Jongwha Chang, PhD

To determine the impact of health information technology (HIT) adoption and hospital-physician integration on hospital efficiency.

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.

 Furthermore, the US government and researchers have also been focusing on hospital-physician relationships.

 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.

 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.

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&mdash;hospital employment of physicians, and the interaction effects of both on hospital efficiency.

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,

 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,

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 ï¬rst 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.

 Banker et al evaluated the impact of HIT investment on public accounting ï¬rm productivity.

Forsund and Sarafoglou noted that the 2-stage DEA approach has been used for over 20 years.

Banker and Natarajan provided theoretical justiï¬cation for the application of the 2-stage models to evaluate contextual variables that affect DEA eï¬ƒciency scores.

 shows descriptive statistics of efficiency scores, input and output measures, and independent variables such as HIT adoption and hospital-physician integration.

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 speciï¬cation 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.

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.

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,

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.

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&mdash;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.

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,

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 

, as the dependent variable. The key explanatory variables are IT

, a measure of hospital-physician integration; IT INT

 that captures the simultaneous effect of HIT and integration on the efficiency  of hospital 

, a binary indicator that takes a value of 1 if hospital 

 is a government hospital, and 0 otherwise.

 is a binary indicator equal to 1 if a hospital has adopted an EMR, one of the prominent HIT applications, and 0 otherwise. This variable represents the broader construct of HIT adoption, which is designed to help healthcare providers better manage patient care through exchanging and sharing health information electronically. IT

is expected to be positively related to hospital efficiency, as several studies have found that HIT improves efficiency by reducing redundant diagnostic tests and other medication errors.

 is a binary variable that shows hospital employment of physicians. The value of INT

 is 1 if a hospital has adopted arrangements under which physicians become employees of the hospital, and 0 otherwise. Following Lammers,

 we have restricted the definition of hospital-physician integration to hospital employment of physicians because it is the tightest form of integration between hospitals and physicians and the most common form of integration reported in the AHA annual survey. Compared with other, looser hospital-physician arrangements—such as independent practice associations, open physician-hospital organizations, closed physician-hospital organizations, and management service organizations&mdash;hospital employment of physicians is expected to improve hospital efficiency as hospitals can use hierarchy and fiat in allocating resources without the need for negotiation.

Although we hypothesize that both ITi and INTi are positively related to hospital efficiency, it is unclear how the 2 will interact to affect the efficiency of hospitals. Since the underlying mechanisms behind HIT adoption and employment of physicians are increased control and monitoring of physician behavior, the 2 may work as substitutes. Conversely, utilization of HIT and employment of physicians may complement each other and further enhance efficiency. Consequently, we create an interaction variable between IT

) to capture the simultaneous effect of the 2 variables.

The OLS, 2SLS, and GMM estimates are given in 

. Heteroskedasticity-robust standard errors are reported in parentheses. Table 2 shows the results when IT

 is measured as an indicator variable equal to 1 if a hospital has adopted an EMR, and 0 otherwise. The first column of the table shows the OLS estimates. Neither IT

 are statistically significant at any conventional level of significance, while IT

 are statistically significant at the 5% level.

As mentioned before, a concern with the OLS regressions is that the IT

 variables may be correlated with unobserved factors that can also affect the technical efficiency of hospitals. As a result, the OLS estimator is generally biased and inconsistent for the causal effects of IT

 on hospital efficiency. A standard solution to this endogeneity problem is to find an instrumental variable for IT

A good instrument is one that is correlated with the key explanatory variables but not with the dependent variable. Extant literature has shown that hospitals in urban centers are more likely to adopt HIT than those in rural areas.

 Also, several studies suggest that hospitals in high-income areas are more likely to adopt HIT than those in low-income areas.

 Consequently, in addition to all the explanatory variables, we include the degree of urbanization of the county in which the hospital is located (urban) and county-level median household income (medincome) as instruments. If we used only urban and medincome as instrumental variables, the regression would just be identified, and there would be no difference between 2SLS and GMM because when the number of instruments is equal to the number of regressors, all GMM estimators reduce to the standard IV estimator.

