142 0.1504 0.0735 0.1789 0.1886 0.0216(0.1158) (0.1544) (0.1816) (0.0968)* (0.1447) (0.1349)
Forâprofit ownership â0.0219 â0.0001 0.0048 â0.0023 0.0057 0.0277(0.0371) (0.0489) (0.0602) (0.0286) (0.0357) (0.0475)
Nonâsecular nonprofit ownership
0.0181 0.0069 0.0394 0.0339 0.0157 0.0686(0.0271) (0.0324) (0.0526) (0.0215) (0.0251) (0.0416)*
Nonâprofit church ownership 0.0699 0.0465 0.1025 0.0824 0.0393 0.1416(0.0369)* (0.0479) (0.0638) (0.0298)*** (0.0384) (0.0506)***
Log number of discharges Medicare
0.1364 0.2136 0.0944 0.1325 0.1335 0.1318(0.0443)*** (0.0445)*** (0.0471)** (0.0337)*** (0.0457)*** (0.0491)***
Log number of discharges Medicaid
0.0075 0.0059 0.0070 0.0043 0.0029 0.0028(0.0025)*** (0.0033)* (0.0035)** (0.0022)* (0.0031) (0.0031)
Log number of discharges total
0.2343 0.1999 0.2832 0.2204 0.2306 0.2413(0.0520)*** (0.0442)*** (0.0848)*** (0.0375)*** (0.0446)*** (0.0642)***
Residency or Member of Council Teaching Hospitals
0.0099 0.0024 0.0150 0.0082 â0.0011 0.0135(0.0120) (0.0140) (0.0151) (0.0103) (0.0150) (0.0129)
Vertically integrated with doctors
0.0099 0.0075 0.0135 0.0046 0.0021 0.0031(0.0082) (0.0112) (0.0117) (0.0069) (0.0093) (0.0104)
Vert. integ. w drs (excl. integrated salary model)
â0.0012 0.0066 â0.0116 0.0098 0.0113 0.0103(0.0105) (0.0129) (0.0142) (0.0086) (0.0116) (0.0120)
FT physicians / total hospital 0.2454 0.4247 0.0001 0.2580 0.4452 0.0273
beds (0.0815)*** (0.0965)*** (0.0833) (0.0711)*** (0.0998)*** (0.0701)Year 1998 â0.0219 â0.0967 0.1220 â0.1084 â0.3182 0.1808
(0.0798) (0.0951) (0.1356) (0.0710) (0.0911)*** (0.1233)Year 2000 â0.0491 â0.2182 0.2568 â0.2216 â0.6535 0.3719
(0.1588) (0.1903) (0.2685) (0.1420) (0.1828)*** (0.2455)Year 2002 â0.0432 â0.2947 0.4186 â0.2989 â0.9503 0.5944
(0.2377) (0.2851) (0.4019) (0.2127) (0.2741)*** (0.3672)Year 2004 â0.0593 â0.3960 0.5623 â0.4000 â1.2706 0.7954
(0.3171) (0.3802) (0.5363) (0.2836) (0.3655)*** (0.4896)Year 2006 â0.0478 â0.4612 0.7220 â0.4742 â1.5624 1.0210
(0.3966) (0.4756) (0.6700) (0.3547) (0.4572)*** (0.6122)*Year 2008 â0.0441 â0.5337 0.8604 â0.5605 â1.8651 1.2184
(0.4771) (0.5713) (0.8065) (0.4260) (0.5488)*** (0.7356)*MSA dummy x year â0.0052 â0.0051 â0.0080 â0.0059 â0.0067 â0.0080
(0.0019)*** (0.0022)** (0.0042)* (0.0018)*** (0.0023)*** (0.0034)**Log population in 2000 census x year
â0.0017 â0.0026 â0.0009 â0.0018 â0.0026 â0.0011(0.0007)** (0.0012)** (0.0012) (0.0007)*** (0.0012)** (0.0010)
% Hispanic in 2000 census x year
â0.0087 â0.0025 â0.0165 â0.0054 â0.0034 â0.0060(0.0058) (0.0062) (0.0116) (0.0053) (0.0064) (0.0100)
% Black in 2000 census x year
â0.0147 â0.0137 â0.0205 â0.0093 â0.0076 â0.0139(0.0049)*** (0.0064)** (0.0074)*** (0.0045)** (0.0059) (0.0069)**
% age 65+ in 2000 census x year
â0.0386 â0.0263 â0.0603 â0.0356 â0.0302 â0.0400(0.0219)* (0.0286) (0.0358)* (0.0197)* (0.0267) (0.0308)
% age 25â64 in 2000 census x year
â0.0402 â0.0415 â0.0211 â0.0158 â0.0317 0.0185(0.0158)** (0.0197)** (0.0275) (0.0150) (0.0208) (0.0247)
% university education in 2000 census x year
â0.0061 â0.0113 0.0087 â0.0065 â0.0312 0.0409(0.0166) (0.0235) (0.0303) (0.0143) (0.0208) (0.0255)
Log median home value in 2000 census x year
0.0103 0.0150 0.0040 0.0132 0.0207 0.0038(0.0030)*** (0.0042)*** (0.0047) (0.0026)*** (0.0037)*** (0.0039)
Log median household income in 2000 census x year
0.0013 0.0007 â0.0009 0.0003 0.0049 â0.0076(0.0050) (0.0061) (0.0084) (0.0046) (0.0059) (0.0073)
Constant 15.3022 15.2933 15.5743 16.0945 15.0773 17.2294 (0.5709)*** (0.6625)*** (1.0392)*** (0.6488)*** (1.0003)*** (0.9580)***
Unit of observation is a hospitalâyear. Data biannually from 1996 to 2008. Regressions include hospitalâspecific fixed effects, differenced out at means.
