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The Effects of Health Information Technology Adoption and Hospital-Physician Integration on Hospital Efficiency
Na-Eun Cho, PhD; Jongwha Chang, PhD; and Bebonchu Atems, PhD

The Effects of Health Information Technology Adoption and Hospital-Physician Integration on Hospital Efficiency

Na-Eun Cho, PhD; Jongwha Chang, PhD; and Bebonchu Atems, PhD
Determining the impact of health information technology adoption and hospital-physician integration on hospital efficiency.
To determine the impact of health information technology (HIT) adoption and hospital-physician integration on hospital efficiency.

Study Design
Using 2010 data from the American Hospital Association's (AHA) annual survey, the AHA IT survey, supplemented by the CMS Case Mix Index, and the US Census Bureau's small area income and poverty estimates, we examined how the adoption of HIT and employment of physicians affected hospital efficiency and whether they were substitutes or complements.

The sample included 2173 hospitals. We employed a 2-stage approach. In the first stage, data envelopment analysis was used to estimate technical efficiency of hospitals. In the second stage, we used instrumental variable approaches, notably 2-stage least squares and the generalized method of moments, to examine the effects of IT adoption and integration on hospital efficiency.

We found that HIT adoption and hospital-physician integration, when considered separately, each have statistically significant positive impacts on hospital efficiency. Also, we found that hospitals that adopted HIT with employed physicians will achieve less efficiency compared with hospitals that adopted HIT without employed physicians.

Although HIT adoption and hospital-physician integration both seem to be key parts of improving hospital efficiency when one or the other is utilized individually, they can hurt hospital efficiency when utilized together.

Am J Manag Care. 2014;20(11 Spec No. 17):eSP9-eSP15
The US government has made significant efforts toward both increased health information technology (HIT) adoption and hospital-physician integration as solutions to reduce healthcare costs while simultaneously improving the quality of care.
  • Our findings suggest that both HIT adoption and hospital-physician integration have positive effects on hospital efficiency.
  • However, our work suggests that the HIT adoption and the hospital-physician integration are substitutes.
  • The US government may want to pay more attention to this substitute relationship, as hospitals using both together are less efficient.
Policy makers and academics have long tried to reduce healthcare costs while simultaneously improving the quality of care. Recently, they have been particularly interested in utilizing health information technology (HIT) and strengthening hospital-physician integration. In an effort to enhance HIT infrastructure, the American Recovery and Reinvestment Act of 2009 (ARRA) set aside approximately $20 billion in stimulus funds to encourage physicians and hospitals to adopt HIT systems, which are expected to reduce medication errors and enhance monitoring.1,2 Furthermore, the US government and researchers have also been focusing on hospital-physician relationships.3,4 With the recognition that independent hospital-physician arrangements can lead to inefficiencies, the Patient Protection and Affordable Care Act of 2010 (ACA) emphasized the importance of this integration of healthcare delivery. Thus, physicians, who have traditionally worked independently with hospital systems, are increasingly being encouraged to find employment within a hospital system, or to work under other forms of contractual arrangements to try to reduce inefficiencies and improve the quality of care.

While the government’s efforts have increased the adoption of HIT systems and accelerated the growth of hospital employment of physicians, it is unclear how integration and HIT adoption interact to affect outcomes, such as hospital efficiency. Previous researchers have found that the probability of HIT adoption is positively associated with hospital-physician integration, positing that the 2 are complementary.5,6 To achieve the ultimate goal of reducing costs and increasing efficiency, however, it is important to investigate how both HIT adoption and hospital-physician integration affect outcomes, and if and how they interact to affect those outcomes.5

If HIT adoption and hospital-physician integration complement each other, finding the optimal level of HIT utilization, given the specific type of physician-hospital arrangement (eg, employment of physicians or other contractual relationships) that can lead to the best outcomes, should become the next step for both policy makers and academics. However, if HIT adoption is a substitute for hospital-physician integration, then the government’s efforts encouraging both HIT adoption and integrated systems warrant reconsideration. Despite its potential for important implication, the interaction between HIT and hospital-physician integration has received little attention in the literature.

Carefully considering its policy implications, we investigated the effects of HIT and hospital-physician integration on hospital efficiency. First, we used data envelopment analysis (DEA) to calculate hospital efficiency. Second, we examined the main effects of the adoption of the electronic medical record (EMR)—which is one of the prominent HIT applications—hospital employment of physicians, and the interaction effects of both on hospital efficiency.


Data Source

We used data from the American Hospital Association's (AHA) annual survey, the AHA Information Technology (AHA-IT) survey, the CMS Case Mix Index (CMI), and the US Census Bureau's small area income and poverty estimates for 2010. The AHA annual survey provides information on hospital characteristics, including number of beds, service mix, nonphysician full-time equivalent (FTE) employees, nonlabor expense, total facility admissions, outpatient visits, hospital employment of physicians, hospital ownership, and urbanness. The AHA-IT survey notes whether a hospital uses an EMR system and if it electronically shares patient data with hospitals or ambulatory providers inside and/or outside its system. Lastly, we obtained the case mix data from the CMS CMI and county-level median household income data from the Census Bureau. After merging these 4 data sets, the sample consisted of 2173 hospitals observed in 2010. The sample represents 67% of 3233 hospitals that participated in the AHA-HIT survey.