Columns 2 and 3 of Table 2 show the 2SLS and GMM results, respectively. As the table shows, the estimates using 2SLS and GMM are qualitatively and quantitatively similar, and they are noticeably higher than the OLS estimates on IT

. As previous literature suggests, we find that HIT adoption and hospital-physician integration each have statistically significant positive effects on hospital efficiency. Interestingly, the interaction term is negative and significant, indicating that hospitals that electronically exchange patient data with other hospitals or ambulatory providers inside and/or outside its system, while simultaneously employing physician groups, are less efficient. As expected, we find a significant negative association between government hospitals and efficiency scores.

As a robustness check, we use an alternative measure of HIT to verify how sensitive the signs and significance of estimates in columns 2 and 3 of Table 2 are to the choice of the HIT measure. Thus, we use as an alternative measure of HIT, the level of HIT utilization. This measure shows how broadly the hospital electronically exchanges patient data with hospitals or ambulatory providers inside and/or outside its system. Looking at column 1 of Table 3, the OLS estimates are similar to those in Table 2. Importantly, columns 2 and 3 of Table 3 show that the signs and significance of the baseline results remain unchanged, indicating that the results in Table 2 are robust to the choice of the measure of HIT. With the high 

 values, it is also worth noting that the tests of over identifying restrictions indicate that the instruments in the 2SLS and GMM estimations are valid.

Despite governmental efforts to promote the adoption of HIT and strengthen hospital-physician integration, we know little about the effects of HIT adoption and hospital-physician integration on hospital efficiency.

Using data from the 2010 AHA annual survey and the AHA-HIT survey, supplemented by the CMS CMI, and the US Census Bureau's small area income and poverty estimates, we examine how the adoption of HIT and employment of physicians affect hospital efficiency, and whether they are substitutes or complementary. For example, if the adoption of HIT and employment of physicians are substitutes, using them together will not help increase hospital efficiency. On the other hand, if the adoption of HIT and employment of physicians are complementary, using them together will add to the overall hospital efficiency. To the best of our knowledge, we are the first to argue that these 2 are substitutes, not complements. This finding may seem inconsistent with studies positing that these 2 are complements; yet those studies are different in that they examine the relationship between the HIT adoption and employment of physicians,

 not how these 2 interact to impact hospital efficiency.

Our findings have important policy implications. Although HIT utilization and hospital-physician integration increase hospital efficiency when considered together, they may, in fact be substitutes, suggest that the government should reconsider its efforts toward both. It is possible the use of HIT and employment of physicians simultaneously imposes excessive and unnecessary costs to hospitals because of their overlapping functions to increase control and monitoring of physician behavior. Given that the ultimate goals of policies such as the ARRA and ACA are to reduce costs and improve outcomes, if hospitals achieve less efficiency by adopting HIT and employing physicians simultaneously, the government may want to consider focusing on one policy, rather than both.

Hospital administrators may also use the results of this study to enhance hospital efficiency. Although previous studies have found that hospital employment of physicians may increase the probability of adopting (or utilizing) HIT,

 administrators may want to be careful in using hospital employment of physicians as a way to increase HIT adoption. As our study suggests, HIT adoption and hospital-physician integration, when working together, can hurt hospital efficiency. This is an example of when too much of a good thing may be bad. Administrators should be aware of other ways of enhancing adoption the and utilization of HIT rather than employment of physicians—for example, providing a financial incentive or penalty.

This study has some limitations. First, this study may only capture a snapshot of the effect at a particular point in time. Future research based on multi-year data would enrich our understanding. Also, the fact that we restricted the definition of hospital-physician integration to hospital employment of physicians can limit our understanding. Although employment of physicians can be understood as the most common form of integration between hospitals and physicians, other forms of hospital-physician arrangements, such as group practices without walls, equity models, and foundation models, could be considered tight physician-hospital integration.

These arrangements should be further studied in the context we have discussed.

More broadly, although our study suggests that HIT adoption and hospital-physician integration are substitutes, the underlying mechanisms that seem to prevent complementarity need to be examined. Case studies that examine the mechanisms behind our results can increase our understanding.