Appendix Table 2: No controls
(1) (2) (3) (4) (5) (6) (7) (8)
Technology Basic EMRadoption
Advanced EMR adoption
Basic EMRadoption
Advanced EMR adoption
Basic EMRadoption
Advanced EMR adoption
Basic EMRadoption
Advanced EMR adoption
Adopt in previous 2 year period 0.2981 0.3785 0.2979 0.3827 0.2807 0.4055 0.2922 0.4204(0.0079)*** (0.0092)*** (0.0106)*** (0.0144)*** (0.0127)*** (0.0145)*** (0.0151)*** (0.0198)***
Adopt at least 2 years earlier 0.5837 0.5941 0.5993 0.6156 0.5725 0.6037 0.5955 0.6449(0.0081)*** (0.0114)*** (0.0112)*** (0.0179)*** (0.0124)*** (0.0184)*** (0.0149)*** (0.0255)***
Adopt in previous 2 year period x IT intensive county
â0.2784 â0.3311 â0.2542 â0.2530 â0.3229(0.0157)*** (0.0184)*** (0.0239)*** (0.0208)*** (0.0243)***
Adopt at least 2 years earlier x IT intensive county
â0.6123 â0.5804 â0.5773 â0.5603 â0.5585(0.0183)*** (0.0240)*** (0.0368)*** (0.0248)*** (0.0324)***
Adopt in previous 2 year period x Programmer 1996
â0.3778 â0.0960 â0.1790(0.0254)*** (0.0252)*** (0.0276)***
Adopt at least 2 years earlier x Programmer 1996
â0.5801 â0.2214 â0.2491(0.0387)*** (0.0411)*** (0.0428)***
Observations 12093 17631 11542 16933 5256 7462 5147 7326# of hospitals 2298 3367 2155 3184 837 1200 819 1177Râsquared 0.37 0.15 0.53 0.38 0.46 0.25 0.59 0.45
CONTROLS Top quartile IT using county x year 0.0696 0.0659 0.0612 0.0584
(0.0016)*** (0.0012)*** (0.0023)*** (0.0016)***Programmer in 1996 x year 0.0666 0.0666 0.0253 0.0288
(0.0037)*** (0.0025)*** (0.0046)*** (0.0035)***Constant 17.4994 17.2661 17.4944 17.2990 18.1014 17.8641 18.0514 17.8526 (0.0051)*** (0.0094)*** (0.0056)*** (0.0098)*** (0.0069)*** (0.0126)*** (0.0073)*** (0.0125)***
Unit of observation is a hospitalâyear. Data biannually from 1996 to 2008. Regressions include hospitalâspecific fixed effects, differenced out at means. Dependent variable is logged total costs
Appendix Table 3: Include missing controls and add a dummy for âmissing dataâ
(1) (2) (3) (4) (5) (6) (7) (8)
Technology Basic EMRadoption
Advanced EMR adoption
Basic EMRadoption
Advanced EMR adoption
Basic EMRadoption
Advanced EMR adoption
Basic EMRadoption
Advanced EMR adoption
Adopt in previous 2 year period 0.0256 0.0412 0.0326 0.0516 0.0157 0.0519 0.0226 0.0649(0.0067)*** (0.0075)*** (0.0091)*** (0.0119)*** (0.0092)* (0.0113)*** (0.0119)* (0.0162)***
Adopt at least 2 years earlier 0.0035 0.0275 0.0283 0.0484 â0.0026 0.0403 0.0241 0.0636(0.0084) (0.0092)*** (0.0116)** (0.0141)*** (0.0120) (0.0149)*** (0.0165) (0.0212)***
Adopt in previous 2 year period x IT intensive county
â0.0172 â0.0193 â0.0101 â0.0231(0.0133) (0.0150) (0.0171) (0.0203)
Adopt at least 2 years earlier x IT intensive county
â0.0568 â0.0389 â0.0488 â0.0426(0.0168)*** (0.0184)** (0.0223)** (0.0261)
Adopt in previous 2 year period x Programmer 1996
â0.0006 â0.0363 â0.0021 â0.0330(0.0200) (0.0208)* (0.0202) (0.0212)
Adopt at least 2 years earlier x Programmer 1996
â0.0213 â0.0251 â0.0172 â0.0163(0.0333) (0.0300) (0.0330) (0.0296)
Observations 11909 17329 11467 16791 5199 7356 5130 7281# of hospitals 2269 3322 2146 3171 831 1189 819 1176Râsquared 0.76 0.75 0.77 0.75 0.80 0.77 0.80 0.78
CONTROLS Missing discharge data 0.8555 0.8846 0.8985 0.9210 1.2566 1.4966 1.2214 1.4707
(0.0796)*** (0.0651)*** (0.0829)*** (0.0675)*** (0.1799)*** (0.1679)*** (0.1733)*** (0.1659)***Missing hospital type data 0.0014 â0.0149 0.0023 â0.0141 â0.0170 â0.0291 â0.0143 â0.0280
(0.0093) (0.0076)* (0.0093) (0.0077)* (0.0125) (0.0108)*** (0.0125) (0.0108)***Top quartile IT using county x year 0.0016 â0.0014 â0.0017 â0.0038
(0.0020) (0.0014) (0.0025) (0.0018)**Programmer in 1996 x year 0.0028 0.0024 0.0027 0.0024
(0.0035) (0.0024) (0.0035) (0.0024)Log inpatient days â0.3839 â0.3218 â0.4362 â0.3487 â0.3930 â0.4332 â0.3940 â0.4357
(0.0719)*** (0.0576)*** (0.0845)*** (0.0640)*** (0.2590) (0.2181)** (0.2713) (0.2115)**Log outpatient visits â0.0962 â0.0912 â0.0775 â0.0817 â0.1303 â0.2440 â0.1317 â0.2397
(0.0458)** (0.0427)** (0.0509) (0.0462)* (0.2636) (0.1701) (0.2494) (0.1703)Log inpatient days x Log inpatient days 0.0232 0.0187 0.0272 0.0207 0.0314 0.0149 0.0330 0.0151
(0.0047)*** (0.0033)*** (0.0050)*** (0.0034)*** (0.0159)** (0.0113) (0.0161)** (0.0108)Log outpatient visits x Log outpatient 0.0071 0.0066 0.0071 0.0065 0.0169 0.0064 0.0185 0.0063
