The study of hospital financial performance has become an important part of organizational performance research. Considering the current attention on the financial sustainability of U.S. hospitals, understanding the major factors that influence hospital financial performance is imperative for researchers, policy makers, and administrators. Despite a slight dip after the 2008 financial crisis, U.S. hospitals continue their capital investments in plants, facilities, and cutting edge technologies (Zengul & O'Connor, 2013). About 50% of hospital capital investment is attributed to clinical and information technologies (Callahan, 2009). These capital investments are expected to continue considering the legislative, demographic, and environmental forces that are taking place in the United States. For example, retirement of the baby boomers and approximately 31 million newly insured individuals as a result of the Patient Protection and Affordable Care Act of 2010 will require not only investment in additional hospital space but also additional equipment and services. Among these investments, high-technology medical services have a prominent place given their high upfront costs.
High-technology medical services (e.g., positron emission tomography, organ transplant services) are defined as those machines, equipment, or services that are designed to improve certain human health conditions through their technical (i.e., diagnostic or interventional) features (Spetz & Maiuro, 2004; Zengul, Weech-Maldonado, & Savage, 2014). Recouping the initial cost within a certain time frame is crucial for hospital financial performance. Moreover, if investments in high-technology medical services are not accompanied with enough patient volume, the initial investment cost and excess capacity might negatively affect financial performance.
However, little information is known about the implications of high-tech medical services on hospital financial performance. Prior research has tended to focus on the availability (e.g., Baker & Phibbs, 2002; Bryce & Cline, 1998; Cutler & McClellan, 1996; Grossman & Banks, 1998), antecedents of diffusion (e.g., Teplensky, Pauly, Kimberly, Hillman, & Schwartz, 1995; Zhang, Kohn, McGarrah, & Anderson, 1999), or cost implications (e.g., Chernew, Fendrick, & Hirth, 1997) of high-tech medical services rather than the influence of these services on hospital financial performance (Zengul et al., 2014). Moreover, there are only a few studies that have specifically focused on clinical technology and explored the effects of high-tech services on hospital financial performance (Irwin, Hoffman, & Lamont, 1998; Li & Collier, 2000; Trinh, Begun, & Luke, 2008). Most of these studies have methodological limitations, which constrain their generalizability, such as cross-sectional designs, inconsistent measures of high-tech services, and limited geographic scope. Therefore, it remains unclear whether there is a relationship between high-tech medical services and hospital financial performance.
The aim of this article is to evaluate the implications of availability of high-tech medical services on financial performance of hospitals by using the resource-based view (RBV) of the firm. RBV attributes better performance of organizations to the unique amalgam of their resources (Barney, 1991). RBV is rarely used in hospital financial performance studies (Irwin et al., 1998; Short, Palmer, & Ketchen, 2002) despite being one of the most frequently used strategic management frameworks for organizational performance.
Overall, this study contributes to the literature in four major ways by (a) addressing the aforementioned need for research on the “technology–financial performance” link, (b) addressing the limitations of previous studies through its longitudinal design and national sample, (c) including four different measures of financial performance, and (d) including the Saidin high-technology index that takes into account the breadth and rareness of offered high-tech services.
Conceptual Framework and Hypotheses
The emergence of RBV can be attributed to the long-run scholarly endeavors (e.g., Dierickx & Cool, 1989; Pfeffer, 1994; Porter, 1985; Rumelt & Wensley, 1981; Wernerfelt, 1984) of understanding the underlying reasons for the outperformance of some organizations relative to their peers. RBV posits that certain organizations exhibit superior performance and achieve sustained competitive advantage through organizational resources with distinctive features. Barney (1991) proposed three dimensions for a firm’s capital resources: physical, human, and organizational. The focus of this study, high-tech services, falls under the physical resources category. However, the labor and operationally intensive nature of health care delivery makes it imperative to recognize the human and organizational dimensions as well. Moreover, a physical resource by itself may not satisfy the inimitability and nonsubstitutability criteria of RBV. Therefore, this article will use the physical dimensions of hospital resources (i.e., high-tech medical services) and account for other organizational and human capital resources.