We employed a 2-stage approach to investigate the impact of HIT and hospital-physician integration on hospital efficiency. In the first-stage, DEA was used to estimate technical efficiency for the 2173 hospitals. Following previous studies,7,8 we estimated technical efficiency using 4 input measures (number of beds, service mix, FTE employees, and non-labor expenses) and 2 output measures (case-mix adjusted admissions and outpatient visits). Service mix is a measure of the diversity of diagnostic and special services provided by each hospital. Using the same formulation that was created by Dr Ozcan,8 we obtained the service mix input.

In the second stage, we used instrumental variable (IV) approaches, notably 2-stage least squares (2SLS) and the generalized method of moments (GMM), to examine the effects of HIT adoption and integration on hospital efficiency. This 2-stage approach of first estimating efficiency scores and then regressing these scores on several explanatory variables was influenced by prior research. Ray (1991) regressed DEA scores on various social and economic variables to identify performance drivers in school districts.9 Banker et al evaluated the impact of HIT investment on public accounting firm productivity.10 Forsund and Sarafoglou noted that the 2-stage DEA approach has been used for over 20 years.11 Banker and Natarajan provided theoretical justification for the application of the 2-stage models to evaluate contextual variables that affect DEA efficiency scores.12 Table 1 shows descriptive statistics of efficiency scores, input and output measures, and independent variables such as HIT adoption and hospital-physician integration.

First Stage: DEA and Hospital Efficiency

DEA is a linear programming approach for constructing a nonparametric piecewise linear production frontier using observed hospital-level input and output data. DEA allows for the consideration of multiple inputs and outputs simultaneously and makes very few a priori assumptions about the underlying technology of the production frontier, thus rendering DEA less susceptible to specification error. Only a brief discussion of the relevant aspects of DEA is presented in the current paper; for detailed technical descriptions of DEA, we refer the reader to a variety of papers.13-16

In DEA, the efficiency of a hospital is typically estimated using either an input-oriented or output-oriented approach. The input-oriented approach assumes that the hospital holds output constant, while decreasing the quantity of inputs used to produce the given level of output. The output-oriented approach maximizes output while holding the quantity of inputs constant.17 In this paper, we adopt the input-oriented approach since many hospitals tend to face resource constraints such that incentives are often oriented toward minimizing inputs, such as length of stay.

Another important aspect to consider when estimating measures of technical efficiency using DEA is whether to apply constant or variable returns to scale technology. Many studies apply constant returns to scale technology; however, as pointed out by Hollingsworth and Smith,18 when the data are ratio data, variable returns to scale technology should be used. Since our inputs and outputs are ratio data, we assume variable returns to scale technology. It should be noted that the underlying results of this paper do not change when efficiency measures are computed assuming constant returns to scale technology. Those results are available upon request.

After determining the returns to scale technology, the input-oriented efficiency scores are calculated by radically decreasing the quantity of inputs relative to the frontier technology, while holding output constant. Only inputs and outputs are needed to obtain the efficiency ratios since the Farrell measure is independent of prices.19 The technical efficiency scores lie between 0 and 1. A score of 1 indicates a technically efficient hospital—while a score less than 1 indicates inefficiency—and that it is possible to produce the observed output level using fewer inputs.

Second Stage: Instrumental Variables Estimation

While DEA is an appropriate framework for calculating efficiency scores to determine which hospitals are more efficient than others, it does not provide a framework for analyzing the underlying determinants of differences in hospital efficiency. As a consequence, a few recent studies have employed a 2-stage approach to examine the determinants of hospital efficiency.20 In these studies, efficiency scores are calculated as described in the previous subsection, and then ordinary least squares (OLS) estimation is employed in the second stage. OLS estimation, however, has several limitations. First, OLS estimates cannot be interpreted as causal effects; they are estimates of partial correlations. Second, if the explanatory variables in the OLS regression are not exogenous, OLS estimation results in biased and inconsistent parameter estimates. To avoid these problems, we use IV approaches to estimate the effect of HIT and hospital integration on hospital efficiency in the second stage of our analysis. As pointed out by Imbens and Angrist,21 the estimated coefficients from IV methods can be interpreted as causal effects in the absence of controlled experiments or when a treatment is not successfully delivered to every unit in an experiment. In particular, estimation is done using 2SLS. 2SLS, however, can sometimes be inefficient in certain cases, such as when heteroskedasticity is present in the regression residuals. Consequently, we also apply the GMM procedure in order to get more efficient estimations.

The baseline regression model we estimate has the estimated measure of technical efficiency for hospital i, EFFi, as the dependent variable. The key explanatory variables are ITi, a measure of HIT adoption by hospital i; INTi, a measure of hospital-physician integration; IT INTi, an interaction term between ITi and INTi that captures the simultaneous effect of HIT and integration on the efficiency  of hospital i; and GOVi, a binary indicator that takes a value of 1 if hospital i is a government hospital, and 0 otherwise.

EFFi = β1 + β2ITi + β3INTi + β4IT INTi + β5GOVi + u i

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