visits (0.0020)*** (0.0013)*** (0.0020)*** (0.0013)*** (0.0115) (0.0014)*** (0.0112)* (0.0014)***Log inpatient days x Log outpatient visits 0.0001 0.0008 â0.0020 â0.0001 â0.0182 0.0151 â0.0219 0.0147
(0.0056) (0.0041) (0.0063) (0.0045) (0.0162) (0.0144) (0.0162) (0.0144)Log total hospital beds 0.0566 0.0772 0.0698 0.0867 0.0974 0.1005 0.1145 0.1117 (0.0190)*** (0.0154)*** (0.0188)*** (0.0154)*** (0.0312)*** (0.0265)*** (0.0272)*** (0.0249)***Independent practice association hospital
0.0072 0.0126 0.0066 0.0125 0.0124 0.0152 0.0149 0.0166(0.0076) (0.0067)* (0.0075) (0.0067)* (0.0098) (0.0093) (0.0097) (0.0094)*
Management service organization hospital
0.0175 0.0091 0.0186 0.0091 0.0215 0.0087 0.0207 0.0077(0.0085)** (0.0074) (0.0085)** (0.0074) (0.0103)** (0.0095) (0.0103)** (0.0094)
Equity model hospital
â0.0075 â0.0172 â0.0059 â0.0156 â0.0185 â0.0478 â0.0166 â0.0467(0.0168) (0.0185) (0.0161) (0.0184) (0.0257) (0.0284)* (0.0246) (0.0283)*
Foundation hospital â0.0355 â0.0242 â0.0353 â0.0236 â0.0545 â0.0648 â0.0549 â0.0643(0.0167)** (0.0152) (0.0169)** (0.0154) (0.0195)*** (0.0249)*** (0.0191)*** (0.0248)***
Log admissions 0.1575 0.1685 0.1791 0.1807 0.2260 0.1905 0.2712 0.2109(0.0281)*** (0.0226)*** (0.0316)*** (0.0243)*** (0.0653)*** (0.0493)*** (0.0644)*** (0.0490)***
Births (000s) 0.0074 0.0080 0.0033 0.0050 â0.0046 â0.0039 â0.0026 â0.0035(0.0090) (0.0078) (0.0090) (0.0078) (0.0177) (0.0131) (0.0175) (0.0132)
Full time physicians and dentists (000s) â0.1951 â0.1826 â0.1876 â0.1693 â0.0234 â0.0851 â0.0268 â0.0828(0.2018) (0.1613) (0.2040) (0.1627) (0.3569) (0.3472) (0.3563) (0.3486)
Percent births 0.1371 0.1471 0.1162 0.1329 0.2463 0.1373 0.0897 0.0592(0.1017) (0.0796)* (0.0998) (0.0799)* (0.2695) (0.1900) (0.2258) (0.1801)
Forâprofit ownership â0.0456 â0.0214 â0.0379 â0.0182 â0.1305 â0.0509 â0.1202 â0.0465(0.0311) (0.0230) (0.0309) (0.0230) (0.0514)** (0.0317) (0.0506)** (0.0314)
Nonâsecular nonprofit ownership 0.0173 0.0122 0.0144 0.0111 â0.0138 0.0038 â0.0213 0.0011(0.0226) (0.0181) (0.0228) (0.0182) (0.0351) (0.0253) (0.0359) (0.0257)
Nonâprofit church ownership 0.0342 0.0417 0.0309 0.0404 â0.0401 0.0173 â0.0445 0.0171(0.0319) (0.0262) (0.0319) (0.0262) (0.0410) (0.0318) (0.0412) (0.0319)
Log number of discharges Medicare 0.0850 0.0857 0.0814 0.0815 0.1167 0.1268 0.1036 0.1147(0.0189)*** (0.0161)*** (0.0193)*** (0.0161)*** (0.0283)*** (0.0239)*** (0.0276)*** (0.0233)***
Log number of discharges Medicaid 0.0027 0.0033 0.0019 0.0028 0.0078 0.0073 0.0073 0.0071(0.0022) (0.0021) (0.0022) (0.0021) (0.0033)** (0.0032)** (0.0033)** (0.0032)**
Log number of discharges total 0.0180 0.0210 0.0263 0.0289 0.0256 0.0442 0.0338 0.0522(0.0165) (0.0141) (0.0168) (0.0142)** (0.0227) (0.0206)** (0.0224) (0.0202)***
Residency or Member of Council Teaching Hospitals
0.0013 0.0121 0.0044 0.0134 0.0014 0.0061 0.0016 0.0058(0.0120) (0.0113) (0.0118) (0.0110) (0.0124) (0.0136) (0.0122) (0.0132)
Vertically integrated with doctors 0.0096 0.0059 0.0108 0.0065 0.0194 0.0093 0.0178 0.0083(0.0081) (0.0069) (0.0082) (0.0070) (0.0112)* (0.0103) (0.0112) (0.0102)
Vert. integ. w drs (excl. integrated salary model)
â0.0044 0.0061 â0.0058 0.0058 â0.0140 â0.0020 â0.0144 â0.0004(0.0104) (0.0082) (0.0105) (0.0082) (0.0136) (0.0120) (0.0137) (0.0120)
FT physicians / total hospital beds 0.2013 0.2062 0.1960 0.1998 0.1769 0.2008 0.1791 0.2031(0.0841)** (0.0684)*** (0.0850)** (0.0690)*** (0.1556) (0.1496) (0.1552) (0.1498)
Year 1998 â0.1352 â0.0965 â0.1200 â0.0855 â0.0175 â0.0259 â0.0197 â0.0370(0.0840) (0.0744) (0.0830) (0.0746) (0.1113) (0.1024) (0.1105) (0.1028)
Year 2000 â0.2775 â0.2007 â0.2477 â0.1781 â0.0451 â0.0533 â0.0512 â0.0751(0.1675)* (0.1481) (0.1655) (0.1486) (0.2214) (0.2045) (0.2199) (0.2053)
Year 2002 â0.3910 â0.2724 â0.3473 â0.2389 â0.0353 â0.0533 â0.0459 â0.0864(0.2508) (0.2218) (0.2478) (0.2226) (0.3313) (0.3060) (0.3291) (0.3073)
Year 2004 â0.5271 â0.3692 â0.4681 â0.3234 â0.0399 â0.0651 â0.0540 â0.1091(0.3345) (0.2958) (0.3306) (0.2970) (0.4421) (0.4081) (0.4391) (0.4099)
Year 2006 â0.6340 â0.4410 â0.5595 â0.3842 â0.0505 â0.0871 â0.0665 â0.1412(0.4183) (0.3699) (0.4133) (0.3713) (0.5526) (0.5105) (0.5488) (0.5128)