Investment in physical capital resources is a necessary business practice for hospitals to enhance survival and to protect their competitive edge. Hospitals adopt high-tech medical services with an expectation of an improvement in their financial bottom line—by revenues from high-tech services exceeding their costs and/or lower operational costs in the long term. Especially because of the initial large capital investment cost, investing in certain technologies may increase costs in the short term. However, the benefits (increased revenues and/or lowered costs) may outweigh the costs in the long term.
There are potential direct financial benefits of high-tech medical services through lower costs and/or increased revenues. New technologies may lower costs by increasing efficiency of existing services or processes. Furthermore, considering that both government and private payers tend to reimburse high-tech services with relatively higher rates (Coye & Kell, 2006), hospitals may adopt new technologies with the goal of adding a new service line or substituting an existing one with an expectation of higher reimbursements. However, hospitals may not optimize potential benefits of these relatively higher reimbursement rates unless they experience higher patient volume.
There may be also indirect financial benefits of high-tech medical services through increased inpatient volume, which can result in higher revenues for other services and economies of scale. To increase patient volume, hospitals can use marketing to promote the adoption of new and cutting-edge technologies. Furthermore, hospitals may improve their competitiveness, image, and prestige by providing high-tech medical services (Teplensky et al., 1995). As such, hospitals may use high-tech services to attract both patients and physicians (Irwin et al., 1998). Attracting physicians, especially more qualified ones, helps hospitals to attract even more patients (Coye & Kell, 2006). In addition, increasing patient volume may result in economies of scale (Morrisey, 2001). Higher patient volume would reduce hospital costs by reducing per-patient fixed cost allocation. Therefore, considering the aforementioned financial benefits of high-tech services, offering a larger breadth of these services is expected to yield better financial performance.
Hypothesis 1. An increase in the number of high-tech medical services offered (breadth) will result in better hospital financial performance.
Historically, heterogeneity of profit margins in a diagnosis-related group (DRG)-based reimbursement system has not only favored the emergence of single specialty hospitals but also increased the adoption of more lucrative services by general hospitals (Carey, Burgess, & Young, 2009). Some of the inequalities in DRG-based reimbursement were intentionally done by the Centers for Medicare & Medicaid Services (CMS) to accelerate the adoption of certain high-tech services (Straube, 2005). However, adoption of these more lucrative services by various hospitals in the same market area has created service duplications (Trinh et al., 2008). As such, organizations may not be able to reap all potential benefits from their valuable resources if their competitors have access to the same resources. As RBV suggests, having rare resources would provide an organization with competitive advantage over its rivals (Barney, 1991). Therefore, not only the number of resources (breadth) but also the rareness of these resources would be an important factor for financial performance. Overall, hospitals offering a larger number of rare high-tech services are expected to exhibit better financial performance.
Hypothesis 2. An increase in the number of rare high-tech medical services offered will result in better hospital financial performance.
Data and Sample
This study used longitudinal panel data covering the period of 2005–2010 from the following sources: American Hospital Association (AHA) annual survey, CMS Medicare Cost Reports (MCRs), CMS Case Mix Index (CMI), and Area Health Resource File (AHRF). AHA annual survey is a comprehensive data source that includes more than 1,000 data fields of organizational characteristics for approximately 6,400 U.S. hospitals. MCR is a comprehensive data source that includes provider, utilization, and financial information for Medicare-certified institutional providers. CMI file includes hospital’s CMIs for discharges based on the average DRG relative weights for hospitals. AHRF is an extensive county-based annual database that includes various demographic, labor-related, social, and economic indicators.
The study focused on general medical-surgical hospitals in the United States for two reasons: (a) specialty hospitals tend to have relatively narrow technological focus and (b) focusing on medical surgical hospitals allows better interpretation of results with regard to high-tech services. Furthermore, the study was limited to private hospitals, because government hospitals tend to have a different financial focus than profit and not-for-profit hospitals.