The findings of this study indicate that the government's efforts to encourage both HIT adoption and integrated systems warrant consideration.

 College of Business, Clarkson University (N-EC, BA), Potsdam, NY; McWhorter School of Pharmacy, Samford University (JC), Birmingham, AL.

The authors report no relationships or financial interests in any entity that would be a conflict of interest with the subject of this paper.

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Determining the impact of health information technology adoption and hospital-physician integration on hospital efficiency.

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

 To perform a systematic review of the current literature to assess the association between integrated healthcare delivery systems and changes in cost and quality.

: Medline, Embase, Cochrane Reviews, Academic Search Premier, and reference lists were used to retrieve peer-reviewed articles reporting outcomes (cost and quality) related to integrated delivery systems. A general Internet search and reference lists were used to retrieve non—peer reviewed publications meeting the same criteria. Included peer and non–peer reviewed publications were based in the United States and were published between the years 2000 and 2011.

 A total of 21 peer-reviewed articles and 4 non—peer reviewed manuscripts met the inclusion criteria. Twenty studies showed an association between increased integration in healthcare delivery and an increase in the quality of care. One study reported no changes in quality indicators associated with increased integration. None of these studies measured cost reduction directly, but used reduction in utilization of services instead. Four studies associated decreases in the utilization of services with increases in integration.

The vast majority of studies we reviewed have shown that integrated delivery systems have positive effects on quality of care. Few studies linked use of an integrated delivery system to lower health service utilization. Only 1 study reported some small cost savings.

Recent discussion of health reform has placed much emphasis on integrating healthcare delivery systems. Do integrated healthcare systems truly do a better job of providing highquality care while holding down costs? Very few studies have provided empirical evidence.

		We conducted a systematic literature review (2000-present) including peer-reviewed and non—peer reviewed studies, and analyzed 25 eligible studies that examined integrated delivery systems on the topics of cost and quality.

		The majority of these studies reported positive correlation between health system integration and quality of care.

		The evidence on reducing cost of care, however, is rather weak.

The US healthcare system has long been characterized as complex, fragmented, costly, and with significant variation in quality of care. During the health reform debate, many health policy experts have called for the country to reorganize healthcare providers and delivery systems through organizational or virtual integration. The concept of integrated healthcare delivery systems (IDSs) has gained considerable interest. Such systems have been viewed as a better approach to addressing the issues of quality and cost. Indeed, some IDSs are frequently portrayed not just in academic and medical circles but in the popular media as examples of the best, most effective healthcare in this country. However, the evidence used to support such assertions may not have been systematically evaluated. Now that healthcare reform has been enacted and accountable care organizations (ACOs), which have many organizational features similar to those of IDSs, are rapidly being developed and implemented, what do we know about how this approach to organizing healthcare impacts the key factors of quality and cost of care? Do IDSs truly do a better job of providing high-quality care while holding down costs? A comprehensive review of the existing literature that assesses the relationship between IDSs and cost/quality is critically needed.

Much of the recent innovation in US health policy has been based upon a fundamental belief that a higher level of integration will yield a more efficient healthcare delivery system. An IDS presumably provides higher quality and more patient-centric care at lower costs. However, there is no clear definition of what constitutes an IDS. Recently reorganized healthcare delivery systems (eg, ACOs) come in all sizes and shapes, and span a wide spectrum of types and levels of integration. Inconsistency in concept and vague definitions can pose significant challenges as policy makers strive to design policy tools to steer the current healthcare away from uncoordinated and fragmented health systems. Integrated healthcare is complex and has been categorized conceptually in 2 ways: (1) an organized structure that is managed by a financial entity (eg, a financial group that manages different facilities within a healthcare system) or (2) an organized healthcare delivery system that coordinates care and has synchronized functioning.

 An IDS may encompass providers belonging to multiple facilities who are responsible for providing quality patient care rather than providers from a single facility like a hospital or general practice.

Integrated healthcare has numerous definitions.