Year 2008 â0.7713 â0.5462 â0.6848 â0.4816 â0.0874 â0.1410 â0.1066 â0.2068(0.5018) (0.4437) (0.4958) (0.4454) (0.6632) (0.6123) (0.6586) (0.6149)
MSA dummy x year â0.0036 â0.0029 â0.0038 â0.0031 â0.0041 â0.0039 â0.0043 â0.0041(0.0020)* (0.0017)* (0.0020)** (0.0017)* (0.0025) (0.0023)* (0.0025)* (0.0023)*
Log population in 2000 census x year â0.0016 â0.0023 â0.0015 â0.0021 â0.0016 â0.0029 â0.0009 â0.0022(0.0008)** (0.0006)*** (0.0008)* (0.0007)*** (0.0011) (0.0010)*** (0.0011) (0.0010)**
% Hispanic in 2000 census x year
0.0001 0.0022 â0.0028 â0.0021 â0.0044 0.0077 â0.0076 0.0026(0.0055) (0.0050) (0.0058) (0.0052) (0.0073) (0.0075) (0.0075) (0.0076)
% Black in 2000 census x year
â0.0139 â0.0118 â0.0143 â0.0115 â0.0143 â0.0072 â0.0139 â0.0064(0.0049)*** (0.0041)*** (0.0049)*** (0.0041)*** (0.0069)** (0.0060) (0.0069)** (0.0060)
% age 65+ in 2000 census x year â0.0252 â0.0235 â0.0275 â0.0274 â0.0525 â0.0090 â0.0551 â0.0099(0.0214) (0.0185) (0.0215) (0.0186) (0.0249)** (0.0236) (0.0245)** (0.0233)
% age 25â64 in 2000 census x year â0.0518 â0.0401 â0.0563 â0.0456 â0.0082 â0.0200 â0.0109 â0.0255(0.0152)*** (0.0141)*** (0.0153)*** (0.0144)*** (0.0183) (0.0209) (0.0189) (0.0217)
% university education in 2000 census x year
â0.0015 0.0198 0.0084 0.0283 â0.0014 0.0225 0.0178 0.0382(0.0171) (0.0141) (0.0177) (0.0143)** (0.0226) (0.0204) (0.0229) (0.0205)*
Log median home value in 2000 census x year
0.0099 0.0109 0.0093 0.0107 0.0098 0.0097 0.0083 0.0091(0.0031)*** (0.0026)*** (0.0032)*** (0.0026)*** (0.0044)** (0.0038)** (0.0044)* (0.0039)**
Log median hh income in 2000 census x year
0.0079 0.0044 0.0080 0.0043 â0.0008 0.0009 0.0003 0.0017(0.0052) (0.0046) (0.0052) (0.0047) (0.0071) (0.0066) (0.0071) (0.0066)
Constant 16.5171 15.9968 16.4314 15.9371 16.3218 17.1961 15.9457 16.9888 (0.4645)*** (0.3881)*** (0.5395)*** (0.4262)*** (1.0282)*** (1.6134)*** (0.9427)*** (1.5915)***Unit of observation is a hospitalâyear. Data biannually from 1996 to 2008. Regressions include hospitalâspecific fixed effects, differenced out at means. Dependent variable is logged total costs
Appendix Table 4: Missing EMR set to zero. Also includes missing controls and adds a dummy for âmissing dataâ
(1) (2) (3) (4) (5) (6) (7) (8)
Technology Basic EMRadoption
Advanced EMR adoption
Basic EMRadoption
Advanced EMR adoption
Basic EMRadoption
Advanced EMR adoption
Basic EMRadoption
Advanced EMR adoption
Adopt in previous 2 year period 0.0278 0.0374 0.0350 0.0494 0.0140 0.0485 0.0204 0.0643(0.0062)*** (0.0073)*** (0.0086)*** (0.0119)*** (0.0090) (0.0112)*** (0.0113)* (0.0161)***
Adopt at least 2 years earlier 0.0088 0.0200 0.0335 0.0445 â0.0064 0.0338 0.0189 0.0641(0.0072) (0.0090)** (0.0099)*** (0.0140)*** (0.0103) (0.0147)** (0.0142) (0.0213)***
Adopt in previous 2 year period x IT intensive county
â0.0151 â0.0399 â0.0094 â0.0345(0.0125) (0.0196)** (0.0158) (0.0200)*
Adopt at least 2 years earlier x IT intensive county
â0.0511 â0.0319 â0.0459 â0.0188(0.0143)*** (0.0278) (0.0187)** (0.0274)
Adopt in previous 2 year period x Programmer 1996
â0.0223 â0.0034 â0.0023 â0.0284(0.0148) (0.0177) (0.0179) (0.0200)
Adopt at least 2 years earlier x Programmer 1996
â0.0449 â0.0237 â0.0155 â0.0552(0.0181)** (0.0253) (0.0249) (0.0255)**
Observations 22369 22369 21737 21737 9594 9594 9495 9495# of hospitals 4277 4277 4100 4100 1549 1549 1532 1532Râsquared 0.76 0.76 0.76 0.76 0.79 0.79 0.79 0.79
CONTROLS Missing discharge data 0.8330 0.8321 0.8692 0.8675 1.4447 1.4392 1.4368 1.4288
(0.0569)*** (0.0569)*** (0.0591)*** (0.0590)*** (0.1406)*** (0.1399)*** (0.1402)*** (0.1392)***Missing hospital type data â0.0113 â0.0107 â0.0103 â0.0103 â0.0265 â0.0251 â0.0250 â0.0247
(0.0068)* (0.0068) (0.0068) (0.0068) (0.0095)*** (0.0095)*** (0.0095)*** (0.0094)***Top quartile IT using county x year 0.0011 0.0001 â0.0010 â0.0018
(0.0013) (0.0012) (0.0017) (0.0016)Programmer in 1996 x year 0.0031 0.0029 0.0027 0.0025
(0.0019) (0.0018) (0.0020) (0.0018)Log inpatient days â0.3299 â0.3298 â0.3431 â0.3444 â0.3338 â0.3364 â0.3347 â0.3320
(0.0528)*** (0.0528)*** (0.0588)*** (0.0588)*** (0.1618)** (0.1613)** (0.1596)** (0.1589)**Log outpatient visits â0.0948 â0.0932 â0.0834 â0.0821 â0.1544 â0.1495 â0.1616 â0.1608