After merging data from the AHA annual survey and the MCR data, 1,357 hospital-year observations (average of 266 hospitals per year) were excluded because of missing hospital information in MCR data. In addition, hospitals that reported 6 months or less of MCR data for a given year were dropped, and the remaining data with coverage periods other than 12 months were annualized. This resulted in 27,786 hospital-year observations (average 4,631 hospitals per year). Further merging of the data with Area Resource File resulted in the deletion of an additional 279 hospitals (average of 46 hospitals per year) because of missing market control variables and 1,335 hospital-year observations (average of 222 hospitals per year) because of incomplete financial performance information. In addition, 6,321 hospital-year observations (average of 1,053 hospitals per year) corresponding to government (federal and nonfederal) hospitals were excluded as previously noted. Finally, dependent variable observations in excess of three standard deviations from the mean were flagged as potential outliers. After testing for normality and manually checking observations for potential data entry errors, an additional 240 hospital-year observation (average 40 hospitals per year) were identified as outliers and dropped from the sample. This resulted in a final analytic sample of 19,611 hospital-year observations (an average of 3,268 hospitals per year).
Dependent, independent, and control variables of this study and their operational definitions are shown in Table 1. The financial performance (dependent variable) variables consist of two commonly used hospital profitability measures: operating margin and total margin. Two complementary dependent variables for operating expenses and operating revenue per inpatient day were also included as dependent variables. Operating margin takes into account the operating income (net patient revenue − total operating expenses) and excludes nonoperating sources of income or expenses such as government appropriations, philanthropy, investment income from endowments, grants, investments, gift shops, interest income/expense, and all other non-patient-related expenses or revenues. Total margin takes into account both operating and nonoperating sources of income and expenses. Not-for-profit and government hospitals tend to receive income from non-patient-related sources (i.e., gifts, endowments, grants, government transfers) more regularly than for-profit hospitals. We use both operating margin and total margin as indicators of financial performance, because it is possible that high-tech services may attract nonoperating revenues such as philanthropic contributions because of their relatively prestigious status.
Given the nonnormal distribution of operating expenses and operating revenues, we used the natural log to normalize the values of these variables. In the regression interpretation, one unit of change in a nontransformed independent variable indicates the percentage change in these log-transformed variables when these coefficients are multiplied by 100.
Two different measures of high-tech services are developed to test our hypotheses. The first measure, high-tech breadth index, is an index that is based on a simple count of available high-tech medical services (breadth of high-tech services) (Hypothesis 1). This index is based on the AHA’s Annual Survey data. The list of high-tech services included in the index is provided in Supplemental Digital Content 1 (see Supplemental Digital Content 1, http://links.lww.com/HCMR/A16). This list was originally developed by Spetz and Baker (1999); however, the list was enhanced by including other services identified through a comprehensive literature review of hospital’s technological sophistication or new high-tech services that have been added to the AHA survey since 1999 (Bazzoli & Andes, 1995; Bazzoli, Chen, Zhao, & Lindrooth, 2008; Irwin et al., 1998; Jha, Orav, Dobson, Book, & Epstein, 2009; Trinh et al., 2008; Trinh, Begun, & Luke, 2010). As a result, the high-tech breadth index includes an average of 61 services for each year, starting from 54 in 2005 to 68 in 2010 with the new addition of services.
The second measure, the Saidin index, is a high-technology index that takes into account the breadth and rareness of offered high-tech services (Hypothesis 2); it represents the weighted sum of technologies and services offered in hospitals (Spetz & Baker, 1999). The weights are calculated by finding the proportion of hospitals in the United States that do not own the technology or service (Spetz & Baker, 1999). Appendix A provides the list of the Saidin index weights for each hospital high-tech service from 2005 to 2010. For example, a weight of 0.85 indicates that only 15% of hospitals own that particular service in that particular year. The Saidin index is the sum of the high-tech services weighted by the relative rareness of the particular service. In this study, we used a modified Saidin index, which represents the weighted sum of 35 services that had 85% or larger weights in 2010. Using an 85% weight threshold is more appropriate than choosing a larger threshold such as 90%. With a 90% threshold, 73% of hospitals would have had zero index value. With the 85% weight threshold, only 44% of hospitals had index value of zero. Therefore, an 85% threshold provided a more balanced approach with regard to the Saidin index.