The World Health Organization defines integrated delivery as “the organization and management of health services so that people get the care they need, when they need it, in ways that are user-friendly, achieve the desired results and provide value for money.”

(p1) While this definition is focused on the patient experience of integrated delivery, other definitions focus on the responsibility of the system. One such definition states that organized delivery systems consist of “a network of organizations that provides or arranges to provide a coordinated continuum of services to a defined population and is willing to be held clinically and fiscally accountable for the outcomes and health status of the population served.”

(p7) Finally, other definitions focus on the specific services and features that describe an IDS as “one or more hospitals along with physicians, diagnostic centers, and other components of the supply side of the supply chain strive to share information, minimize duplication, and make treatment decisions based upon the institutional best practices.”

The preceding concepts and definitions illustrate the complexity and variation involved when discussing IDSs. However, they also help to determine the commonalities present. All of these concepts and definitions include increased communication and information sharing across the care continuum. There is also a common understanding that integrated care should coordinate patient care in a way that improves the patient experience and the quality of the care received.

Jonas and Kovner’s Health Care Delivery

 in the United States includes the various forms of IDSs under the umbrella of organized healthcare delivery. This book defines organized healthcare delivery as a situation where “Care providers have established relationships and mechanisms for communicating and working to coordinate patient care across health conditions, services, and care settings over time.”

(p206) This definition is broad enough to capture many different system structures, while still including the essential functions that an IDS must possess. Hence, for this systematic review, we include broader definitions of IDS that focus on care coordination and system integration.

Literature Search Strategy and Inclusion Criteria

This systematic literature review provides a comprehensive look at the current peer-reviewed literature on IDSs. It also includes a review of other non—peer reviewed publications on the subject, including white papers and reports from nonprofit and industry organizations. Although these documents have not been independently evaluated through peer-review processes, we believe that these sources can provide valuable insights. Due to the fact that these documents are not required to go through a lengthy review process required for publication in a peer-reviewed journal, they are often the first to report on emerging topics and issues.

For peer-reviewed articles, a systematic literature search was performed in 2012 using Medline, Embase, Cochrane Reviews, and Academic Search Premier, as well as the follow-up references in retrieved articles for outcomes (cost and quality) of IDSs from 2000 to 2011. Search terms included 

integrated delivery system, integrated care, integrated service network, continuity of care, chains of care, care coordination, coordinated care, provider system integration, and vertical integration

While the literature search for non—peer reviewed articles was not as systematic as that for the peer-reviewed literature, it was conducted through a similar process. A general Internet search engine was used to find white papers, case studies, and reports from various organizations. Several peer-reviewed publications cited such documents as well, leading to several more studies for consideration. The content of these documents was then assessed to determine the applicability of each paper to this review.

Articles were included if they evaluated outcomes (cost and quality) of IDSs. We excluded theory-based articles, articles not specific to the US health system, and articles published before 2000.

Numerous publications on IDSs were available. However, the majority of this material focused on the key elements required to successfully adapt current health systems to IDSs.

 In this review we were interested in articles and papers pertaining to the cost of care associated with IDSs and the quality of care they provide. Previous literature reviews have addressed the issue of how to define and measure integrated healthcare delivery

 but did not link integration with outcomes.

After the initial literature search, we identified 168 peerreviewed articles. We excluded 78 based on title and abstract review. A total of 90 full-text articles were assessed for eligibility, 69 of which were not eligible, for a final sample of 21 articles. All 21 articles evaluated some measures of quality of care and correlated these measures with health system integration; 6 of these articles also assessed the impacts on cost of care.

The initial search of the non—peer reviewed literature generated 27 white papers, reports, and case studies for consideration. Of these studies, 13 were excluded based on abstract review. The remaining 14 studies were read in their entirety to assess their eligibility for this review. Those that did not pertain to information on cost or quality outcomes were excluded. This resulted in a final sample of 4 non–peer reviewed publications (

To synthesize our literature review findings, we abstracted study settings and key findings from all selected papers into 3 domains: health system focus, outcome measures, and conclusion.