(0.0389)** (0.0390)** (0.0421)** (0.0420)* (0.1054) (0.1049) (0.1052) (0.1046)Log inpatient days x Log inpatient days 0.0192 0.0192 0.0205 0.0206 0.0145 0.0147 0.0140 0.0139
(0.0030)*** (0.0030)*** (0.0031)*** (0.0031)*** (0.0109) (0.0109) (0.0106) (0.0106)Log outpatient visits x Log outpatient 0.0068 0.0067 0.0068 0.0067 0.0060 0.0059 0.0060 0.0060
visits (0.0013)*** (0.0013)*** (0.0013)*** (0.0013)*** (0.0012)*** (0.0012)*** (0.0013)*** (0.0013)***Log inpatient days x Log outpatient visits 0.0007 0.0006 â0.0006 â0.0006 0.0073 0.0069 0.0078 0.0078
(0.0038) (0.0038) (0.0041) (0.0041) (0.0090) (0.0090) (0.0090) (0.0090)Log total hospital beds 0.0779 0.0772 0.0865 0.0859 0.0998 0.1006 0.1097 0.1102 (0.0140)*** (0.0140)*** (0.0139)*** (0.0139)*** (0.0230)*** (0.0230)*** (0.0212)*** (0.0212)***Independent practice association hospital
0.0087 0.0088 0.0084 0.0084 0.0072 0.0085 0.0079 0.0090(0.0060) (0.0060) (0.0060) (0.0060) (0.0079) (0.0080) (0.0079) (0.0081)
Management service organization hospital
0.0144 0.0140 0.0150 0.0140 0.0134 0.0124 0.0130 0.0116(0.0065)** (0.0065)** (0.0065)** (0.0065)** (0.0084) (0.0084) (0.0084) (0.0084)
Equity model hospital
â0.0136 â0.0140 â0.0130 â0.0132 â0.0302 â0.0312 â0.0301 â0.0306(0.0149) (0.0149) (0.0146) (0.0148) (0.0218) (0.0218) (0.0214) (0.0216)
Foundation hospital â0.0341 â0.0338 â0.0338 â0.0339 â0.0506 â0.0507 â0.0511 â0.0510(0.0137)** (0.0137)** (0.0139)** (0.0138)** (0.0187)*** (0.0185)*** (0.0187)*** (0.0184)***
Log admissions 0.1737 0.1739 0.1838 0.1836 0.2076 0.2079 0.2262 0.2247(0.0201)*** (0.0201)*** (0.0212)*** (0.0212)*** (0.0443)*** (0.0440)*** (0.0434)*** (0.0431)***
Births (000s) 0.0081 0.0081 0.0054 0.0051 â0.0022 â0.0021 â0.0025 â0.0025(0.0068) (0.0068) (0.0068) (0.0068) (0.0119) (0.0119) (0.0120) (0.0120)
Full time physicians and dentists (000s) â0.1631 â0.1620 â0.1550 â0.1525 â0.0629 â0.0652 â0.0645 â0.0651(0.0939)* (0.0938)* (0.0943) (0.0942) (0.2661) (0.2622) (0.2663) (0.2628)
Percent births 0.1857 0.1857 0.1824 0.1830 0.1628 0.1570 0.1078 0.1020(0.0742)** (0.0741)** (0.0746)** (0.0745)** (0.1853) (0.1853) (0.1762) (0.1761)
Forâprofit ownership â0.0161 â0.0154 â0.0153 â0.0139 â0.0537 â0.0530 â0.0496 â0.0469(0.0213) (0.0213) (0.0212) (0.0212) (0.0300)* (0.0298)* (0.0299)* (0.0296)
Nonâsecular nonprofit ownership 0.0199 0.0194 0.0178 0.0172 0.0063 0.0041 0.0043 0.0030(0.0159) (0.0159) (0.0158) (0.0159) (0.0230) (0.0227) (0.0234) (0.0229)
Nonâprofit church ownership 0.0345 0.0345 0.0316 0.0321 â0.0024 â0.0039 â0.0041 â0.0038(0.0233) (0.0232) (0.0232) (0.0232) (0.0296) (0.0293) (0.0298) (0.0294)
Log number of discharges Medicare 0.0782 0.0784 0.0747 0.0750 0.1078 0.1084 0.1000 0.1006(0.0140)*** (0.0140)*** (0.0140)*** (0.0140)*** (0.0210)*** (0.0210)*** (0.0204)*** (0.0203)***
Log number of discharges Medicaid 0.0032 0.0032 0.0025 0.0026 0.0078 0.0081 0.0073 0.0077(0.0019) (0.0019)* (0.0019) (0.0019) (0.0028)*** (0.0029)*** (0.0028)** (0.0028)***
Log number of discharges total 0.0219 0.0216 0.0294 0.0288 0.0543 0.0529 0.0608 0.0591(0.0124)* (0.0124)* (0.0125)** (0.0125)** (0.0191)*** (0.0191)*** (0.0187)*** (0.0186)***
Residency or Member of Council Teaching Hospitals
â0.0010 â0.0008 0.0004 0.0002 â0.0012 â0.0009 â0.0009 â0.0006(0.0101) (0.0101) (0.0100) (0.0099) (0.0120) (0.0120) (0.0119) (0.0119)
Vertically integrated with doctors 0.0031 0.0031 0.0037 0.0034 0.0071 0.0074 0.0069 0.0067(0.0060) (0.0060) (0.0060) (0.0060) (0.0082) (0.0083) (0.0082) (0.0082)
Vert. integ. w drs (excl. integrated salary model)
0.0023 0.0019 0.0018 0.0021 â0.0036 â0.0050 â0.0039 â0.0039(0.0072) (0.0072) (0.0073) (0.0073) (0.0097) (0.0098) (0.0097) (0.0098)
FT physicians / total hospital beds 0.1870 0.1861 0.1810 0.1804 0.1945 0.1944 0.1963 0.1959(0.0475)*** (0.0473)*** (0.0477)*** (0.0476)*** (0.1222) (0.1214) (0.1221) (0.1213)
Year 1998 â0.0907 â0.0905 â0.0869 â0.0835 0.0210 0.0235 0.0082 0.0094(0.0645) (0.0644) (0.0646) (0.0646) (0.0842) (0.0849) (0.0844) (0.0853)
Year 2000 â0.1957 â0.1965 â0.1876 â0.1820 0.0330 0.0369 0.0074 0.0087(0.1284) (0.1282) (0.1286) (0.1286) (0.1680) (0.1693) (0.1683) (0.1701)
Year 2002 â0.2650 â0.2663 â0.2531 â0.2446 0.0746 0.0804 0.0360 0.0382(0.1922) (0.1920) (0.1925) (0.1925) (0.2514) (0.2533) (0.2518) (0.2545)