Among the control variables (Table 1), organizational/operational factors include hospital characteristics that have been associated with financial performance (McCue, 2011; McCue, Mark, & Harless, 2003). Operational characteristics such as occupancy rate, average length of stay, percentage of Medicare/Medicaid discharges, CMI, and outpatient mix are among these factors. The remainder of organizational and environmental control variables and their operational definitions are provided in Table 1. Among these control variables occupancy rate, a commonly used measure indicating the utilization of hospital beds and average length of stay may be important factors for the profitability of hospitals. Moreover, the CMI is an important control variable, because it is related to both patient case severity and reimbursement rates of hospitals. The last organizational control variable, outpatient mix, was developed by following the methodology from prior studies (Detsky, O'Rourke, Naylor, Stacey, & Kitchens, 1990; Vujicic, Addai, & Bosomprah, 2009) with an assumption that outpatient visits utilize one third of hospital resources.
Under staffing factors, registered nurse (RN) staffing (Table 1) variables were included among control variables because of their strong influence on financial performance. Multicollinearity was not considered a concern given that none of the included staffing variables had more than 40% correlation. Among our staffing measures, we included two RN-related measures because of RNs’ capacity to optimize the benefits of high-tech services.
Financial performance of hospitals may be influenced by market/environmental factors such as competition, market affluence, managed care penetration, and market share (Table 1) Market competition is considered a major driver of hospital technology adoption (Devers, Brewster, & Casalino, 2003). Moreover, competition is an important variable because of its influence on hospital costs. Hospitals may use high-tech services as a differentiation strategy in more competitive markets to increase market share and ultimately experience better financial performance. Some studies indicate an association between higher competition and lower hospital cost especially in a managed care and prospective payment environment (Gift, Arnould, & DeBrock, 2002; Keeler, Melnick, & Zwanziger, 1999). However, other studies have found an association between higher competition and higher hospital costs (Devers et al., 2003; Rivers, Glover, & Munchus, 2000; Trinh et al., 2008). We have attempted to address the aforementioned findings on cost–competition relationship by using both Medicare-managed care penetration and Herfindahl–Hirschman Index, a commonly used measure of competition that is calculated by finding the sum of squared market shares of hospitals in a Dartmouth Atlas’ Health Service Area.
Other time-invariant organizational and market factors, such as location (urban/rural), Certificate of Need laws, teaching status, size, ownership status, and health system membership were not included because of the use of a fixed-effects regression (see Analysis section). In addition, we did not include payer mix (percent Medicaid and percent Medicare) as a control variable given its potential endogeneity with financial performance.
In this article, we used a within-group hospital fixed-effect regression model for analysis. We compared random effects and fixed-effects models with the Hausman test. The significant result of the Hausman test for all of the analytical models for the four dependent variables favored the fixed-effects models over random effects models. Therefore, we opted to use fixed-effects models. Moreover, to control for potential endogeneity, we used 2 years of lagged independent variables in our fixed-effects model panel data analyses. Finally, we included year of fixed effects to control for the time trend effects.
A fixed-effects model has an advantage over traditional multivariate regression models with regard to the omitted variable bias, given that the model controls for unmeasured time constant attributes (Allison, 2005). For unmeasured and stable characteristics of hospitals, the fixed-effects model uses each hospital as its own control by using within variation (Allison, 2005). Therefore, to be able to utilize a fixed-effects model, one needs enough within variation for the measures of interest. We had enough within variation for our measures of interest. For example, in our sample of 3,268 hospitals, the three variations for high-tech breadth index were reported as (a) overall = 12.39, (b) between = 11.31, and (c) within = 5.19. In these figures, the between variation indicates the variation across hospitals, whereas the within variation indicates the variation within individual hospitals across time.
In our fixed-effects models, we excluded measures with zero or very low within variation such as location, teaching status, size, and ownership status. This decision is based on the fact that each hospital serves as its own control in the fixed-effects model, and all stable and time invariant factors are already controlled for.
Separate regressions were run for each of the four dependent variables. There were two regression models. Model 1 used breadth of high-tech services as the main independent variable, whereas Model 2 included rareness (Saidin index) of high-tech services as the main predictor. As a sensitivity analysis, we conducted separate regression analyses for not-for-profit and for-profit hospitals. FileMaker Pro 12 was used for data management, whereas SAS 9.3 and STATA 13 were used for data analysis.
Table 2 presents the descriptive statistics for the variables that were included in the fixed-effects model. Table 3 displays the fixed-effects regression coefficients for the relationship between high-tech services and four financial performance measures (Hypotheses 1 and 2) including operating margin, natural log of operating expenses per inpatient day, natural log of operating revenues per inpatient day, and total margin. Coefficients for high-tech breadth index are presented under the breadth column. Coefficients for high-tech Saidin index are presented under the rareness column.