 Enhancing care continuity and coordination are 2 important components of IDSs. With more stringent guideline- or protocol-based care, integrated healthcare systems seemed to provide better quality of care in our systematic review. Out of 21 studies, 19 showed improvement in quality of care with respect to clinical effectiveness,

 Compared with non-IDSs, several studies have found more favorable outcomes for patients treated in IDSs for chronic diseases such as diabetes, hypertension, depression, congestive heart failure, and asthma.

 There is evidence of some improvement in the care delivery process as a result of IDSs, which when quantified ranged from 10 to 25 percentage points.

 For example, 4 studies found that IDSs improved patient medication adherence and decreased medication error rates.

Focusing on outpatient primary care, 1 study evaluated the association between organizational structure of physician groups and healthcare quality to determine whether IDSs provided higher quality care.

 These researchers found that integrated medical groups performed better on 4 Healthcare Effectiveness Data and Information Set (HEDIS) measures related to preventive health screenings; although on 2 measures related to management of chronic disease, there was no statistically significant improvement.

One study found that there was a correlation between integration and the presence of practice systems for chronic care management.

 Functional integration was much more significant than merely structural or financial integration as a determinant of chronic care systems. Similarly, another study found that large physician groups and those owned by a hospital or health plan were more likely than smaller groups to use evidence-based care management processes.

Four studies looked at the impact of electronic medical record/health information technology in IDSs and found that introducing health information technology creates operational efficiencies and patient-centered care

Few studies examined the impact of IDSs on cost of care. Only 5 peer-reviewed papers in our systematic review looked at healthcare cost.

 Of those, most studies provided evidence about the differences in utilization of services for patients treated in IDSs versus non-IDSs. In other words, the level of service utilization per patient was used as a proxy measure for cost of care. Using this measure, 4 out of 5 papers reported that IDSs were associated with lower cost of care. Two studies found that clinical service integration in various forms of ACOs and large multispecialty group practices would lower cost of care.

 One study examined the medication treatment management program within an IDS and concluded that the program generated cost savings related to medication use.

One study discussed the cost related to the level of service integration within the hospital for patients with heart failure, but found no significant relationship between integration of services and economic outcomes.

The studies selected for this review consist of 2 monographs

 Of the 4 selected non—peer reviewed publications, 3 reported that improvements in quality of care were associated with health systems’ clinical service integration. All 3 reports showed improvement in HEDIS measures.

 Among the 17 different health systems these reports examined, IDSs were associated with a lower hospital admission rate per patient, a shorter length of stay per hospital admission, and an overall lower rate of adverse health outcomes. With IDSs, there also were increases in the use of evidencebased practices by physicians in various fields.

 Many IDSs also saw improvements in preventive care (eg, increased vaccination rates).

 Although these studies appear to indicate a positive correlation between integration and quality of care, 1 report found that expected quality improvements were not present. They also found that patients participating in multiyear wellness and prevention programs in 1 IDS showed little to no health improvement38 (

Of the 4 non—peer reviewed publications selected for this systematic review, 3 examined cost of care. All 3 publications reported that cost savings were seen in health systems that had various features of clinical integration. Of these 3 reports, 2 found that there were substantial improvements in quality-of-care measures that the researchers assumed led to indirect cost savings for the system. These reports also noted that substantial cost savings were associated with electronic prescribing implementation due to a tandem generic medication prescription initiative within the IDS involved.

 While these reports indicate positive results, 1 report using data from several IDSs across the country found that, among the systems observed, there was very little cost difference, and that when cost differences were observed they were much smaller than what was expected.