Year 2004 â0.3591 â0.3616 â0.3424 â0.3318 0.1079 0.1140 0.0564 0.0580(0.2563) (0.2560) (0.2566) (0.2567) (0.3351) (0.3377) (0.3357) (0.3393)
Year 2006 â0.4297 â0.4329 â0.4092 â0.3960 0.1298 0.1363 0.0663 0.0671(0.3204) (0.3200) (0.3208) (0.3210) (0.4190) (0.4223) (0.4198) (0.4243)
Year 2008 â0.5306 â0.5349 â0.5089 â0.4939 0.1196 0.1270 0.0428 0.0432(0.3845) (0.3840) (0.3850) (0.3851) (0.5029) (0.5068) (0.5039) (0.5092)
MSA dummy x year â0.0015 â0.0015 â0.0017 â0.0017 â0.0036 â0.0036 â0.0037 â0.0037(0.0015) (0.0015) (0.0015) (0.0015) (0.0019)* (0.0019)* (0.0019)* (0.0019)*
Log population in 2000 census x year â0.0023 â0.0023 â0.0022 â0.0022 â0.0024 â0.0024 â0.0019 â0.0019(0.0006)*** (0.0006)*** (0.0006)*** (0.0006)*** (0.0008)*** (0.0008)*** (0.0009)** (0.0009)**
% Hispanic in 2000 census x year
0.0008 0.0011 â0.0043 â0.0040 0.0035 0.0034 â0.0028 â0.0032(0.0046) (0.0046) (0.0047) (0.0047) (0.0066) (0.0066) (0.0068) (0.0068)
% Black in 2000 census x year
â0.0133 â0.0131 â0.0134 â0.0132 â0.0114 â0.0110 â0.0115 â0.0110(0.0037)*** (0.0037)*** (0.0037)*** (0.0037)*** (0.0053)** (0.0053)** (0.0053)** (0.0053)**
% age 65+ in 2000 census x year â0.0267 â0.0270 â0.0302 â0.0316 â0.0330 â0.0342 â0.0359 â0.0359(0.0165) (0.0164)* (0.0166)* (0.0165)* (0.0216) (0.0215) (0.0214)* (0.0214)*
% age 25â64 in 2000 census x year â0.0441 â0.0446 â0.0502 â0.0504 â0.0182 â0.0197 â0.0242 â0.0263(0.0122)*** (0.0121)*** (0.0123)*** (0.0123)*** (0.0175) (0.0174) (0.0181) (0.0180)
% university education in 2000 census x year
0.0150 0.0140 0.0167 0.0160 0.0281 0.0265 0.0344 0.0333(0.0121) (0.0121) (0.0125) (0.0124) (0.0165)* (0.0165) (0.0168)** (0.0167)**
Log median home value in 2000 census x year
0.0089 0.0088 0.0091 0.0091 0.0055 0.0057 0.0055 0.0058(0.0024)*** (0.0024)*** (0.0024)*** (0.0024)*** (0.0034) (0.0035)* (0.0035) (0.0035)*
Log median hh income in 2000 census x year
0.0069 0.0071 0.0069 0.0068 0.0032 0.0030 0.0037 0.0036(0.0041)* (0.0041)* (0.0041)* (0.0041)* (0.0055) (0.0056) (0.0055) (0.0055)
Constant 16.1177 16.1056 16.0037 16.0014 16.1416 16.1301 16.0140 16.0029 (0.3650)*** (0.3655)*** (0.4045)*** (0.4037)*** (0.6764)*** (0.6751)*** (0.6823)*** (0.6778)***Unit of observation is a hospitalâyear. Data biannually from 1996 to 2008. Regressions include hospitalâspecific fixed effects, differenced out at means. Dependent variable is logged total costs
Appendix Table 5: Missing EMR set to one. Also includes missing controls and adds a dummy for âmissing dataâ
(1) (2) (3) (4) (5) (6) (7) (8)
Technology Basic EMRadoption
Advanced EMR adoption
Basic EMRadoption
Advanced EMR adoption
Basic EMRadoption
Advanced EMR adoption
Basic EMRadoption
Advanced EMR adoption
Adopt in previous 2 year period 0.0278 0.0374 0.0350 0.0494 0.0140 0.0485 0.0204 0.0643(0.0062)*** (0.0073)*** (0.0086)*** (0.0119)*** (0.0090) (0.0112)*** (0.0113)* (0.0161)***
Adopt at least 2 years earlier 0.0088 0.0200 0.0335 0.0445 â0.0064 0.0338 0.0189 0.0641(0.0072) (0.0090)** (0.0099)*** (0.0140)*** (0.0103) (0.0147)** (0.0142) (0.0213)***
Adopt in previous 2 year period x IT intensive county
â0.0151 â0.0223 â0.0094 â0.0284(0.0125) (0.0148) (0.0158) (0.0200)
Adopt at least 2 years earlier x IT intensive county
â0.0511 â0.0449 â0.0459 â0.0552(0.0143)*** (0.0181)** (0.0187)** (0.0255)**
Adopt in previous 2 year period x Programmer 1996
â0.0034 â0.0399 â0.0023 â0.0345(0.0177) (0.0196)** (0.0179) (0.0200)*
Adopt at least 2 years earlier x Programmer 1996
â0.0237 â0.0319 â0.0155 â0.0188(0.0253) (0.0278) (0.0249) (0.0274)
Observations 22369 22369 21737 21737 9594 9594 9495 9495# of hospitals 4277 4277 4100 4100 1549 1549 1532 1532Râsquared 0.76 0.76 0.76 0.76 0.79 0.79 0.79 0.79
CONTROLS Missing discharge data 0.8330 0.8321 0.8692 0.8675 1.4447 1.4392 1.4368 1.4288
(0.0569)*** (0.0569)*** (0.0591)*** (0.0590)*** (0.1406)*** (0.1399)*** (0.1402)*** (0.1392)***Missing hospital type data â0.0113 â0.0107 â0.0103 â0.0103 â0.0265 â0.0251 â0.0250 â0.0247
(0.0068)* (0.0068) (0.0068) (0.0068) (0.0095)*** (0.0095)*** (0.0095)*** (0.0094)***Top quartile IT using county x year 0.0011 0.0001 â0.0010 â0.0018
(0.0013) (0.0012) (0.0017) (0.0016)Programmer in 1996 x year 0.0031 0.0029 0.0027 0.0025