Hypothesis 1 (breadth) predicted that an increase in the number of high-tech services would result in better financial performance of a hospital. Hypothesis 1 is supported for two of the four financial performance measures. In Table 3, for ease of interpretation, coefficients for total margin and operating margin were reported in percentages. For every 10-unit increase in high-tech services, there is a 3% increase in total margin (p < .001). Because the operating expenses per inpatient day and operating revenue per inpatient day were log-transformed, coefficients are multiplied by 100 to obtain the percentage change for these variables. Therefore, for every 10-unit increase in high-tech services, there is a 10% decrease in operating expenses per inpatient day (p < .05) and a marginally significant 4% decrease in operating revenue per inpatient day (p < .10). Although revenue decreased with high-tech services, the reduction in expenses is greater, resulting in a positive operating margin; however, it is not statistically significant. Same analyses stratified by ownership type show that these significant relationships only held for not-for-profit hospitals. Breath of high-tech services did not significantly affect any of the financial performance measures of for-profit hospitals.
Hypothesis 2 predicted that an increase in the number of rare high-tech services (Saidin index) would result in better hospital financial performance. Hypothesis 2 is supported for two of the four financial performance measures. For every 10-unit increase in rare high-tech services, there is a 13% significant increase in total margin (p < .001). For every 10-unit increase in the number of rare high-tech services, there is a 30% decrease in operating expenses per inpatient day (p < .01). Contrary to the hypothesized relationship, an increase in the number of high-tech services resulted in lower operating revenue per inpatient day too. For every 10-unit increase in the Saidin index (rareness), there is a 20% decrease in operating revenue per inpatient day (p < .01). Moreover, there was no statistically significant association between rare high-tech services and operating margin. Therefore, although an increase in use of rare high-tech services results in lower expenses per inpatient day, the lower expenses are offset by lower revenues per inpatient day, resulting in a nonsignificant change in operating margin. Same analyses stratified by ownership type show that these significant relationships only held for not-for-profit hospitals. The rareness of high-tech services did not significantly affect any of the financial performance measures of for-profit hospitals.
There are some other noteworthy statistically significant results for other measures in the models that were applicable to the full sample of hospitals. An increase in CMI resulted in higher total margin. Occupancy rate and length of stay were not related to operating margin or total margin. Although higher occupancy rate and length of stay resulted in lower operating expenses, these were offset by lower revenues per inpatient day. Among staffing control variables, physician and RN staffing intensity were not related to operating margin or total margin. Higher physician and RN staffing intensity resulted in higher operating revenues, but these were offset by higher operating expenses per inpatient day.
This study examined the association between high-tech services and several hospital financial performance measures by using RBV of the firm as a theoretical framework. To date, the relationship between high-tech services and hospital financial performance has not been explored extensively. Furthermore, the methodological limitations of the extant literature confine the generalizability of the study findings. Therefore, we attempted to contribute to the knowledge base about the possible link between high-tech services and financial performance. In line with RBV, we hypothesized that the breadth and the rareness of high-tech services would be positively associated with financial performance of hospitals. To test our hypothesis, we used within-group fixed-effects models with 2 years lagged independent variables on a longitudinal panel data of approximately 3,268 hospitals from 2005 through 2010.
Study results partially support Hypotheses 1 and 2: An increase in the number and the rareness of high-tech medical services offered results in better hospital financial performance in terms of higher total margin and lower operating expenses per inpatient day. This supports RBV’s notion that investments in physical capital resources can lead to better financial performance. However, contrary to expectations, we found that high-tech services resulted in lower operating revenues per inpatient day and there were no significant results in terms of operating margin Finally, stratified analysis based on ownership status showed that these relationships only held among not-for-profit hospitals. High-tech services were not associated with any of the financial performance measures among for-profit hospitals.