A variety of emerging concepts describe IDSs. Most of the studies are theory based, yet the tenets of the theories have not been tested empirically to evaluate the relationship between the level of integration and either cost or quality outcomes. Earlier work by Shortell and colleagues

suggests that it is not size or structure per se, but rather an organizational commitment to, and culture of, continuous quality improvement that is most closely linked to better performance as measured by clinical quality, patient satisfaction, organizational learning, and financial performance. However, relatively few medical groups were high performers in all performance categories. In many cases, a medical group’s performance varied considerably within the domain of clinical quality; for example, a medical group that performed well in chronic disease management might be average or below in health promotion. Furthermore, the same study by Shortell and colleagues showed no correlation between high performance on quality measures and financial performance, calling into question the assumption that IDSs are inherently better at both improving quality and controlling costs. The researchers did find some positive correlation between high performance on quality measures and certain indicators of integration (eg, larger practice size, affiliation of the medical group with a hospital, health system, or health plan), yet affiliated practices were actually less likely to be high performers on financial measures.

The result of our systematic review provides partial evidence outcomes. The evidence on cost of care, however, is rather weak. No study in our review was able to directly measure the cost savings as a result of integration of a health system. Most studies reported a decrease in healthcare utilization as a result of health system integration but did not assess whether the decrease of service utilization was appropriate. While many of the IDSs involved in these studies had improvements in quality of care with lower service utilization, it is important to note these studies’ limitations. First, many of these reports did not include any indication that statistical tests were performed on the data. While the general data trends may have been positive in the areas of cost and quality, significance cannot be assumed. Also, most of the IDSs included in these studies were implementing multiple programs within and outside of their clinical integration initiatives. It is important to note that healthcare systems may operate under different mixes of reimbursement models (eg, managed care contracts). It was not possible for us to take this factor into consideration, as most of the studies included did not report on reimbursement models.

It is also difficult to distinguish the effect of integration per se from the effects of characteristics (eg, larger patient volume, greater use of electronic medical records, systematic quality improvement initiatives) that tend to be associated more frequently with integrated systems. Therefore, larger sample sizes that include various types of IDSs and more consistent outcome measures are needed to gauge the effects of IDSs on the cost and quality of care.

ACOs and their features involving integrated delivery of healthcare are currently at the forefront of healthcare policy. The hope for the ACO is that it will reduce healthcare costs and improve the quality of care. However, there are no data to support these assumptions, as these systems have only recently been created. Integrated healthcare delivery systems share with ACOs many of the same organizational features, foremost among them being high levels of organizational and clinical service integration. For this reason, the performance of existing IDSs with respect to quality and cost of care are being assessed to predict how well ACOs will perform in the future US healthcare system.

The vast majority of studies we reviewed showed that IDSs have positive effects on quality of care. A few studies we reviewed also linked IDSs to lower health service utilization. However, each study had a different way of describing and defining IDSs. In order to compare IDSs with respect to their effectiveness in controlling costs and improving quality of care, consistent definitions should be used and components of integration need to be well defined. Due to limited information provided in the literature, we were unable to identify specific features in each health system. Nor were we able to meaningfully group the health systems based on their commonalities into various types of IDSs. As a result, it was difficult to draw definitive conclusions. Moreover, to accurately capture the performance of a health system, established methods that consistently measure cost and quality of care across health systems are needed.

Our study has other limitations. The study lacks generalizability due to the heterogeneous nature of the IDS in the studies we reviewed, the unavailability of effect size calculations, and the absence of systematic working definitions for outcomes. There also may be publication bias since there is a higher probability of peer-reviewed journals publishing studies with favorable results rather than studies with inconclusive or unexpected results.

As integrated systems become more common, it also is becoming more apparent that despite certain commonalities, these systems vary tremendously in how they operate. The emergence of a growing number of IDSs should allow for more data-driven studies comparing organizational performance on quality, safety, access, and cost metrics, thereby adding more insight into exactly what characteristics of IDSs lead to improvements in each of these areas. The question, perhaps, is not so much whether IDSs are better than other models of delivery, but rather, how to identify specific system features that most effectively improve quality of care while controlling costs.

 From Department of Public Health Sciences (WH, JC, ML), Department of Medicine and Public Health Sciences (HP), Division of Health Services Research, Penn State University College of Medicine, Hershey, PA.

Integrated health systems are often portrayed as the most effective in providing high-quality care at lower costs. This systematic review assesses the empirical evidence.

Jongwha Chang, PhD

Articles

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

Effects of Integrated Delivery System on Cost and Quality