(0.0019) (0.0018) (0.0020) (0.0018)Log inpatient days â0.3299 â0.3298 â0.3431 â0.3444 â0.3338 â0.3364 â0.3347 â0.3320
(0.0528)*** (0.0528)*** (0.0588)*** (0.0588)*** (0.1618)** (0.1613)** (0.1596)** (0.1589)**Log outpatient visits â0.0948 â0.0932 â0.0834 â0.0821 â0.1544 â0.1495 â0.1616 â0.1608
(0.0389)** (0.0390)** (0.0421)** (0.0420)* (0.1054) (0.1049) (0.1052) (0.1046)Log inpatient days x Log inpatient days 0.0192 0.0192 0.0205 0.0206 0.0145 0.0147 0.0140 0.0139
(0.0030)*** (0.0030)*** (0.0031)*** (0.0031)*** (0.0109) (0.0109) (0.0106) (0.0106)Log outpatient visits x Log outpatient 0.0068 0.0067 0.0068 0.0067 0.0060 0.0059 0.0060 0.0060
visits (0.0013)*** (0.0013)*** (0.0013)*** (0.0013)*** (0.0012)*** (0.0012)*** (0.0013)*** (0.0013)***Log inpatient days x Log outpatient visits 0.0007 0.0006 â0.0006 â0.0006 0.0073 0.0069 0.0078 0.0078
(0.0038) (0.0038) (0.0041) (0.0041) (0.0090) (0.0090) (0.0090) (0.0090)Log total hospital beds 0.0779 0.0772 0.0865 0.0859 0.0998 0.1006 0.1097 0.1102 (0.0140)*** (0.0140)*** (0.0139)*** (0.0139)*** (0.0230)*** (0.0230)*** (0.0212)*** (0.0212)***Independent practice association hospital
0.0087 0.0088 0.0084 0.0084 0.0072 0.0085 0.0079 0.0090(0.0060) (0.0060) (0.0060) (0.0060) (0.0079) (0.0080) (0.0079) (0.0081)
Management service organization hospital
0.0144 0.0140 0.0150 0.0140 0.0134 0.0124 0.0130 0.0116(0.0065)** (0.0065)** (0.0065)** (0.0065)** (0.0084) (0.0084) (0.0084) (0.0084)
Equity model hospital
â0.0136 â0.0140 â0.0130 â0.0132 â0.0302 â0.0312 â0.0301 â0.0306(0.0149) (0.0149) (0.0146) (0.0148) (0.0218) (0.0218) (0.0214) (0.0216)
Foundation hospital â0.0341 â0.0338 â0.0338 â0.0339 â0.0506 â0.0507 â0.0511 â0.0510(0.0137)** (0.0137)** (0.0139)** (0.0138)** (0.0187)*** (0.0185)*** (0.0187)*** (0.0184)***
Log admissions 0.1737 0.1739 0.1838 0.1836 0.2076 0.2079 0.2262 0.2247(0.0201)*** (0.0201)*** (0.0212)*** (0.0212)*** (0.0443)*** (0.0440)*** (0.0434)*** (0.0431)***
Births (000s) 0.0081 0.0081 0.0054 0.0051 â0.0022 â0.0021 â0.0025 â0.0025(0.0068) (0.0068) (0.0068) (0.0068) (0.0119) (0.0119) (0.0120) (0.0120)
Full time physicians and dentists (000s) â0.1631 â0.1620 â0.1550 â0.1525 â0.0629 â0.0652 â0.0645 â0.0651(0.0939)* (0.0938)* (0.0943) (0.0942) (0.2661) (0.2622) (0.2663) (0.2628)
Percent births 0.1857 0.1857 0.1824 0.1830 0.1628 0.1570 0.1078 0.1020(0.0742)** (0.0741)** (0.0746)** (0.0745)** (0.1853) (0.1853) (0.1762) (0.1761)
Forâprofit ownership â0.0161 â0.0154 â0.0153 â0.0139 â0.0537 â0.0530 â0.0496 â0.0469(0.0213) (0.0213) (0.0212) (0.0212) (0.0300)* (0.0298)* (0.0299)* (0.0296)
Nonâsecular nonprofit ownership 0.0199 0.0194 0.0178 0.0172 0.0063 0.0041 0.0043 0.0030(0.0159) (0.0159) (0.0158) (0.0159) (0.0230) (0.0227) (0.0234) (0.0229)
Nonâprofit church ownership 0.0345 0.0345 0.0316 0.0321 â0.0024 â0.0039 â0.0041 â0.0038(0.0233) (0.0232) (0.0232) (0.0232) (0.0296) (0.0293) (0.0298) (0.0294)
Log number of discharges Medicare 0.0782 0.0784 0.0747 0.0750 0.1078 0.1084 0.1000 0.1006(0.0140)*** (0.0140)*** (0.0140)*** (0.0140)*** (0.0210)*** (0.0210)*** (0.0204)*** (0.0203)***
Log number of discharges Medicaid 0.0032 0.0032 0.0025 0.0026 0.0078 0.0081 0.0073 0.0077(0.0019) (0.0019)* (0.0019) (0.0019) (0.0028)*** (0.0029)*** (0.0028)** (0.0028)***
Log number of discharges total 0.0219 0.0216 0.0294 0.0288 0.0543 0.0529 0.0608 0.0591(0.0124)* (0.0124)* (0.0125)** (0.0125)** (0.0191)*** (0.0191)*** (0.0187)*** (0.0186)***
Residency or Member of Council Teaching Hospitals
â0.0010 â0.0008 0.0004 0.0002 â0.0012 â0.0009 â0.0009 â0.0006(0.0101) (0.0101) (0.0100) (0.0099) (0.0120) (0.0120) (0.0119) (0.0119)
Vertically integrated with doctors 0.0031 0.0031 0.0037 0.0034 0.0071 0.0074 0.0069 0.0067(0.0060) (0.0060) (0.0060) (0.0060) (0.0082) (0.0083) (0.0082) (0.0082)
Vert. integ. w drs (excl. integrated salary model)
0.0023 0.0019 0.0018 0.0021 â0.0036 â0.0050 â0.0039 â0.0039(0.0072) (0.0072) (0.0073) (0.0073) (0.0097) (0.0098) (0.0097) (0.0098)
FT physicians / total hospital beds 0.1870 0.1861 0.1810 0.1804 0.1945 0.1944 0.1963 0.1959(0.0475)*** (0.0473)*** (0.0477)*** (0.0476)*** (0.1222) (0.1214) (0.1221) (0.1213)
Year 1998 â0.0907 â0.0905 â0.0869 â0.0835 0.0210 0.0235 0.0082 0.0094(0.0645) (0.0644) (0.0646) (0.0646) (0.0842) (0.0849) (0.0844) (0.0853)