There are several limitations to this study. First, the study was limited to the use of secondary data. However, secondary data sources of this study such as AHA Annual Survey, Area Resource File, and MCRs are widely used by health services researchers. Second, there is potential endogeneity between adoption of high-tech services and financial performance. However, to address potential endogeneity, we used a fixed-effects model and 2 years of lagged independent variables for high-tech services. Third, this study focused on the effect of high-tech services based on a 2-year lag structure. However, some of the financial benefits of technology adoption may accrue in the long term. Future studies should explore whether high-tech services have a similar effect on financial performance based on lag structures of more than 2 years. Fourth, rareness of high-tech services is based on a national sample of hospitals. Given that hospitals compete on a local level, future studies should examine rareness based on the market in which the hospital competes, such as the health service area. Finally, this study focused on financial profitability, and there may be differences between for-profit and not-for-profit in their profit motivations. However, profit considerations may be a major driver in the behavior of both for-profit and not-for-profit hospitals. For example, a recent study showed seven not-for-profit hospitals among the top 10 most profitable hospitals (Bai & Anderson, 2016). Nevertheless, we evaluated the differential effect of high-tech services on financial performance based on ownership status by conducting a stratified analysis of for-profit and not-for-profit hospitals. Future studies should also explore the impact of high-tech services on nonfinancial metrics, such as inpatient/outpatient utilization, payer mix, market share, and quality of care.
There are several important managerial implications of this study. First, study findings suggest that enhancing both the breadth and rareness of high-tech services may be a legitimate organizational strategy to improve financial performance. Given that both the breadth and rareness of high-tech services has a significant impact on total margin but not on operating margin suggests that the benefits accruing from high-tech services may be a result of increased nonoperating revenues, such as increased charitable contributions. Our further stratified analyses based on ownership status shows that this relationship holds only for not-for-profit hospitals. This confirms the potential role of high-tech services in generating nonoperating revenue; however, future studies are needed to identify the exact type of nonoperating revenue that may be driving the relationship between high-tech services and total margin.
Second, increasing the breadth and rareness of high-tech services can result in lower operating expenses per inpatient day. Hospitals may benefit from a cost advantage as they adopt newer technologies that result in greater efficiency in the use of resources. However, the findings also indicate that revenue per inpatient day also declines (i.e., revenue disadvantage) with an increase in the breadth and rareness of high-tech services. There are two potential explanations for this counterintuitive finding. First, high-tech services may result in less invasive care and shorter length of stay. This may in turn lead to lower reimbursement especially among payers with per-diem reimbursement. Second, government payers (like Medicare) may be more likely to embrace newer technologies (Coye & Kell, 2006). As such, hospitals with more high-tech services may get more Medicare referrals and, as a result, lower revenues (since Medicare reimbursement rates are lower compared to private insurance).
Third, there are additional managerial implications from the findings based on several control variables. Among both for-profit and not-for-profit hospitals, an increase in case mix results in better financial performance, in terms of total margin. This suggests that hospitals that provide services with higher DRGs, such as cardiac surgery, can benefit financially. This may be a result of the higher reimbursement or prestige associated with providing these services.
Our results also suggest that a strategy aimed at increasing occupancy rate may not be financially lucrative. Lower operating expenses, as a result of economies of scale from higher occupancy rate, are offset by lower operating revenues, perhaps as a result of attracting patients with less attractive reimbursement.
Increased hospital length of stay is believed to be associated with lower financial performance as a result of the prospective system; however, our findings suggest otherwise. Although greater length of stay is associated with lower reimbursement per inpatient day, this is offset by lower expenses per inpatient day. Given that premature inpatient discharges may increase the likelihood of readmissions and given that hospitals are being held accountable for readmissions under value-based purchasing, hospitals should reconsider their strategy for discharging patients. Finally, increased RN staffing does not impact negatively financial performance. Given the potential impact of increased RN staffing on hospital quality of care, hospitals should be cautious in reducing their RN staffing.
In summary, this is the first study that uses a longitudinal design and national data to examine the relationship between use of high-tech services and financial performance. Our study finds that the breadth and rareness of high-tech services improves the total margin of not-for-profit hospitals, but not necessarily for for-profit hospitals. Increased nonoperating revenues, such as charitable contributions, as a result of high-tech services may explain these findings among not-for-profit hospitals. Future studies should examine the departmental or unit level financial performance implications of specific high-tech services and the impact of high-tech services on quality of care.
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Keywords:Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved
financial performance; high-technology medical services; hospital; resource-based view