Year 2000 â0.1957 â0.1965 â0.1876 â0.1820 0.0330 0.0369 0.0074 0.0087(0.1284) (0.1282) (0.1286) (0.1286) (0.1680) (0.1693) (0.1683) (0.1701)
Year 2002 â0.2650 â0.2663 â0.2531 â0.2446 0.0746 0.0804 0.0360 0.0382(0.1922) (0.1920) (0.1925) (0.1925) (0.2514) (0.2533) (0.2518) (0.2545)
Year 2004 â0.3591 â0.3616 â0.3424 â0.3318 0.1079 0.1140 0.0564 0.0580(0.2563) (0.2560) (0.2566) (0.2567) (0.3351) (0.3377) (0.3357) (0.3393)
Year 2006 â0.4297 â0.4329 â0.4092 â0.3960 0.1298 0.1363 0.0663 0.0671(0.3204) (0.3200) (0.3208) (0.3210) (0.4190) (0.4223) (0.4198) (0.4243)
Year 2008 â0.5306 â0.5349 â0.5089 â0.4939 0.1196 0.1270 0.0428 0.0432(0.3845) (0.3840) (0.3850) (0.3851) (0.5029) (0.5068) (0.5039) (0.5092)
MSA dummy x year â0.0015 â0.0015 â0.0017 â0.0017 â0.0036 â0.0036 â0.0037 â0.0037(0.0015) (0.0015) (0.0015) (0.0015) (0.0019)* (0.0019)* (0.0019)* (0.0019)*
Log population in 2000 census x year â0.0023 â0.0023 â0.0022 â0.0022 â0.0024 â0.0024 â0.0019 â0.0019(0.0006)*** (0.0006)*** (0.0006)*** (0.0006)*** (0.0008)*** (0.0008)*** (0.0009)** (0.0009)**
% Hispanic in 2000 census x year
0.0008 0.0011 â0.0043 â0.0040 0.0035 0.0034 â0.0028 â0.0032(0.0046) (0.0046) (0.0047) (0.0047) (0.0066) (0.0066) (0.0068) (0.0068)
% Black in 2000 census x year
â0.0133 â0.0131 â0.0134 â0.0132 â0.0114 â0.0110 â0.0115 â0.0110(0.0037)*** (0.0037)*** (0.0037)*** (0.0037)*** (0.0053)** (0.0053)** (0.0053)** (0.0053)**
% age 65+ in 2000 census x year â0.0267 â0.0270 â0.0302 â0.0316 â0.0330 â0.0342 â0.0359 â0.0359(0.0165) (0.0164)* (0.0166)* (0.0165)* (0.0216) (0.0215) (0.0214)* (0.0214)*
% age 25â64 in 2000 census x year â0.0441 â0.0446 â0.0502 â0.0504 â0.0182 â0.0197 â0.0242 â0.0263(0.0122)*** (0.0121)*** (0.0123)*** (0.0123)*** (0.0175) (0.0174) (0.0181) (0.0180)
% university education in 2000 census x year
0.0150 0.0140 0.0167 0.0160 0.0281 0.0265 0.0344 0.0333(0.0121) (0.0121) (0.0125) (0.0124) (0.0165)* (0.0165) (0.0168)** (0.0167)**
Log median home value in 2000 census x year
0.0089 0.0088 0.0091 0.0091 0.0055 0.0057 0.0055 0.0058(0.0024)*** (0.0024)*** (0.0024)*** (0.0024)*** (0.0034) (0.0035)* (0.0035) (0.0035)*
Log median hh income in 2000 census x year
0.0069 0.0071 0.0069 0.0068 0.0032 0.0030 0.0037 0.0036(0.0041)* (0.0041)* (0.0041)* (0.0041)* (0.0055) (0.0056) (0.0055) (0.0055)
Constant 16.1006 16.0927 16.0026 15.9953 16.1506 16.1276 16.0296 16.0049 (0.3651)*** (0.3658)*** (0.4047)*** (0.4039)*** (0.6760)*** (0.6750)*** (0.6821)*** (0.6777)***Unit of observation is a hospitalâyear. Data biannually from 1996 to 2008. Regressions include hospitalâspecific fixed effects, differenced out at means. Dependent variable is logged total costs
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
All locations NonâIT intensive locations
ITâIntensive locations
No programmers in 1996
Has programmers in 1996
Figure 1a: % rise in costs from 2 years earlier, by timing of basic EMR adoption
2 years before adopt year adopt 2 years after
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
All locations NonâIT intensive locations
ITâIntensive locations
No programmers in 1996
Has programmers in 1996
Figure 1b: % rise in costs from 2 years earlier, by timing of advanced EMR adoption
2 years before adopt year adopt 2 years after
Error bars show 95% confidence intervals. Full set of coefficients in Appendix Table 1
â0.04
â0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
6 years before adoption
4 years before adoption
2 years before adoption
Adoption period
2 years after adoption
4 years after adoption
6 or more years after adoption
Figure 2a: Coefficients by years from adoption
Basic EMR Advanced EMR
â0.15
â0.05
0.05
0.15
0.25
6 years before adoption
4 years before adoption
2 years before adoption
Adoption period
2 years after adoption
4 years after adoption
6 or more years after adoption
Figure 2b: Coefficients by years from Basic EMR adoption
non IT intensive location IT intensive location
â0.1
â0.05
0
0.05
0.1
0.15
6 years before adoption
4 years before adoption
2 years before adoption
Adoption period
2 years after adoption
4 years after adoption
6 or more years after adoption
Figure 2c: Coefficients by years from Advanced EMR adoption
non IT intensive location IT intensive location